diff --git "a/attnserver.run_attnserver.slurm.sh.343239.out.log" "b/attnserver.run_attnserver.slurm.sh.343239.out.log" --- "a/attnserver.run_attnserver.slurm.sh.343239.out.log" +++ "b/attnserver.run_attnserver.slurm.sh.343239.out.log" @@ -9992,3 +9992,2750 @@ 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 +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 +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 .............................. 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 ..................... 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 ......................... 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 ...................................... 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 + > 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 +> 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 +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.404 seconds +time to initialize megatron (seconds): 7.836 +[after megatron is initialized] datetime: 2025-06-21 22:07:39 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 329857024 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 329857024 +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 (329857024 elements, 329857024 padded size): + 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.bias + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.embedding.word_embeddings.weight + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.layers.0.self_attention.linear_proj.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.layer_norm_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.weight + 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.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc2.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=, 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 ................................: (3.02, 3.26) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 22:07:40 +> 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=12288, 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=, 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: 0.004851 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 5549 +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: 0.001863 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 5546 +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: 0.001804 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 5557 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 22:07:40 +done with setup ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (892.43, 895.36) + train/valid/test-data-iterators-setup ..........: (15.45, 146.88) +training ... +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 22:07:40 +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp:batch tensor after cp: tokenslabels torch.Size([4, 6144]) +batch tensor after cp: torch.Size([4, 6144])loss_mask +torch.Size([4, 6144]) +batch tensor after cp:batch tensor after cp: attention_mask labels torch.Size([4, 1, 6144, 49152])torch.Size([4, 6144]) + +batch tensor after cp:batch tensor after cp: position_idsloss_mask torch.Size([4, 6144])torch.Size([4, 6144]) + +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) 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49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 0 +Done exporting trace 0 + [2025-06-21 22:07:58] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 18314.3 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[Rank 14] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22296.0 | max reserved: 22296.0 +[Rank 9] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22164.0 | max reserved: 22164.0 +[Rank 13] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22692.0 | max reserved: 22692.0 +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 8] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22164.0 | max reserved: 22164.0 +[Rank 1] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22152.0 | max reserved: 22152.0 +[Rank 12] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22308.0 | max reserved: 22308.0 +[Rank 11] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22200.0 | max reserved: 22200.0 +[Rank 10] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22200.0 | max reserved: 22200.0 +[Rank 0] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22104.0 | max reserved: 22104.0 +[Rank 15] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22536.0 | max reserved: 22536.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22164.0 | max reserved: 22164.0 +[Rank 3] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22164.0 | max reserved: 22164.0[Rank 5] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22164.0 | max reserved: 22164.0 + +[Rank 6] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22212.0 | max reserved: 22212.0 +[Rank 4] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22452.0 | max reserved: 22452.0 +[Rank 7] (after 1 iterations) memory (MB) | allocated: 14414.67431640625 | max allocated: 20926.39306640625 | reserved: 22212.0 | max reserved: 22212.0 +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 1 +Done exporting trace 1 + [2025-06-21 22:08:00] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 1682.2 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 2147483648.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 2 +Done exporting trace 2 + [2025-06-21 22:08:02] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 1650.5 | 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([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp:batch tensor: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) + batch tensor after cp:tokens attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 3 +Done exporting trace 3 + [2025-06-21 22:08:03] iteration 4/ 10 | consumed samples: 4 | elapsed time per iteration (ms): 1640.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 536870912.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 4 +Done exporting trace 4 + [2025-06-21 22:08:05] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 1651.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 268435456.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 5 +Done exporting trace 5 + [2025-06-21 22:08:07] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 1701.2 | 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([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 6 +Done exporting trace 6 + [2025-06-21 22:08:08] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 1690.2 | 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([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 7 +Done exporting trace 7 + [2025-06-21 22:08:10] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 1762.9 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 33554432.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor after cp:batch tensor: tokenslabels torch.Size([4, 49152])torch.Size([4, 6144]) + +batch tensor: batch tensor after cp:loss_mask labelstorch.Size([4, 49152]) +torch.Size([4, 6144]) +batch tensor after cp: batch tensor:loss_mask attention_masktorch.Size([4, 6144]) +batch tensor after cp:torch.Size([4, 1, 49152, 49152]) +attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor: batch tensor after cp:position_ids position_idstorch.Size([4, 49152]) +torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 8 +Done exporting trace 8 + [2025-06-21 22:08:12] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 1667.9 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 16777216.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +batch tensor: tokens torch.Size([4, 49152]) +batch tensor: labels torch.Size([4, 49152]) +batch tensor: loss_mask torch.Size([4, 49152]) +batch tensor: attention_mask torch.Size([4, 1, 49152, 49152]) +batch tensor: position_ids torch.Size([4, 49152]) +batch tensor after cp: tokens torch.Size([4, 6144]) +batch tensor after cp: labels torch.Size([4, 6144]) +batch tensor after cp: loss_mask torch.Size([4, 6144]) +batch tensor after cp: attention_mask torch.Size([4, 1, 6144, 49152]) +batch tensor after cp: position_ids torch.Size([4, 6144]) +Start exporting trace 9 +Done exporting trace 9 + [2025-06-21 22:08:13] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 1674.6 | 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 22:08:13 +saving checkpoint at iteration 10 to gpt-checkpoint in torch_dist format +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.03203129768371582 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.03205609321594238 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.03249001502990723 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.03251481056213379 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.03248167037963867 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.034761905670166016 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.0375828742980957 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.03753829002380371 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.05472278594970703 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.05476546287536621 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.05479860305786133 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.05682110786437988 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.05683469772338867 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.05688357353210449 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.0568850040435791 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.059110164642333984 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)] +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(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(413138944), 0), (np.int64(223346688), 1), (np.int64(234881024), 2), (np.int64(226492416), 3), (np.int64(226492416), 4), (np.int64(218265600), 5), (np.int64(218265600), 6), (np.int64(218259456), 7)] +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.315496206283569 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.309655427932739 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.317747592926025 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.315589189529419 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.309914588928223 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.316823482513428 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.310296297073364 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.316735744476318 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.310387849807739 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.3174145221710205 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 0.8373363018035889 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.384422779083252 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.316680431365967 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 2, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 8, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 9, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.311866283416748 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.312025547027588 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 6, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 13, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 4, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 12, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 14, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 15, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 10, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 11, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 3, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 5, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 0, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 5.314745187759399 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 1, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 7, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed 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+DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata 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1750543699.7544944 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750543699.7544944 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750543699.7544966 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:bucket_prep, time: 5.555152893066406e-05 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:bucket_prep, time: 6.437301635742188e-05 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 15, plan time: 0.009244918823242188 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750543699.7545013 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750543699.7545023 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:bucket_prep, time: 6.389617919921875e-05 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 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+DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, takes 1.811981201171875e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, takes 3.4809112548828125e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 798720, before: 1680785408, after: 1681584128 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__11_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543699.9253876, rank: 11, write(sync,parallel): 0.05653858184814453 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, takes 1.8358230590820312e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 3, takes 0.03238654136657715 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 2, takes 0.03347635269165039 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 274432, before: 1710583808, after: 1710858240 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__9_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543699.9442613, rank: 9, write(sync,parallel): 0.06725859642028809 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 4, takes 0.031247377395629883 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, takes 1.7642974853515625e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, takes 1.7404556274414062e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 10, takes 0.03482341766357422 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, takes 0.03553295135498047 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, takes 0.03745388984680176 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, takes 0.03255152702331543 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, takes 0.0309293270111084 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, takes 0.03515458106994629 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 733184, before: 1682554880, after: 1683288064 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 failed +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 1, takes 2.1219253540039062e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 143360, before: 1686913024, after: 1687056384 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 786432, before: 1684025344, after: 1684811776 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 647168, before: 1745899520, after: 1746546688 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 135168, before: 1684672512, after: 1684807680 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 733184, before: 1682567168, after: 1683300352 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__12_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.018274, rank: 12, write(sync,parallel): 0.05564260482788086 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 659456, before: 1685573632, after: 1686233088 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 790528, before: 1684025344, after: 1684815872 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__14_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 647168, before: 1745899520, after: 1746546688 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__8_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.0229206, rank: 14, write(sync,parallel): 0.057500362396240234 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.0241973, rank: 8, write(sync,parallel): 0.06079459190368652 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 651264, before: 1686913024, after: 1687564288 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__10_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.0265474, rank: 10, write(sync,parallel): 0.07027602195739746 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 1, takes 0.03471636772155762 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 135168, before: 1684672512, after: 1684807680 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__13_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.037049, rank: 13, write(sync,parallel): 0.053795576095581055 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 failed +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 770048, before: 1685573632, after: 1686343680 +ERROR:megatron.core.dist_checkpointing.strategies.filesystem_async:Local process 0 encountered an error: [Errno 13] Permission denied: 'gpt-checkpoint/iter_0000010/__15_0.distcp' +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.046938, rank: 15, write(sync,parallel): 0.058882713317871094 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 22179840, before: 1679671296, after: 1701851136 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 0, takes 2.7179718017578125e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 55558144, before: 1679175680, after: 1734733824 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 55427072, before: 1682337792, after: 1737764864 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 38821888, before: 1699041280, after: 1737863168 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 139264, before: 1720008704, after: 1720147968 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 0, takes 0.04057049751281738 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 0, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 1, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 9, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 2, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 10, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 3, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.18s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 4, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.27s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 6, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 11, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 5, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 7, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.27s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.18s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.17s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0014s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0011s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0015s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0012s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0014s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.16s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0010s +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 97570816, before: 1703763968, after: 1801334784 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 339968, before: 1993269248, after: 1993609216 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.20s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0011s +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 139411456, before: 1703763968, after: 1843175424 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.17s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0011s +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.2174459, rank: 6, write(sync,parallel): 0.32836055755615234 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 139309056, before: 1682337792, after: 1821646848 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 139276288, before: 1679175680, after: 1818451968 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.40s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.2742217, rank: 7, write(sync,parallel): 0.38229942321777344 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.287221, rank: 5, write(sync,parallel): 0.40310001373291016 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 206663680, before: 1699049472, after: 1905713152 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 211984384, before: 1679671296, after: 1891655680 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.49s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 211714048, before: 1678516224, after: 1890230272 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.3538835, rank: 4, write(sync,parallel): 0.40372371673583984 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.363429, rank: 2, write(sync,parallel): 0.41827893257141113 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.381626, rank: 3, write(sync,parallel): 0.4369378089904785 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.49s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.51s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 417927168, before: 1720008704, after: 2137935872 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.8301456, rank: 1, write(sync,parallel): 0.796142578125 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 417771520, before: 1993269248, after: 2411040768 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.87s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750543700.9042387, rank: 0, write(sync,parallel): 0.7997002601623535 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.88s from forking +Running ctx_length=16384, TP_SIZE=2, CP_SIZE=8, BATCH_SIZE=4 +Cleaning up checkpoint directory: gpt-checkpoint +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 16384 +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: 16384 +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