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attnserver.run_attnserver.slurm.sh.343207.out.log

@@ -19193,3 +19193,182 @@ done with setup ...
 training ...
 Setting rerun_state_machine.current_iteration to 0...
 [before the start of training step] datetime: 2025-06-21 21:57:50 
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
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+batch tensor after cp: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor after cp: position_ids torch.Size([1, 131072])
+Start exporting trace 0
+Done exporting trace 0
+ [2025-06-21 21:58:40] iteration        1/      10 | consumed samples:            1 | elapsed time per iteration (ms): 49759.1 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 4294967296.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+Number of parameters in transformer block in billions:  0.35
+Number of parameters in embedding layers in billions: 0.21
+Total number of parameters in billions: 0.56
+Number of parameters in most loaded shard in billions: 0.0703
+Theoretical memory footprints: weight and optimizer=1206.09 MB
+[Rank 3] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 43860.0 | max reserved: 43860.0
+[Rank 6] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 43860.0 | max reserved: 43860.0
+[Rank 7] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 43860.0 | max reserved: 43860.0
+[Rank 5] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 43860.0 | max reserved: 43860.0
+[Rank 2] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 43988.0 | max reserved: 43988.0
+[Rank 0] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 43860.0 | max reserved: 43860.0[Rank 4] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 43860.0 | max reserved: 43860.0
+
+[Rank 1] (after 1 iterations) memory (MB) | allocated: 23474.22607421875 | max allocated: 41389.73681640625 | reserved: 44660.0 | max reserved: 44660.0
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+batch tensor after cp: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor after cp: position_ids torch.Size([1, 131072])
+Start exporting trace 1
+Done exporting trace 1
+ [2025-06-21 21:59:22] iteration        2/      10 | consumed samples:            2 | elapsed time per iteration (ms): 41689.2 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 2147483648.0 | number of skipped iterations:   1 | number of nan iterations:   0 |

attnserver.run_attnserver.slurm.sh.343213.out.log

@@ -53447,3 +53447,336 @@ batch tensor after cp: labels torch.Size([1, 12288])
 batch tensor after cp: loss_mask torch.Size([1, 12288])
 batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 98304])
 batch tensor after cp: position_ids torch.Size([1, 12288])
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+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 12288])
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+batch tensor after cp: loss_mask torch.Size([1, 12288])
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+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
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+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 98304])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+Start exporting trace 5
+Done exporting trace 5
+ [2025-06-21 21:58:24] iteration        6/      10 | consumed samples:            6 | elapsed time per iteration (ms): 80453.4 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 134217728.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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attnserver.run_attnserver.slurm.sh.343214.out.log

@@ -39716,3 +39716,586 @@ batch tensor after cp: position_ids torch.Size([2, 20480])
 Start exporting trace 5
 Done exporting trace 5
  [2025-06-21 21:58:04] iteration        6/      10 | consumed samples:            6 | elapsed time per iteration (ms): 57588.2 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 134217728.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
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+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+Start exporting trace 6
+Done exporting trace 6
+ [2025-06-21 21:59:02] iteration        7/      10 | consumed samples:            7 | elapsed time per iteration (ms): 58097.7 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 67108864.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
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+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
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+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
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+batch tensor after cp: position_ids torch.Size([2, 20480])
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+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
+batch tensor after cp: tokens torch.Size([2, 20480])
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+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
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+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
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+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
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+batch tensor: position_ids torch.Size([2, 163840])
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+batch tensor after cp: loss_mask torch.Size([2, 20480])
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+batch tensor: tokens torch.Size([2, 163840])
+batch tensor: labels torch.Size([2, 163840])
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+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
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+batch tensor after cp: labels torch.Size([2, 20480])
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+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 163840])
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+batch tensor after cp: tokens torch.Size([2, 20480])
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+batch tensor: labels torch.Size([2, 163840])
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+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
+batch tensor: position_ids torch.Size([2, 163840])
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+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 163840])
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+batch tensor: attention_mask torch.Size([2, 1, 163840, 163840])
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+batch tensor: labels torch.Size([2, 163840])
+batch tensor: loss_mask torch.Size([2, 163840])
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attnserver.run_attnserver.slurm.sh.343220.out.log

@@ -25136,3 +25136,103 @@ WARNING: constraints for invoking optimized fused softmax kernel are not met. We
 time to initialize megatron (seconds): 8.313
 [after megatron is initialized] datetime: 2025-06-21 21:58:11 
 building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 676924416
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (676924416 elements, 676924416 padded size):
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.embedding.position_embeddings.weight
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14990c75e420>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 676924416
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 676924416
+(TP, PP, encoder TP, encoder PP) mismatch after resume ((4, 1, 0, 0) vs (2, 1, 0, 0) from checkpoint): RNG state will be ignored
+(TP, PP, encoder TP, encoder PP) mismatch after resume ((4, 1, 0, 0) vs (2, 1, 0, 0) from checkpoint): Rerun state will be ignored
+ loading distributed checkpoint from gpt-checkpoint at iteration 10

attnserver.run_attnserver.slurm.sh.343225.out.log

@@ -21350,3 +21350,96 @@ batch tensor after cp: labels torch.Size([1, 49152])
 batch tensor after cp: loss_mask torch.Size([1, 49152])
 batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
 batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+Start exporting trace 5
+Done exporting trace 5
+ [2025-06-21 21:58:24] iteration        6/      10 | consumed samples:            6 | elapsed time per iteration (ms): 64617.0 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 134217728.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 98304])
+batch tensor: labels torch.Size([1, 98304])
+batch tensor: loss_mask torch.Size([1, 98304])
+batch tensor: attention_mask torch.Size([1, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([1, 98304])
+batch tensor after cp: tokens torch.Size([1, 49152])
+batch tensor after cp: labels torch.Size([1, 49152])
+batch tensor after cp: loss_mask torch.Size([1, 49152])
+batch tensor after cp: attention_mask torch.Size([1, 1, 49152, 98304])
+batch tensor after cp: position_ids torch.Size([1, 49152])

attnserver.run_attnserver.slurm.sh.343226.out.log

@@ -17266,3 +17266,136 @@ batch tensor after cp: labels torch.Size([2, 65536])
 batch tensor after cp: loss_mask torch.Size([2, 65536])
 batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
 batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+Start exporting trace 2
+Done exporting trace 2
+ [2025-06-21 21:58:49] iteration        3/      10 | consumed samples:            3 | elapsed time per iteration (ms): 53213.3 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 1073741824.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])

attnserver.run_attnserver.slurm.sh.343237.err.log

@@ -2046,3 +2046,75 @@ W0621 21:54:51.707000 1120324 site-packages/torch/distributed/run.py:766] ******
   warnings.warn(
 /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
   warnings.warn(
+[rank1]:[W621 21:58:38.212320465 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank3]:[W621 21:58:39.540096928 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank7]:[W621 21:58:39.600834705 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank11]:[W621 21:58:39.271604056 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank13]:[W621 21:58:39.281585895 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank15]:[W621 21:58:39.341994445 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank10]:[W621 21:58:39.666457382 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank0]:[W621 21:58:39.083150728 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank9]:[W621 21:58:39.774865528 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank5]:[W621 21:58:39.154577396 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank12]:[W621 21:58:39.908045005 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank8]:[W621 21:58:40.928288102 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank6]:[W621 21:58:40.328026038 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank14]:[W621 21:58:40.331172688 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank4]:[W621 21:58:40.992897374 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank2]:[W621 21:58:41.506227065 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+W0621 21:59:05.149000 1120324 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1341] The node 'fs-mbz-gpu-476_1120324_0' has failed to send a keep-alive heartbeat to the rendezvous '343237' due to an error of type RendezvousTimeoutError.
++ set +x
++ set +x
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=65536
++ PROF_CTX_LENGTH=65536
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L65536*tp2.cp8.bs1.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L65536*tp2.cp8.bs1.json' ']'
++ echo 'Running ctx_length=65536, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=1'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343237 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-274:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 65536 --max-position-embeddings 65536 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343237 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-274:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 65536 --max-position-embeddings 65536 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:59:08.538000 849579 site-packages/torch/distributed/run.py:766] 
+W0621 21:59:08.538000 849579 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:59:08.538000 849579 site-packages/torch/distributed/run.py:766] 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. 
+W0621 21:59:08.538000 849579 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:59:08.581000 1124226 site-packages/torch/distributed/run.py:766] 
+W0621 21:59:08.581000 1124226 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:59:08.581000 1124226 site-packages/torch/distributed/run.py:766] 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. 
+W0621 21:59:08.581000 1124226 site-packages/torch/distributed/run.py:766] *****************************************
+[rank2]:[W621 21:59:34.872941463 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank6]:[W621 21:59:34.873518740 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank1]:[W621 21:59:34.873538258 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank4]:[W621 21:59:34.873652088 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank5]:[W621 21:59:34.874032995 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank3]:[W621 21:59:34.874207362 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank7]:[W621 21:59:34.875025848 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank11]:[W621 21:59:34.544452962 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank10]:[W621 21:59:34.544486163 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank12]:[W621 21:59:34.544489592 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank15]:[W621 21:59:34.544505034 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank14]:[W621 21:59:34.544539355 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank9]:[W621 21:59:34.544642645 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank13]:[W621 21:59:34.544805119 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank8]:[W621 21:59:34.671912210 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank0]:[W621 21:59:34.026347727 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.

attnserver.run_attnserver.slurm.sh.343237.out.log

@@ -28995,3 +28995,818 @@ batch tensor after cp: labels torch.Size([1, 6144])
 batch tensor after cp: loss_mask torch.Size([1, 6144])
 batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
 batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+Start exporting trace 10
+Done exporting trace 10
+WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED
+(min, max) time across ranks (ms):
+    evaluate .......................................: (17500.76, 17506.43)
+----------------------------------------------------------------------------------------------------------------
+ validation loss at iteration 10 on validation set | lm loss value: 1.070103E+01 | lm loss PPL: 4.440147E+04 | 
+----------------------------------------------------------------------------------------------------------------
+WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED
+WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED
+Evaluating on 1 samples
+Evaluating iter 1/1
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 6144])
+batch tensor after cp: labels torch.Size([1, 6144])
+batch tensor after cp: loss_mask torch.Size([1, 6144])
+batch tensor after cp: attention_mask torch.Size([1, 1, 6144, 49152])
+batch tensor after cp: position_ids torch.Size([1, 6144])
+Start exporting trace 11
+Done exporting trace 11
+WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED
+WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED
+(min, max) time across ranks (ms):
+    evaluate .......................................: (10344.77, 10347.15)
+----------------------------------------------------------------------------------------------------------
+ validation loss at iteration 10 on test set | lm loss value: 1.070103E+01 | lm loss PPL: 4.440147E+04 | 
+----------------------------------------------------------------------------------------------------------
+Running ctx_length=65536, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=1
+Cleaning up checkpoint directory: gpt-checkpoint
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 65536
+TP_SIZE: 2
+CP_SIZE: 8
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+--------------------------------
+CTX_LENGTH: 65536
+TP_SIZE: 2
+CP_SIZE: 8
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 16, data-parallel size: 1, context-parallel size: 8, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 2, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 8
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 65536
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 2
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 16
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 65536
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 65536
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 2
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 16
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+INFO:megatron.training.initialize:Setting logging level to 0
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 2
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.043 seconds
+WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
+> compiling and loading fused kernels ...

attnserver.run_attnserver.slurm.sh.343238.err.log

@@ -6502,3 +6502,32 @@ W0621 21:56:13.850000 3515598 site-packages/torch/distributed/run.py:766] ******
   warnings.warn(
 /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
   warnings.warn(
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank0]:     pretrain(
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 879, in pretrain
+[rank0]:     save_checkpoint(
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 469, in save_checkpoint
+[rank0]:     async_save_request = dist_checkpointing.save(state_dict, checkpoint_name, save_strategy,
+[rank0]:                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/dist_checkpointing/serialization.py", line 386, in save
+[rank0]:     common_strategy.save_common(state_dict, checkpoint_dir)
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/dist_checkpointing/strategies/common.py", line 48, in save_common
+[rank0]:     torch.save(common_state_dict, path)
+[rank0]:   File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/serialization.py", line 964, in save
+[rank0]:     with _open_zipfile_writer(f) as opened_zipfile:
+[rank0]:          ^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/serialization.py", line 828, in _open_zipfile_writer
+[rank0]:     return container(name_or_buffer)
+[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/serialization.py", line 792, in __init__
+[rank0]:     torch._C.PyTorchFileWriter(
+[rank0]: RuntimeError: Parent directory gpt-checkpoint/iter_0000010 does not exist.
+[rank0]:[W621 21:59:24.782079410 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+W0621 21:59:31.544000 3515598 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3515671 closing signal SIGTERM
+W0621 21:59:31.547000 3515598 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3515672 closing signal SIGTERM
+W0621 21:59:31.555000 3515598 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3515673 closing signal SIGTERM
+W0621 21:59:31.558000 3515598 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3515674 closing signal SIGTERM
+W0621 21:59:31.561000 3515598 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3515675 closing signal SIGTERM
+W0621 21:59:31.584000 3515598 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3515676 closing signal SIGTERM
+W0621 21:59:31.588000 3515598 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3515677 closing signal SIGTERM

attnserver.run_attnserver.slurm.sh.343238.out.log

@@ -22630,3 +22630,696 @@ batch tensor after cp: position_ids torch.Size([2, 10240])
 Start exporting trace 5
 Done exporting trace 5
  [2025-06-21 21:58:10] iteration        6/      10 | consumed samples:            6 | elapsed time per iteration (ms): 8301.6 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 134217728.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 6
+Done exporting trace 6
+ [2025-06-21 21:58:18] iteration        7/      10 | consumed samples:            7 | elapsed time per iteration (ms): 8399.8 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 67108864.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 7
+Done exporting trace 7
+ [2025-06-21 21:58:26] iteration        8/      10 | consumed samples:            8 | elapsed time per iteration (ms): 8378.4 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 33554432.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+batch tensor after cp: attention_mask torch.Size([2, 1, 10240, 81920])
+batch tensor after cp: position_ids torch.Size([2, 10240])
+Start exporting trace 8
+Done exporting trace 8
+ [2025-06-21 21:58:35] iteration        9/      10 | consumed samples:            9 | elapsed time per iteration (ms): 8393.7 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 16777216.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+batch tensor after cp: position_ids torch.Size([2, 10240])
+Start exporting trace 9
+Done exporting trace 9
+ [2025-06-21 21:58:43] iteration       10/      10 | consumed samples:           10 | elapsed time per iteration (ms): 8070.9 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 8388608.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+[after training is done] datetime: 2025-06-21 21:58:43 
+saving checkpoint at iteration      10 to gpt-checkpoint in torch_dist format
+DEBUG:megatron.training.checkpointing:rank: 13, takes 0.03457331657409668 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 9, takes 0.03459668159484863 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 15, takes 0.03460335731506348 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 11, takes 0.03466534614562988 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 8, takes 0.0349271297454834 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 12, takes 0.03521132469177246 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 5, takes 0.037848711013793945 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 1, takes 0.03790855407714844 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 0, takes 0.03832292556762695 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 2, takes 0.038640737533569336 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 6, takes 0.03871750831604004 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 14, takes 0.03852057456970215 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 7, takes 0.03897690773010254 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 10, takes 0.042994022369384766 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 4, takes 0.045691728591918945 to prepare state dict for ckpt 
+DEBUG:megatron.training.checkpointing:rank: 3, takes 0.046314239501953125 to prepare state dict for ckpt 
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]
+DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(206569472), 1), (np.int64(184549376), 2), (np.int64(184549376), 3), (np.int64(167839744), 4), (np.int64(167839744), 5), (np.int64(176160768), 6), (np.int64(176160768), 7)]

attnserver.run_attnserver.slurm.sh.343239.err.log

@@ -639,3 +639,457 @@ W0621 21:48:34.474000 792627 site-packages/torch/distributed/run.py:766] *******
   warnings.warn(
 /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
   warnings.warn(
+[rank4]:[E621 21:59:05.835457414 ProcessGroupNCCL.cpp:632] [Rank 4] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600012 milliseconds before timing out.
+[rank4]:[E621 21:59:05.838947487 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 4]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank4]:[E621 21:59:05.838973444 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank4]:[E621 21:59:05.839017292 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 4] First PG on this rank to signal dumping.
+[rank6]:[E621 21:59:05.841279436 ProcessGroupNCCL.cpp:632] [Rank 6] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600040 milliseconds before timing out.
+[rank10]:[E621 21:59:05.263170673 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 10] Observed flight recorder dump signal from another rank via TCPStore.
+[rank14]:[E621 21:59:05.263188285 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 14] Observed flight recorder dump signal from another rank via TCPStore.
+[rank6]:[E621 21:59:05.841893069 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 6]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank6]:[E621 21:59:05.841907689 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank10]:[E621 21:59:05.263518632 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 10] Received a dump signal due to a collective timeout from  rank 6 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank6]:[E621 21:59:05.841950398 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 6] First PG on this rank to signal dumping.
+[rank14]:[E621 21:59:05.263527176 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 14] Received a dump signal due to a collective timeout from  rank 6 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank14]:[E621 21:59:05.265583056 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 14] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank10]:[E621 21:59:05.265584315 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 10] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank3]:[E621 21:59:05.847201991 ProcessGroupNCCL.cpp:632] [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600020 milliseconds before timing out.
+[rank3]:[E621 21:59:05.847789234 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 3]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank3]:[E621 21:59:05.847813647 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank3]:[E621 21:59:05.847852370 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 3] First PG on this rank to signal dumping.
+[rank1]:[E621 21:59:05.861509950 ProcessGroupNCCL.cpp:632] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600041 milliseconds before timing out.
+[rank1]:[E621 21:59:05.862185767 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 1]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank1]:[E621 21:59:05.862201821 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank1]:[E621 21:59:05.862247317 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 1] First PG on this rank to signal dumping.
+[rank7]:[E621 21:59:05.874078529 ProcessGroupNCCL.cpp:632] [Rank 7] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600051 milliseconds before timing out.
+[rank7]:[E621 21:59:05.874698822 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 7]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank7]:[E621 21:59:05.874714009 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank7]:[E621 21:59:05.874751064 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 7] First PG on this rank to signal dumping.
+[rank0]:[E621 21:59:05.886110614 ProcessGroupNCCL.cpp:632] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600063 milliseconds before timing out.
+[rank0]:[E621 21:59:05.886715752 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 0]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank0]:[E621 21:59:05.886729098 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank0]:[E621 21:59:05.886768276 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 0] First PG on this rank to signal dumping.
+[rank2]:[E621 21:59:05.904074971 ProcessGroupNCCL.cpp:632] [Rank 2] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600077 milliseconds before timing out.
+[rank2]:[E621 21:59:05.904682497 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 2]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank2]:[E621 21:59:05.904696114 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank2]:[E621 21:59:05.904736894 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 2] First PG on this rank to signal dumping.
+[rank5]:[E621 21:59:05.919745642 ProcessGroupNCCL.cpp:632] [Rank 5] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600081 milliseconds before timing out.
+[rank5]:[E621 21:59:05.920397850 ProcessGroupNCCL.cpp:2268] [PG ID 0 PG GUID 0(default_pg) Rank 5]  failure detected by watchdog at work sequence id: 8 PG status: last enqueued work: 8, last completed work: 7
+[rank5]:[E621 21:59:05.920414087 ProcessGroupNCCL.cpp:670] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+[rank5]:[E621 21:59:05.920456736 ProcessGroupNCCL.cpp:2103] [PG ID 0 PG GUID 0(default_pg) Rank 5] First PG on this rank to signal dumping.
+[rank0]:[E621 21:59:06.177554073 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 0] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank0]:[E621 21:59:06.177941327 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 0] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank2]:     pretrain(
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank2]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank2]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank2]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank2]:                                                                 ^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank2]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank2]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank2]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank2]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank2]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank2]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank1]:     pretrain(
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank1]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank1]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank1]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank1]:                                                                 ^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank1]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank1]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank1]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank1]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank1]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank1]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank6]: Traceback (most recent call last):
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank6]:     pretrain(
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank6]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank6]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank6]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank6]:                                                                 ^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank6]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank6]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank6]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank6]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank6]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank6]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank6]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank0]:     pretrain(
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank0]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank0]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank0]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank0]:                                                                 ^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank0]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank0]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank0]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank0]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank0]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank0]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank5]: Traceback (most recent call last):
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank5]:     pretrain(
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank5]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank5]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank5]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank5]:                                                                 ^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank5]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank5]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank5]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank5]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank5]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank5]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank5]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank4]: Traceback (most recent call last):
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank4]:     pretrain(
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank4]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank4]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank4]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank4]:                                                                 ^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank4]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank4]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank4]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank4]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank4]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank4]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank4]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank7]: Traceback (most recent call last):
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank7]:     pretrain(
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank7]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank7]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank7]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank7]:                                                                 ^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank7]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank7]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank7]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank7]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank7]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank7]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank7]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank3]:     pretrain(
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 805, in pretrain
+[rank3]:     model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
+[rank3]:                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1283, in setup_model_and_optimizer
+[rank3]:     args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
+[rank3]:                                                                 ^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 1374, in load_checkpoint
+[rank3]:     state_dict, checkpoint_name, release, ckpt_type = _load_base_checkpoint(
+[rank3]:                                                       ^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 959, in _load_base_checkpoint
+[rank3]:     ckpt_format = _get_checkpoint_format(checkpoint_name)
+[rank3]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 882, in _get_checkpoint_format
+[rank3]:     is_torch_ckpt = any([f.startswith("mp_rank_0") for f in os.listdir(checkpoint_name)])
+[rank3]:                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010'
+[rank13]:[E621 21:59:06.773515282 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 13] Observed flight recorder dump signal from another rank via TCPStore.
+[rank12]:[E621 21:59:06.773515209 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 12] Observed flight recorder dump signal from another rank via TCPStore.
+[rank9]:[E621 21:59:06.773517421 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 9] Observed flight recorder dump signal from another rank via TCPStore.
+[rank15]:[E621 21:59:06.773543543 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 15] Observed flight recorder dump signal from another rank via TCPStore.
+[rank11]:[E621 21:59:06.773722841 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 11] Observed flight recorder dump signal from another rank via TCPStore.
+[rank12]:[E621 21:59:06.773844997 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 12] Received a dump signal due to a collective timeout from  rank 5 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank13]:[E621 21:59:06.773861391 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 13] Received a dump signal due to a collective timeout from  rank 5 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank9]:[E621 21:59:06.773896952 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 9] Received a dump signal due to a collective timeout from  rank 5 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank15]:[E621 21:59:06.773906772 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 15] Received a dump signal due to a collective timeout from  rank 5 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank11]:[E621 21:59:06.774081176 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 11] Received a dump signal due to a collective timeout from  rank 5 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank12]:[E621 21:59:06.774136950 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 12] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank15]:[E621 21:59:06.774151067 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 15] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank13]:[E621 21:59:06.774199178 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 13] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank9]:[E621 21:59:06.774262266 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 9] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank11]:[E621 21:59:06.774298919 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 11] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank3]:[E621 21:59:06.537038033 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 3] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank4]:[E621 21:59:06.537123897 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 4] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank3]:[E621 21:59:06.537436762 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 3] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank4]:[E621 21:59:06.537476982 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 4] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank7]:[E621 21:59:06.555968394 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 7] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank5]:[E621 21:59:06.555975449 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 5] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank5]:[E621 21:59:06.556200093 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 5] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank6]:[E621 21:59:06.556283370 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 6] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank1]:[E621 21:59:06.556319392 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 1] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank7]:[E621 21:59:06.556402526 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 7] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank6]:[E621 21:59:06.556450223 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 6] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank2]:[E621 21:59:06.556490940 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 2] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: 8, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank1]:[E621 21:59:06.556540047 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 1] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank2]:[E621 21:59:06.556654526 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 2] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank8]:[E621 21:59:06.034060191 ProcessGroupNCCL.cpp:1682] [PG ID 0 PG GUID 0(default_pg) Rank 8] Observed flight recorder dump signal from another rank via TCPStore.
+[rank8]:[E621 21:59:06.034569240 ProcessGroupNCCL.cpp:1743] [PG ID 0 PG GUID 0(default_pg) Rank 8] Received a dump signal due to a collective timeout from  rank 5 and we will try our best to dump the debug info. Last enqueued NCCL work: 7, last completed NCCL work: 7.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+[rank8]:[E621 21:59:06.034814979 ProcessGroupNCCL.cpp:1533] [PG ID 0 PG GUID 0(default_pg) Rank 8] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1
+[rank3]:[E621 21:59:06.661078617 ProcessGroupNCCL.cpp:684] [Rank 3] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
+[rank3]:[E621 21:59:06.661102822 ProcessGroupNCCL.cpp:698] [Rank 3] To avoid data inconsistency, we are taking the entire process down.
+[rank3]:[E621 21:59:06.662487744 ProcessGroupNCCL.cpp:1896] [PG ID 0 PG GUID 0(default_pg) Rank 3] Process group watchdog thread terminated with exception: [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600020 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1493e77785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x14938d852a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x14938d8547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x14938d855ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x1493e72f1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x1493e883bac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x1493e88cd850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+terminate called after throwing an instance of 'c10::DistBackendError'
+  what():  [PG ID 0 PG GUID 0(default_pg) Rank 3] Process group watchdog thread terminated with exception: [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600020 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1493e77785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x14938d852a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x14938d8547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x14938d855ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x1493e72f1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x1493e883bac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x1493e88cd850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+Exception raised from ncclCommWatchdog at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1902 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1493e77785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: <unknown function> + 0x11b4a6e (0x14938d824a6e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: <unknown function> + 0xe07bed (0x14938d477bed in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: <unknown function> + 0xd3b6d (0x1493e72f1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #4: <unknown function> + 0x94ac3 (0x1493e883bac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #5: <unknown function> + 0x126850 (0x1493e88cd850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+[rank0]:[E621 21:59:06.672471645 ProcessGroupNCCL.cpp:684] [Rank 0] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
+[rank0]:[E621 21:59:06.672494662 ProcessGroupNCCL.cpp:698] [Rank 0] To avoid data inconsistency, we are taking the entire process down.
+[rank0]:[E621 21:59:06.673713561 ProcessGroupNCCL.cpp:1896] [PG ID 0 PG GUID 0(default_pg) Rank 0] Process group watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600063 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1521eaf785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x152191052a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x1521910547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x152191055ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x1521eaaf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x1521ebfa4ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x1521ec036850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+terminate called after throwing an instance of 'c10::DistBackendError'
+  what():  [PG ID 0 PG GUID 0(default_pg) Rank 0] Process group watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600063 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1521eaf785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x152191052a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x1521910547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x152191055ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x1521eaaf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x1521ebfa4ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x1521ec036850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+Exception raised from ncclCommWatchdog at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1902 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1521eaf785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: <unknown function> + 0x11b4a6e (0x152191024a6e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: <unknown function> + 0xe07bed (0x152190c77bed in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: <unknown function> + 0xd3b6d (0x1521eaaf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #4: <unknown function> + 0x94ac3 (0x1521ebfa4ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #5: <unknown function> + 0x126850 (0x1521ec036850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+[rank1]:[E621 21:59:06.741526297 ProcessGroupNCCL.cpp:684] [Rank 1] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
+[rank1]:[E621 21:59:06.741549384 ProcessGroupNCCL.cpp:698] [Rank 1] To avoid data inconsistency, we are taking the entire process down.
+[rank5]:[E621 21:59:06.741810790 ProcessGroupNCCL.cpp:684] [Rank 5] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
+[rank5]:[E621 21:59:06.741826896 ProcessGroupNCCL.cpp:698] [Rank 5] To avoid data inconsistency, we are taking the entire process down.
+[rank1]:[E621 21:59:06.742820649 ProcessGroupNCCL.cpp:1896] [PG ID 0 PG GUID 0(default_pg) Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600041 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x154b5db785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x154b04052a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x154b040547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x154b04055ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x154af4019b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x154b5ef70ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x154b5f002850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+terminate called after throwing an instance of 'c10::DistBackendError'
+[rank5]:[E621 21:59:06.742982716 ProcessGroupNCCL.cpp:1896] [PG ID 0 PG GUID 0(default_pg) Rank 5] Process group watchdog thread terminated with exception: [Rank 5] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600081 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1498fd1785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x1498a3252a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x1498a32547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x1498a3255ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x1498fccf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x1498fe250ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x1498fe2e2850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+terminate called after throwing an instance of 'c10::DistBackendError'
+[rank4]:[E621 21:59:06.743223711 ProcessGroupNCCL.cpp:684] [Rank 4] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
+[rank4]:[E621 21:59:06.743247496 ProcessGroupNCCL.cpp:698] [Rank 4] To avoid data inconsistency, we are taking the entire process down.
+  what():  [PG ID 0 PG GUID 0(default_pg) Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600041 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x154b5db785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x154b04052a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x154b040547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x154b04055ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x154af4019b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x154b5ef70ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x154b5f002850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+Exception raised from ncclCommWatchdog at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1902 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x154b5db785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: <unknown function> + 0x11b4a6e (0x154b04024a6e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: <unknown function> + 0xe07bed (0x154b03c77bed in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: <unknown function> + 0xd3b6d (0x154af4019b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #4: <unknown function> + 0x94ac3 (0x154b5ef70ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #5: <unknown function> + 0x126850 (0x154b5f002850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+  what():  [PG ID 0 PG GUID 0(default_pg) Rank 5] Process group watchdog thread terminated with exception: [Rank 5] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600081 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1498fd1785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x1498a3252a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x1498a32547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x1498a3255ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x1498fccf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x1498fe250ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x1498fe2e2850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+Exception raised from ncclCommWatchdog at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1902 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1498fd1785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: <unknown function> + 0x11b4a6e (0x1498a3224a6e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: <unknown function> + 0xe07bed (0x1498a2e77bed in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: <unknown function> + 0xd3b6d (0x1498fccf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #4: <unknown function> + 0x94ac3 (0x1498fe250ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #5: <unknown function> + 0x126850 (0x1498fe2e2850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+[rank4]:[E621 21:59:06.744628998 ProcessGroupNCCL.cpp:1896] [PG ID 0 PG GUID 0(default_pg) Rank 4] Process group watchdog thread terminated with exception: [Rank 4] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600012 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x147b791785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x147b1f252a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x147b1f2547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x147b1f255ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x147b78cf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x147b7a1b1ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x147b7a243850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+terminate called after throwing an instance of 'c10::DistBackendError'
+  what():  [PG ID 0 PG GUID 0(default_pg) Rank 4] Process group watchdog thread terminated with exception: [Rank 4] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600012 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x147b791785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x147b1f252a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x147b1f2547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x147b1f255ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x147b78cf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x147b7a1b1ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x147b7a243850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+Exception raised from ncclCommWatchdog at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1902 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x147b791785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: <unknown function> + 0x11b4a6e (0x147b1f224a6e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: <unknown function> + 0xe07bed (0x147b1ee77bed in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: <unknown function> + 0xd3b6d (0x147b78cf1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #4: <unknown function> + 0x94ac3 (0x147b7a1b1ac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #5: <unknown function> + 0x126850 (0x147b7a243850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+[rank7]:[E621 21:59:06.750515400 ProcessGroupNCCL.cpp:684] [Rank 7] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
+[rank7]:[E621 21:59:06.750535519 ProcessGroupNCCL.cpp:698] [Rank 7] To avoid data inconsistency, we are taking the entire process down.
+[rank7]:[E621 21:59:06.751926965 ProcessGroupNCCL.cpp:1896] [PG ID 0 PG GUID 0(default_pg) Rank 7] Process group watchdog thread terminated with exception: [Rank 7] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600051 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1478891785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x14782f652a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x14782f6547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x14782f655ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x14781f619b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x14788a4eaac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x14788a57c850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+terminate called after throwing an instance of 'c10::DistBackendError'
+  what():  [PG ID 0 PG GUID 0(default_pg) Rank 7] Process group watchdog thread terminated with exception: [Rank 7] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=8, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=600000) ran for 600051 milliseconds before timing out.
+Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:635 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1478891785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x23d (0x14782f652a1d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0xc80 (0x14782f6547a0 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x14782f655ead in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #4: <unknown function> + 0xd3b6d (0x14781f619b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #5: <unknown function> + 0x94ac3 (0x14788a4eaac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #6: <unknown function> + 0x126850 (0x14788a57c850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+Exception raised from ncclCommWatchdog at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1902 (most recent call first):
+frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1478891785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
+frame #1: <unknown function> + 0x11b4a6e (0x14782f624a6e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #2: <unknown function> + 0xe07bed (0x14782f277bed in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
+frame #3: <unknown function> + 0xd3b6d (0x14781f619b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
+frame #4: <unknown function> + 0x94ac3 (0x14788a4eaac3 in /lib/x86_64-linux-gnu/libc.so.6)
+frame #5: <unknown function> + 0x126850 (0x14788a57c850 in /lib/x86_64-linux-gnu/libc.so.6)
+
+W0621 21:59:06.954000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2138450 closing signal SIGTERM
+W0621 21:59:06.955000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2138451 closing signal SIGTERM
+W0621 21:59:06.955000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2138452 closing signal SIGTERM
+W0621 21:59:06.956000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2138454 closing signal SIGTERM
+W0621 21:59:06.956000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2138455 closing signal SIGTERM
+W0621 21:59:06.956000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2138456 closing signal SIGTERM
+W0621 21:59:06.957000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2138457 closing signal SIGTERM
+E0621 21:59:07.699000 2138380 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: -6) local_rank: 3 (pid: 2138453) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in <module>
+    main()
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper
+    return arg(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main
+    launch(args)
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch
+    run(args)
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run
+    elastic_launch(
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent
+    raise ChildFailedError(
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+========================================================
+./pretrain_gpt_profile.py FAILED
+--------------------------------------------------------
+Failures:
+  <NO_OTHER_FAILURES>
+--------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2025-06-21_21:59:06
+  host      : fs-mbz-gpu-141
+  rank      : 3 (local_rank: 3)
+  exitcode  : -6 (pid: 2138453)
+  error_file: <N/A>
+  traceback : Signal 6 (SIGABRT) received by PID 2138453
+========================================================
++ set +x

attnserver.run_attnserver.slurm.sh.343240.err.log

@@ -0,0 +1,236 @@
++ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
+++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
+++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export _CE_M=
++++ _CE_M=
++++ export _CE_CONDA=
++++ _CE_CONDA=
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ '[' -z x ']'
+++ conda activate
+++ local cmd=activate
+++ case "$cmd" in
+++ __conda_activate activate
+++ '[' -n '' ']'
+++ local ask_conda
++++ PS1=
++++ __conda_exe shell.posix activate
++++ '[' -n '' ']'
++++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
+++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++++ unset _CE_M
++++ unset _CE_CONDA
++++ PS1='(base) '
++++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ export CONDA_SHLVL=1
++++ CONDA_SHLVL=1
++++ export 'CONDA_PROMPT_MODIFIER=(base) '
++++ CONDA_PROMPT_MODIFIER='(base) '
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ __conda_hashr
+++ '[' -n '' ']'
+++ '[' -n '' ']'
+++ hash -r
++ conda activate junda-attnserver
++ local cmd=activate
++ case "$cmd" in
++ __conda_activate activate junda-attnserver
++ '[' -n '' ']'
++ local ask_conda
+++ PS1='(base) '
+++ __conda_exe shell.posix activate junda-attnserver
+++ '[' -n '' ']'
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ unset _CE_M
+++ unset _CE_CONDA
+++ PS1='(junda-attnserver) '
+++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ export CONDA_SHLVL=2
+++ CONDA_SHLVL=2
+++ export CONDA_DEFAULT_ENV=junda-attnserver
+++ CONDA_DEFAULT_ENV=junda-attnserver
+++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
+++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
+++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++ __conda_hashr
++ '[' -n '' ']'
++ '[' -n '' ']'
++ hash -r
++ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ export PROF_TP_SIZE=2
++ PROF_TP_SIZE=2
++ export PROF_CP_SIZE=8
++ PROF_CP_SIZE=8
++ export PROF_BS=8
++ PROF_BS=8
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=1024
++ PROF_CTX_LENGTH=1024
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp2.cp8.bs8.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp2.cp8.bs8.json' ']'
++ echo 'Running ctx_length=1024, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=8'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343240 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 8 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:58:47.051000 1958026 site-packages/torch/distributed/run.py:766] 
+W0621 21:58:47.051000 1958026 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:58:47.051000 1958026 site-packages/torch/distributed/run.py:766] 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. 
+W0621 21:58:47.051000 1958026 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:58:47.064000 1033650 site-packages/torch/distributed/run.py:766] 
+W0621 21:58:47.064000 1033650 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:58:47.064000 1033650 site-packages/torch/distributed/run.py:766] 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. 
+W0621 21:58:47.064000 1033650 site-packages/torch/distributed/run.py:766] *****************************************
+[rank3]:[W621 21:59:10.986920489 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank1]:[W621 21:59:10.987444238 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank13]:[W621 21:59:10.117890005 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank7]:[W621 21:59:10.988655112 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank9]:[W621 21:59:10.118029109 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank11]:[W621 21:59:10.118039286 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank15]:[W621 21:59:10.118702525 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank5]:[W621 21:59:10.989377387 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank6]:[W621 21:59:10.993195910 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank2]:[W621 21:59:10.997045975 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank4]:[W621 21:59:10.997220851 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank12]:[W621 21:59:10.133479956 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank10]:[W621 21:59:10.133572956 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank14]:[W621 21:59:10.133682828 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank8]:[W621 21:59:10.215546371 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank0]:[W621 21:59:10.121500277 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(

attnserver.run_attnserver.slurm.sh.343240.out.log

@@ -0,0 +1,858 @@
+Running ctx_length=1024, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=8
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 2
+CP_SIZE: 8
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 2
+CP_SIZE: 8
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 16, data-parallel size: 1, context-parallel size: 8, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 2, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 8
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 1024
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 2
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 16
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 1024
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 1024
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 2
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 16
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 2
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.042 seconds
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.459 seconds
+time to initialize megatron (seconds): 8.926
+[after megatron is initialized] datetime: 2025-06-21 21:59:17 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+>>> embedding>>> embedding
+
+>>> decoder>>> decoder
+
+>>> output_layer>>> output_layer
+
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680 > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680
+
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+>>> embedding
+>>> embedding>>> decoder
+
+>>> output_layer
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (283719680 elements, 283719680 padded size):
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.embedding.position_embeddings.weight
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.weight
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x1468718f2420>, config_logger_dir='')
+>>> embedding>>> embedding
+
+>>> decoder
+>>> decoder
+>>> output_layer>>> output_layer
+
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 283719680
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 283719680
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt
+    will not load any checkpoints and will start from random
+(min, max) time across ranks (ms):
+    load-checkpoint ................................: (2.92, 3.04)
+[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:59:17 
+> building train, validation, and test datasets ...
+ > datasets target sizes (minimum size):
+    train:      10
+    validation: 1
+    test:       1
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)]
+> building train, validation, and test datasets for GPT ...
+INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=1024, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=<megatron.training.tokenizer.tokenizer._GPT2BPETokenizer object at 0x146871d076b0>, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None)
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.006372 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 66592
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.003615 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 66562
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.003301 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 66686
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+> finished creating GPT datasets ...
+[after dataloaders are built] datetime: 2025-06-21 21:59:17 
+done with setup ...
+training ...
+(min, max) time across ranks (ms):
+    model-and-optimizer-setup ......................: (432.15, 445.08)
+    train/valid/test-data-iterators-setup ..........: (22.61, 179.91)
+Setting rerun_state_machine.current_iteration to 0...
+[before the start of training step] datetime: 2025-06-21 21:59:17 
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor: tokens torch.Size([8, 8192])
+batch tensor: labels torch.Size([8, 8192])
+batch tensor: loss_mask torch.Size([8, 8192])
+batch tensor: attention_mask torch.Size([8, 1, 8192, 8192])
+batch tensor: position_ids torch.Size([8, 8192])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
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+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp:batch tensor after cp: loss_mask  tokenstorch.Size([8, 1024]) 
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1024])torch.Size([8, 1, 1024, 8192])
+
+batch tensor after cp: position_idsbatch tensor after cp:  torch.Size([8, 1024])labels
+batch tensor after cp: tokens torch.Size([8, 1024])
+ torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+batch tensor after cp: tokens torch.Size([8, 1024])
+batch tensor after cp: labels torch.Size([8, 1024])
+batch tensor after cp: loss_mask torch.Size([8, 1024])
+batch tensor after cp: attention_mask torch.Size([8, 1, 1024, 8192])
+batch tensor after cp: position_ids torch.Size([8, 1024])
+Start exporting trace 0
+Done exporting trace 0

attnserver.run_attnserver.slurm.sh.343243.out.log

@@ -14877,3 +14877,802 @@ make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/mega
 WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
 > compiling and loading fused kernels ...
 >>> done with compiling and loading fused kernels. Compilation time: 2.191 seconds
+time to initialize megatron (seconds): 7.532
+[after megatron is initialized] datetime: 2025-06-21 21:58:16 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (447297536 elements, 447297536 padded size):
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.embedding.position_embeddings.weight
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14f358f7a480>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+ loading distributed checkpoint from gpt-checkpoint at iteration 10
+Running ctx_length=49152, TP_SIZE=2, CP_SIZE=4, BATCH_SIZE=1
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 49152
+TP_SIZE: 2
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 8, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 2, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 4
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 49152
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 2
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 8
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 49152
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 49152
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 2
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 8
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 2
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.047 seconds
+WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.128 seconds
+time to initialize megatron (seconds): 7.450
+[after megatron is initialized] datetime: 2025-06-21 21:58:58 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 480851968
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 480851968
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 480851968
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 480851968
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 480851968
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 480851968
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (480851968 elements, 480851968 padded size):
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.embedding.position_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14fe8ed9a2d0>, config_logger_dir='')
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 480851968
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 480851968
+WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt
+    will not load any checkpoints and will start from random
+(min, max) time across ranks (ms):
+    load-checkpoint ................................: (5.02, 5.22)
+[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:59:01 
+> building train, validation, and test datasets ...
+ > datasets target sizes (minimum size):
+    train:      10
+    validation: 1
+    test:       1
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)]
+> building train, validation, and test datasets for GPT ...
+INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=49152, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=<megatron.training.tokenizer.tokenizer._GPT2BPETokenizer object at 0x14fe8f785430>, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None)
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.005154 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1387
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001636 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1386
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001354 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1389
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+> finished creating GPT datasets ...
+[after dataloaders are built] datetime: 2025-06-21 21:59:01 
+done with setup ...
+(min, max) time across ranks (ms):
+    model-and-optimizer-setup ......................: (2568.09, 2585.27)
+    train/valid/test-data-iterators-setup ..........: (45.93, 175.89)
+training ...
+Setting rerun_state_machine.current_iteration to 0...
+[before the start of training step] datetime: 2025-06-21 21:59:01 
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor:batch tensor after cp: labels  torch.Size([1, 12288])tokens
+ batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+batch tensor: tokens torch.Size([1, 49152])
+batch tensor: labels torch.Size([1, 49152])
+batch tensor: loss_mask torch.Size([1, 49152])
+batch tensor: attention_mask torch.Size([1, 1, 49152, 49152])
+batch tensor: position_ids torch.Size([1, 49152])
+batch tensor after cp: tokens torch.Size([1, 12288])
+batch tensor after cp: labels torch.Size([1, 12288])
+batch tensor after cp: loss_mask torch.Size([1, 12288])
+batch tensor after cp: attention_mask torch.Size([1, 1, 12288, 49152])
+batch tensor after cp: position_ids torch.Size([1, 12288])
+Start exporting trace 0
+Done exporting trace 0

attnserver.run_attnserver.slurm.sh.343244.out.log

@@ -11710,3 +11710,802 @@ make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/mega
 WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
 > compiling and loading fused kernels ...
 >>> done with compiling and loading fused kernels. Compilation time: 2.124 seconds
+time to initialize megatron (seconds): 7.465
+[after megatron is initialized] datetime: 2025-06-21 21:58:15 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 413743104
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 413743104
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 413743104
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 413743104
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 413743104
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 413743104
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 413743104
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 413743104
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (413743104 elements, 413743104 padded size):
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.embedding.word_embeddings.weight
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.embedding.position_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14f5cfd43b00>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+ loading distributed checkpoint from gpt-checkpoint at iteration 10
+Running ctx_length=40960, TP_SIZE=2, CP_SIZE=4, BATCH_SIZE=2
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 40960
+TP_SIZE: 2
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 8, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 2, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 4
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 40960
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 2
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 8
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 40960
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 40960
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 2
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 8
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 2
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.044 seconds
+WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.108 seconds
+time to initialize megatron (seconds): 7.123
+[after megatron is initialized] datetime: 2025-06-21 21:58:58 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 447297536
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (447297536 elements, 447297536 padded size):
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.embedding.position_embeddings.weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14a0fc21b260>, config_logger_dir='')
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 447297536
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt
+    will not load any checkpoints and will start from random
+(min, max) time across ranks (ms):
+    load-checkpoint ................................: (2.95, 3.61)
+[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:59:00 
+> building train, validation, and test datasets ...
+ > datasets target sizes (minimum size):
+    train:      10
+    validation: 1
+    test:       1
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)]
+> building train, validation, and test datasets for GPT ...
+INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=40960, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=<megatron.training.tokenizer.tokenizer._GPT2BPETokenizer object at 0x14a0fc807440>, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None)
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.005345 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1664
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001752 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1664
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001523 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1667
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+> finished creating GPT datasets ...
+[after dataloaders are built] datetime: 2025-06-21 21:59:00 
+done with setup ...
+(min, max) time across ranks (ms):
+    model-and-optimizer-setup ......................: (1993.56, 1994.68)
+    train/valid/test-data-iterators-setup ..........: (29.22, 179.62)
+training ...
+Setting rerun_state_machine.current_iteration to 0...
+[before the start of training step] datetime: 2025-06-21 21:59:00 
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+batch tensor after cp: tokens torch.Size([2, 20480])
+batch tensor after cp: labels torch.Size([2, 20480])
+batch tensor after cp: loss_mask torch.Size([2, 20480])
+batch tensor after cp: attention_mask torch.Size([2, 1, 20480, 81920])
+batch tensor after cp: position_ids torch.Size([2, 20480])
+Start exporting trace 0
+Done exporting trace 0

attnserver.run_attnserver.slurm.sh.343245.out.log

@@ -9564,3 +9564,899 @@ make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/mega
 >>> done with dataset index builder. Compilation time: 0.043 seconds
 > compiling and loading fused kernels ...
 >>> done with compiling and loading fused kernels. Compilation time: 2.218 seconds
+time to initialize megatron (seconds): 8.074
+[after megatron is initialized] datetime: 2025-06-21 21:58:17 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 346634240
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 346634240
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 346634240
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 346634240
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 346634240
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 346634240
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 346634240
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 346634240
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (346634240 elements, 346634240 padded size):
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.embedding.position_embeddings.weight
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x145396736360>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+ loading distributed checkpoint from gpt-checkpoint at iteration 10
+Running ctx_length=24576, TP_SIZE=2, CP_SIZE=4, BATCH_SIZE=4
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 24576
+TP_SIZE: 2
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 8, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 2, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 4
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 24576
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 2
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 8
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 24576
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 24576
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 2
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 8
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+INFO:megatron.training.initialize:Setting logging level to 0
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 2
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.045 seconds
+WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.145 seconds
+time to initialize megatron (seconds): 7.202
+[after megatron is initialized] datetime: 2025-06-21 21:58:58 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 380188672
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 380188672
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 380188672
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 380188672
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 380188672
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 380188672
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 380188672
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 380188672
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (380188672 elements, 380188672 padded size):
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.embedding.position_embeddings.weight
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14d1a7e3a4e0>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt
+    will not load any checkpoints and will start from random
+(min, max) time across ranks (ms):
+    load-checkpoint ................................: (3.64, 3.93)
+[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:58:59 
+> building train, validation, and test datasets ...
+ > datasets target sizes (minimum size):
+    train:      10
+    validation: 1
+    test:       1
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)]
+> building train, validation, and test datasets for GPT ...
+INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=24576, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=<megatron.training.tokenizer.tokenizer._GPT2BPETokenizer object at 0x14d1a7e8ef60>, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None)
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.004665 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 2774
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001884 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 2773
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001557 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 2778
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+> finished creating GPT datasets ...
+[after dataloaders are built] datetime: 2025-06-21 21:58:59 
+done with setup ...
+(min, max) time across ranks (ms):
+    model-and-optimizer-setup ......................: (1222.86, 1231.28)
+    train/valid/test-data-iterators-setup ..........: (32.66, 161.70)
+training ...
+Setting rerun_state_machine.current_iteration to 0...
+[before the start of training step] datetime: 2025-06-21 21:58:59 
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+Start exporting trace 0
+Done exporting trace 0
+ [2025-06-21 21:59:24] iteration        1/      10 | consumed samples:            1 | elapsed time per iteration (ms): 25150.1 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 4294967296.0 | number of skipped iterations:   1 | number of nan iterations:   0 |Number of parameters in transformer block in billions:  0.35
+
+Number of parameters in embedding layers in billions: 0.21
+Total number of parameters in billions: 0.56
+Number of parameters in most loaded shard in billions: 0.2795
+Theoretical memory footprints: weight and optimizer=4797.35 MB
+[Rank 1] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84936.0 | max reserved: 84936.0
+[Rank 4] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84568.0 | max reserved: 84568.0
+[Rank 3] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84376.0 | max reserved: 84376.0
+[Rank 2] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84376.0 | max reserved: 84376.0
+[Rank 6] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84748.0 | max reserved: 84748.0
+[Rank 0] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84168.0 | max reserved: 84168.0
+[Rank 7] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84748.0 | max reserved: 84748.0
+[Rank 5] (after 1 iterations) memory (MB) | allocated: 50709.89306640625 | max allocated: 80939.65478515625 | reserved: 84952.0 | max reserved: 84952.0
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+batch tensor: tokens torch.Size([4, 98304])
+batch tensor: labels torch.Size([4, 98304])
+batch tensor: loss_mask torch.Size([4, 98304])
+batch tensor: attention_mask torch.Size([4, 1, 98304, 98304])
+batch tensor: position_ids torch.Size([4, 98304])
+batch tensor after cp: tokens torch.Size([4, 24576])
+batch tensor after cp: labels torch.Size([4, 24576])
+batch tensor after cp: loss_mask torch.Size([4, 24576])
+batch tensor after cp: attention_mask torch.Size([4, 1, 24576, 98304])
+batch tensor after cp: position_ids torch.Size([4, 24576])
+Start exporting trace 1
+Done exporting trace 1
+ [2025-06-21 21:59:32] iteration        2/      10 | consumed samples:            2 | elapsed time per iteration (ms): 8143.1 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 2147483648.0 | number of skipped iterations:   1 | number of nan iterations:   0 |

attnserver.run_attnserver.slurm.sh.343246.err.log

@@ -4553,3 +4553,671 @@ W0621 21:57:59.783000 2375190 site-packages/torch/distributed/run.py:766]
 W0621 21:57:59.783000 2375190 site-packages/torch/distributed/run.py:766] *****************************************
 W0621 21:57:59.783000 2375190 site-packages/torch/distributed/run.py:766] 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. 
 W0621 21:57:59.783000 2375190 site-packages/torch/distributed/run.py:766] *****************************************
+[rank7]:[W621 21:58:22.758514097 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank5]:[W621 21:58:22.758845110 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank3]:[W621 21:58:22.758872980 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank2]:[W621 21:58:22.759631565 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank4]:[W621 21:58:22.759631437 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank6]:[W621 21:58:22.759857033 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank1]:[W621 21:58:22.763962101 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank0]:[W621 21:58:22.911433410 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank0]:     pretrain(
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank0]:     iteration, num_floating_point_operations_so_far = train(
+[rank0]:                                                       ^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank0]:     ) = train_step(
+[rank0]:         ^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank0]:     losses_reduced = forward_backward_func(
+[rank0]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank0]:     output_tensor, num_tokens = forward_step(
+[rank0]:                                 ^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank0]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank0]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank0]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank0]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank0]:     batch = next(global_batches)
+[rank0]:             ^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank0]:     attention_mask = torch.ones(
+[rank0]:                      ^^^^^^^^^^^
+[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank3]:     pretrain(
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank3]:     iteration, num_floating_point_operations_so_far = train(
+[rank3]:                                                       ^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank3]:     ) = train_step(
+[rank3]:         ^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank3]:     losses_reduced = forward_backward_func(
+[rank3]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank3]:     output_tensor, num_tokens = forward_step(
+[rank3]:                                 ^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank3]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank3]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank3]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank3]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank3]:     batch = next(global_batches)
+[rank3]:             ^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank3]:     attention_mask = torch.ones(
+[rank3]:                      ^^^^^^^^^^^
+[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank7]: Traceback (most recent call last):
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank7]:     pretrain(
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank7]:     iteration, num_floating_point_operations_so_far = train(
+[rank7]:                                                       ^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank7]:     ) = train_step(
+[rank7]:         ^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank7]:     losses_reduced = forward_backward_func(
+[rank7]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank7]:     output_tensor, num_tokens = forward_step(
+[rank7]:                                 ^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank7]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank7]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank7]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank7]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank7]:     batch = next(global_batches)
+[rank7]:             ^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank7]:     attention_mask = torch.ones(
+[rank7]:                      ^^^^^^^^^^^
+[rank7]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank2]:     pretrain(
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank2]:     iteration, num_floating_point_operations_so_far = train(
+[rank2]:                                                       ^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank2]:     ) = train_step(
+[rank2]:         ^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank2]:     losses_reduced = forward_backward_func(
+[rank2]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank2]:     output_tensor, num_tokens = forward_step(
+[rank2]:                                 ^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank2]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank2]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank2]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank2]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank2]:     batch = next(global_batches)
+[rank2]:             ^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank2]:     attention_mask = torch.ones(
+[rank2]:                      ^^^^^^^^^^^
+[rank2]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank5]: Traceback (most recent call last):
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank5]:     pretrain(
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank5]:     iteration, num_floating_point_operations_so_far = train(
+[rank5]:                                                       ^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank5]:     ) = train_step(
+[rank5]:         ^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank5]:     losses_reduced = forward_backward_func(
+[rank5]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank5]:     output_tensor, num_tokens = forward_step(
+[rank5]:                                 ^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank5]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank5]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank5]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank5]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank5]:     batch = next(global_batches)
+[rank5]:             ^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank5]:     attention_mask = torch.ones(
+[rank5]:                      ^^^^^^^^^^^
+[rank5]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank6]: Traceback (most recent call last):
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank6]:     pretrain(
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank6]:     iteration, num_floating_point_operations_so_far = train(
+[rank6]:                                                       ^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank6]:     ) = train_step(
+[rank6]:         ^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank6]:     losses_reduced = forward_backward_func(
+[rank6]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank6]:     output_tensor, num_tokens = forward_step(
+[rank6]:                                 ^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank6]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank6]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank6]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank6]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank6]:     batch = next(global_batches)
+[rank6]:             ^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank6]:     attention_mask = torch.ones(
+[rank6]:                      ^^^^^^^^^^^
+[rank6]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank4]: Traceback (most recent call last):
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank4]:     pretrain(
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank4]:     iteration, num_floating_point_operations_so_far = train(
+[rank4]:                                                       ^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank4]:     ) = train_step(
+[rank4]:         ^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank4]:     losses_reduced = forward_backward_func(
+[rank4]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank4]:     output_tensor, num_tokens = forward_step(
+[rank4]:                                 ^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank4]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank4]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank4]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank4]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank4]:     batch = next(global_batches)
+[rank4]:             ^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank4]:     attention_mask = torch.ones(
+[rank4]:                      ^^^^^^^^^^^
+[rank4]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank1]:     pretrain(
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank1]:     iteration, num_floating_point_operations_so_far = train(
+[rank1]:                                                       ^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank1]:     ) = train_step(
+[rank1]:         ^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank1]:     losses_reduced = forward_backward_func(
+[rank1]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank1]:     output_tensor, num_tokens = forward_step(
+[rank1]:                                 ^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank1]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank1]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank1]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank1]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank1]:     batch = next(global_batches)
+[rank1]:             ^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank1]:     attention_mask = torch.ones(
+[rank1]:                      ^^^^^^^^^^^
+[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 135.17 GiB is free. Including non-PyTorch memory, this process has 4.63 GiB memory in use. Of the allocated memory 3.12 GiB is allocated by PyTorch, and 57.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank1]:[W621 21:58:32.708889669 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank7]:[W621 21:58:32.750157203 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank5]:[W621 21:58:32.750340011 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank3]:[W621 21:58:32.760584988 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+W0621 21:58:33.836000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2375262 closing signal SIGTERM
+W0621 21:58:33.840000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2375263 closing signal SIGTERM
+W0621 21:58:33.840000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2375264 closing signal SIGTERM
+W0621 21:58:33.843000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2375265 closing signal SIGTERM
+W0621 21:58:33.843000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2375266 closing signal SIGTERM
+W0621 21:58:33.846000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2375267 closing signal SIGTERM
+W0621 21:58:33.846000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2375268 closing signal SIGTERM
+E0621 21:58:34.090000 2375190 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 7 (pid: 2375269) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in <module>
+    main()
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper
+    return arg(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main
+    launch(args)
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch
+    run(args)
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run
+    elastic_launch(
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent
+    raise ChildFailedError(
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+./pretrain_gpt_profile.py FAILED
+------------------------------------------------------------
+Failures:
+  <NO_OTHER_FAILURES>
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2025-06-21_21:58:33
+  host      : fs-mbz-gpu-791
+  rank      : 7 (local_rank: 7)
+  exitcode  : 1 (pid: 2375269)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
++ set +x
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=40960
++ PROF_CTX_LENGTH=40960
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L40960*tp2.cp4.bs8.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L40960*tp2.cp4.bs8.json' ']'
++ echo 'Running ctx_length=40960, TP_SIZE=2, CP_SIZE=4, BATCH_SIZE=8'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 1 --node_rank 0 --rdzv_id 343246 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-791:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 4 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 40960 --max-position-embeddings 40960 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:58:38.497000 2377039 site-packages/torch/distributed/run.py:766] 
+W0621 21:58:38.497000 2377039 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:58:38.497000 2377039 site-packages/torch/distributed/run.py:766] 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. 
+W0621 21:58:38.497000 2377039 site-packages/torch/distributed/run.py:766] *****************************************
+[rank1]:[W621 21:59:00.224207839 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank3]:[W621 21:59:00.225005281 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank7]:[W621 21:59:00.225006951 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank5]:[W621 21:59:00.225066158 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank2]:[W621 21:59:00.229077551 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank4]:[W621 21:59:00.229291060 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank6]:[W621 21:59:00.231343031 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank0]:[W621 21:59:00.377878739 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+[rank6]: Traceback (most recent call last):
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank6]:     pretrain(
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank6]:     iteration, num_floating_point_operations_so_far = train(
+[rank6]:                                                       ^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank6]:     ) = train_step(
+[rank6]:         ^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank6]:     losses_reduced = forward_backward_func(
+[rank6]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank6]:     output_tensor, num_tokens = forward_step(
+[rank6]:                                 ^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank6]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank6]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank6]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank6]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank6]:     batch = next(global_batches)
+[rank6]:             ^^^^^^^^^^^^^^^^^^^^
+[rank6]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank6]:     attention_mask = torch.ones(
+[rank6]:                      ^^^^^^^^^^^
+[rank6]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank4]: Traceback (most recent call last):
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank4]:     pretrain(
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank4]:     iteration, num_floating_point_operations_so_far = train(
+[rank4]:                                                       ^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank4]:     ) = train_step(
+[rank4]:         ^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank4]:     losses_reduced = forward_backward_func(
+[rank4]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank4]:     output_tensor, num_tokens = forward_step(
+[rank4]:                                 ^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank4]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank4]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank4]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank4]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank4]:     batch = next(global_batches)
+[rank4]:             ^^^^^^^^^^^^^^^^^^^^
+[rank4]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank4]:     attention_mask = torch.ones(
+[rank4]:                      ^^^^^^^^^^^
+[rank4]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank2]:     pretrain(
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank2]:     iteration, num_floating_point_operations_so_far = train(
+[rank2]:                                                       ^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank2]:     ) = train_step(
+[rank2]:         ^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank2]:     losses_reduced = forward_backward_func(
+[rank2]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank2]:     output_tensor, num_tokens = forward_step(
+[rank2]:                                 ^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank2]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank2]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank2]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank2]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank2]:     batch = next(global_batches)
+[rank2]:             ^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank2]:     attention_mask = torch.ones(
+[rank2]:                      ^^^^^^^^^^^
+[rank2]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank1]:     pretrain(
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank1]:     iteration, num_floating_point_operations_so_far = train(
+[rank1]:                                                       ^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank1]:     ) = train_step(
+[rank1]:         ^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank1]:     losses_reduced = forward_backward_func(
+[rank1]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank1]:     output_tensor, num_tokens = forward_step(
+[rank1]:                                 ^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank1]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank1]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank1]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank1]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank1]:     batch = next(global_batches)
+[rank1]:             ^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank1]:     attention_mask = torch.ones(
+[rank1]:                      ^^^^^^^^^^^
+[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank7]: Traceback (most recent call last):
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank7]:     pretrain(
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank7]:     iteration, num_floating_point_operations_so_far = train(
+[rank7]:                                                       ^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank7]:     ) = train_step(
+[rank7]:         ^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank7]:     losses_reduced = forward_backward_func(
+[rank7]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank7]:     output_tensor, num_tokens = forward_step(
+[rank7]:                                 ^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank7]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank7]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank7]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank7]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank7]:     batch = next(global_batches)
+[rank7]:             ^^^^^^^^^^^^^^^^^^^^
+[rank7]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank7]:     attention_mask = torch.ones(
+[rank7]:                      ^^^^^^^^^^^
+[rank7]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank0]:     pretrain(
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank0]:     iteration, num_floating_point_operations_so_far = train(
+[rank0]:                                                       ^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank0]:     ) = train_step(
+[rank0]:         ^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank0]:     losses_reduced = forward_backward_func(
+[rank0]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank0]:     output_tensor, num_tokens = forward_step(
+[rank0]:                                 ^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank0]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank0]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank0]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank0]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank0]:     batch = next(global_batches)
+[rank0]:             ^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank0]:     attention_mask = torch.ones(
+[rank0]:                      ^^^^^^^^^^^
+[rank0]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank3]:     pretrain(
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank3]:     iteration, num_floating_point_operations_so_far = train(
+[rank3]:                                                       ^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank3]:     ) = train_step(
+[rank3]:         ^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank3]:     losses_reduced = forward_backward_func(
+[rank3]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank3]:     output_tensor, num_tokens = forward_step(
+[rank3]:                                 ^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank3]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank3]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank3]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank3]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank3]:     batch = next(global_batches)
+[rank3]:             ^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank3]:     attention_mask = torch.ones(
+[rank3]:                      ^^^^^^^^^^^
+[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank5]: Traceback (most recent call last):
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
+[rank5]:     pretrain(
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 863, in pretrain
+[rank5]:     iteration, num_floating_point_operations_so_far = train(
+[rank5]:                                                       ^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 2229, in train
+[rank5]:     ) = train_step(
+[rank5]:         ^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 1382, in train_step
+[rank5]:     losses_reduced = forward_backward_func(
+[rank5]:                      ^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 518, in forward_backward_no_pipelining
+[rank5]:     output_tensor, num_tokens = forward_step(
+[rank5]:                                 ^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/pipeline_parallel/schedules.py", line 289, in forward_step
+[rank5]:     output_tensor, loss_func = forward_step_func(data_iterator, model)
+[rank5]:                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step
+[rank5]:     (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)
+[rank5]:                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch
+[rank5]:     batch = next(global_batches)
+[rank5]:             ^^^^^^^^^^^^^^^^^^^^
+[rank5]:   File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches
+[rank5]:     attention_mask = torch.ones(
+[rank5]:                      ^^^^^^^^^^^
+[rank5]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 800.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 134.87 GiB is free. Including non-PyTorch memory, this process has 4.94 GiB memory in use. Of the allocated memory 3.38 GiB is allocated by PyTorch, and 100.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[rank7]:[W621 21:59:11.565613408 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank3]:[W621 21:59:11.139136307 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank5]:[W621 21:59:11.140201601 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank1]:[W621 21:59:11.203365284 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+W0621 21:59:12.862000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2377110 closing signal SIGTERM
+W0621 21:59:12.865000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2377111 closing signal SIGTERM
+W0621 21:59:12.866000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2377112 closing signal SIGTERM
+W0621 21:59:12.869000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2377113 closing signal SIGTERM
+W0621 21:59:12.870000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2377114 closing signal SIGTERM
+W0621 21:59:12.876000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2377115 closing signal SIGTERM
+W0621 21:59:12.877000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2377116 closing signal SIGTERM
+E0621 21:59:13.593000 2377039 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 7 (pid: 2377117) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Traceback (most recent call last):
+  File "<frozen runpy>", line 198, in _run_module_as_main
+  File "<frozen runpy>", line 88, in _run_code
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in <module>
+    main()
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper
+    return arg(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main
+    launch(args)
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch
+    run(args)
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run
+    elastic_launch(
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent
+    raise ChildFailedError(
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+./pretrain_gpt_profile.py FAILED
+------------------------------------------------------------
+Failures:
+  <NO_OTHER_FAILURES>
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2025-06-21_21:59:12
+  host      : fs-mbz-gpu-791
+  rank      : 7 (local_rank: 7)
+  exitcode  : 1 (pid: 2377117)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
++ set +x
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=49152
++ PROF_CTX_LENGTH=49152
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L49152*tp2.cp4.bs8.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L49152*tp2.cp4.bs8.json' ']'
++ echo 'Running ctx_length=49152, TP_SIZE=2, CP_SIZE=4, BATCH_SIZE=8'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 1 --node_rank 0 --rdzv_id 343246 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-791:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 2 --context-parallel-size 4 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 49152 --max-position-embeddings 49152 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:59:17.894000 2378871 site-packages/torch/distributed/run.py:766] 
+W0621 21:59:17.894000 2378871 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:59:17.894000 2378871 site-packages/torch/distributed/run.py:766] 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. 
+W0621 21:59:17.894000 2378871 site-packages/torch/distributed/run.py:766] *****************************************

attnserver.run_attnserver.slurm.sh.343247.out.log

@@ -3933,3 +3933,1272 @@ make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/mega
 >>> done with dataset index builder. Compilation time: 0.058 seconds
 > compiling and loading fused kernels ...
 >>> done with compiling and loading fused kernels. Compilation time: 2.196 seconds
+time to initialize megatron (seconds): 7.659
+[after megatron is initialized] datetime: 2025-06-21 21:58:18 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 296302592
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 296302592
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 296302592
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder>>> embedding
+>>> output_layer
+
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 296302592
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 296302592
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 296302592
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 296302592
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 296302592
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (296302592 elements, 296302592 padded size):
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.embedding.position_embeddings.weight
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x149c1ed8a3f0>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+ loading distributed checkpoint from gpt-checkpoint at iteration 10
+Running ctx_length=8192, TP_SIZE=2, CP_SIZE=4, BATCH_SIZE=16
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 8192
+TP_SIZE: 2
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+using world size: 8, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 2, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 4
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 8192
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 2
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 8
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 8192
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 8192
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 2
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 8
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 2
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.053 seconds
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.115 seconds
+time to initialize megatron (seconds): 7.230
+[after megatron is initialized] datetime: 2025-06-21 21:59:01 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder>>> embedding
+>>> output_layer
+
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 313079808
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 313079808
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 313079808
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 313079808
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 313079808
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 313079808
+>>> embedding > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 313079808
+
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 313079808
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (313079808 elements, 313079808 padded size):
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.embedding.word_embeddings.weight
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.embedding.position_embeddings.weight
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14f72fbf6510>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt
+    will not load any checkpoints and will start from random
+(min, max) time across ranks (ms):
+    load-checkpoint ................................: (2.99, 3.13)
+[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:59:01 
+> building train, validation, and test datasets ...
+ > datasets target sizes (minimum size):
+    train:      10
+    validation: 1
+    test:       1
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)]
+> building train, validation, and test datasets for GPT ...
+INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=8192, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=<megatron.training.tokenizer.tokenizer._GPT2BPETokenizer object at 0x14f730026480>, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None)
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.005166 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8324
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001889 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8320
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001729 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8335
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+> finished creating GPT datasets ...
+[after dataloaders are built] datetime: 2025-06-21 21:59:01 
+done with setup ...
+(min, max) time across ranks (ms):
+    model-and-optimizer-setup ......................: (584.26, 602.26)
+    train/valid/test-data-iterators-setup ..........: (32.39, 161.16)
+training ...
+Setting rerun_state_machine.current_iteration to 0...
+[before the start of training step] datetime: 2025-06-21 21:59:01 
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 256.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 135.91 GiB is free. Including non-PyTorch memory, this process has 3.89 GiB memory in use. Of the allocated memory 2.37 GiB is allocated by PyTorch, and 63.54 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+Running ctx_length=12288, TP_SIZE=2, CP_SIZE=4, BATCH_SIZE=16
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 12288
+TP_SIZE: 2
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 8, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 2, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 4
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 12288
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 2
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 8
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 12288
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 12288
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 2
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 8
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 2
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.054 seconds
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.330 seconds