diff --git "a/attnserver.run_attnserver.slurm.sh.343203.out.log" "b/attnserver.run_attnserver.slurm.sh.343203.out.log" --- "a/attnserver.run_attnserver.slurm.sh.343203.out.log" +++ "b/attnserver.run_attnserver.slurm.sh.343203.out.log" @@ -9351,3 +9351,3114 @@ CHECKPOINT_PATH: gpt-checkpoint PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron -------------------------------- /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written. +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +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: 2, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 8, 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 ........................... 2 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 8192 + encoder_tensor_model_parallel_size .............. 0 + end_weight_decay ................................ 0.1 + eod_mask_loss ................................... False + error_injection_rate ............................ 0 + error_injection_type ............................ transient_error + eval_interval ................................... 16 + eval_iters ...................................... 1 + evidence_data_path .............................. None + exit_duration_in_mins ........................... None + exit_interval ................................... None + exit_on_missing_checkpoint ...................... False + exit_signal_handler ............................. False + exp_avg_dtype ................................... torch.float32 + exp_avg_sq_dtype ................................ torch.float32 + expert_model_parallel_size ...................... 1 + expert_tensor_parallel_size ..................... 8 + 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 ......................... 8192 + max_tokens_to_oom ............................... 12000 + memory_snapshot_path ............................ snapshot.pickle + merge_file ...................................... merges.txt + micro_batch_size ................................ 1 + microbatch_group_size_per_vp_stage .............. None + mid_level_dataset_surplus ....................... 0.005 + min_loss_scale .................................. 1.0 + min_lr .......................................... 0.0 + mlp_chunks_for_prefill .......................... 1 + mmap_bin_files .................................. True + mock_data ....................................... True + moe_apply_probs_on_input ........................ False + moe_aux_loss_coeff .............................. 0.0 + moe_enable_deepep ............................... False + moe_expert_capacity_factor ...................... None + moe_extended_tp ................................. False + moe_ffn_hidden_size ............................. None + moe_grouped_gemm ................................ False + moe_input_jitter_eps ............................ None + moe_layer_freq .................................. 1 + moe_layer_recompute ............................. False + moe_pad_expert_input_to_capacity ................ False + moe_per_layer_logging ........................... False + moe_permute_fusion .............................. False + moe_router_bias_update_rate ..................... 0.001 + moe_router_dtype ................................ None + moe_router_enable_expert_bias ................... False + moe_router_force_load_balancing ................. False + moe_router_group_topk ........................... None + moe_router_load_balancing_type .................. aux_loss + moe_router_num_groups ........................... None + moe_router_padding_for_fp8 ...................... False + moe_router_pre_softmax .......................... False + moe_router_score_function ....................... softmax + moe_router_topk ................................. 2 + moe_router_topk_scaling_factor .................. None + moe_shared_expert_intermediate_size ............. None + moe_shared_expert_overlap ....................... False + moe_token_dispatcher_type ....................... allgather + moe_token_drop_policy ........................... probs + moe_use_legacy_grouped_gemm ..................... False + moe_use_upcycling ............................... False + moe_z_loss_coeff ................................ None + mrope_section ................................... None + mscale .......................................... 1.0 + mscale_all_dim .................................. 1.0 + mtp_loss_scaling_factor ......................... 0.1 + mtp_num_layers .................................. None + multi_latent_attention .......................... False + nccl_all_reduce_for_prefill ..................... False + nccl_communicator_config_path ................... None + nccl_ub ......................................... False + no_load_optim ................................... None + no_load_rng ..................................... None + no_persist_layer_norm ........................... False + no_rope_freq .................................... None + no_save_optim ................................... None + no_save_rng ..................................... None + non_persistent_ckpt_type ........................ None + non_persistent_global_ckpt_dir .................. None + non_persistent_local_ckpt_algo .................. fully_parallel + non_persistent_local_ckpt_dir ................... None + non_persistent_save_interval .................... None + norm_epsilon .................................... 1e-05 + normalization ................................... LayerNorm + num_attention_heads ............................. 64 + num_channels .................................... 3 + num_classes ..................................... 1000 + num_dataset_builder_threads ..................... 1 + num_distributed_optimizer_instances ............. 1 + num_experts ..................................... None + num_layers ...................................... 2 + num_layers_at_end_in_bf16 ....................... 1 + num_layers_at_start_in_bf16 ..................... 1 + num_layers_per_virtual_pipeline_stage ........... None + num_query_groups ................................ 16 + num_virtual_stages_per_pipeline_rank ............ None + num_workers ..................................... 2 + object_storage_cache_path ....................... None + one_logger_async ................................ False + one_logger_project .............................. megatron-lm + one_logger_run_name ............................. None + onnx_safe ....................................... None + openai_gelu ..................................... False + optimizer ....................................... adam + optimizer_cpu_offload ........................... False + optimizer_offload_fraction ...................... 1.0 + output_bert_embeddings .......................... False + overlap_cpu_optimizer_d2h_h2d ................... False + overlap_grad_reduce ............................. False + overlap_p2p_comm ................................ False + overlap_p2p_comm_warmup_flush ................... False + overlap_param_gather ............................ False + overlap_param_gather_with_optimizer_step ........ False + override_opt_param_scheduler .................... False + params_dtype .................................... torch.float16 + patch_dim ....................................... 16 + per_split_data_args_path ........................ None + perform_initialization .......................... True + pin_cpu_grads ................................... True + pin_cpu_params .................................. True + pipeline_model_parallel_comm_backend ............ None + pipeline_model_parallel_size .................... 1 + pipeline_model_parallel_split_rank .............. None + position_embedding_type ......................... learned_absolute + pretrained_checkpoint ........................... None + profile ......................................... False + profile_ranks ................................... [0] + profile_step_end ................................ 12 + profile_step_start .............................. 10 + q_lora_rank ..................................... None + qk_head_dim ..................................... 128 + qk_l2_norm ...................................... False + qk_layernorm .................................... False + qk_pos_emb_head_dim ............................. 64 + query_in_block_prob ............................. 0.1 + rampup_batch_size ............................... None + rank ............................................ 0 + recompute_granularity ........................... None + recompute_method ................................ None + recompute_modules ............................... None + recompute_num_layers ............................ None + record_memory_history ........................... False + relative_attention_max_distance ................. 128 + relative_attention_num_buckets .................. 32 + replication ..................................... False + replication_factor .............................. 2 + replication_jump ................................ None + rerun_mode ...................................... disabled + reset_attention_mask ............................ False + reset_position_ids .............................. False + result_rejected_tracker_filename ................ None + retriever_report_topk_accuracies ................ [] + retriever_score_scaling ......................... False + retriever_seq_length ............................ 256 + retro_add_retriever ............................. False + retro_attention_gate ............................ 1 + retro_cyclic_train_iters ........................ None + retro_encoder_attention_dropout ................. 0.1 + retro_encoder_hidden_dropout .................... 0.1 + retro_encoder_layers ............................ 2 + retro_num_neighbors ............................. 2 + retro_num_retrieved_chunks ...................... 2 + retro_project_dir ............................... None + retro_verify_neighbor_count ..................... True + rope_scaling_factor ............................. 8.0 + rotary_base ..................................... 10000 + rotary_interleaved .............................. False + rotary_percent .................................. 1.0 + rotary_scaling_factor ........................... 1.0 + rotary_seq_len_interpolation_factor ............. None + run_workload_inspector_server ................... False + sample_rate ..................................... 1.0 + save ............................................ gpt-checkpoint + save_interval ................................... 16 + scatter_gather_tensors_in_pipeline .............. True + seed ............................................ 1234 + seq_length ...................................... 8192 + sequence_parallel ............................... False + sgd_momentum .................................... 0.9 + short_seq_prob .................................. 0.1 + skip_train ...................................... False + skipped_train_samples ........................... 0 + spec ............................................ None + split ........................................... None + squared_relu .................................... False + start_weight_decay .............................. 0.1 + straggler_ctrlr_port ............................ 65535 + straggler_minmax_count .......................... 1 + suggested_communication_unit_size ............... None + swiglu .......................................... False + swin_backbone_type .............................. tiny + symmetric_ar_type ............................... None + te_rng_tracker .................................. False + tensor_model_parallel_size ...................... 8 + 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 943 dummy tokens (new size: 51200) +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 8 +> initialized pipeline model parallel with size 1 +> setting random seeds to 1234 ... +> compiling dataset index builder ... +make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +make: Nothing to be done for 'default'. +make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +>>> done with dataset index builder. Compilation time: 0.047 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.551 seconds +time to initialize megatron (seconds): 9.407 +[after megatron is initialized] datetime: 2025-06-21 21:17:47 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 103872000 +>>> embedding>>> embedding + +>>> decoder +>>> decoder>>> output_layer + +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 103872000 +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 (103872000 elements, 103872000 padded size): + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.embedding.word_embeddings.weight + module.decoder.final_layernorm.weight + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.self_attention.linear_qkv.weight + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.embedding.position_embeddings.weight +INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=, config_logger_dir='') +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +>>> embedding +>>> decoder>>> embedding +>>> output_layer + +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 103872000 +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 ................................: (82.18, 82.57) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:17:48 +> building train, validation, and test datasets ... + > datasets target sizes (minimum size): + train: 10 + validation: 1 + test: 1 +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)] +> building train, validation, and test datasets for GPT ... +INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=8192, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=, 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: 0.005283 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8324 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.002156 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8320 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.001968 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8335 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 21:17:48 +done with setup ... +training ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (1002.91, 1028.92) + train/valid/test-data-iterators-setup ..........: (17.56, 129.65) +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 21:17:48 +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 0 +Done exporting trace 0 +Number of parameters in transformer block in billions: 0.35 +Number of parameters in embedding layers in billions: 0.21 +Total number of parameters in billions: 0.56 +Number of parameters in most loaded shard in billions: 0.0703 +Theoretical memory footprints: weight and optimizer=1206.09 MB + [2025-06-21 21:17:58] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 10042.8 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[Rank 0] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18784.0 | max reserved: 18784.0 +[Rank 9] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 19072.0 | max reserved: 19072.0[Rank 13] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 19072.0 | max reserved: 19072.0 + +[Rank 12] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 19072.0 | max reserved: 19072.0[Rank 15] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 19072.0 | max reserved: 19072.0 + +[Rank 10] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18560.0 | max reserved: 18560.0 +[Rank 11] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 19072.0 | max reserved: 19072.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18528.0 | max reserved: 18528.0 +[Rank 8] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18560.0 | max reserved: 18560.0 +[Rank 4] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 19040.0 | max reserved: 19040.0 +[Rank 3] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18528.0 | max reserved: 18528.0 +[Rank 14] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18560.0 | max reserved: 18560.0 +[Rank 5] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18528.0 | max reserved: 18528.0 +[Rank 7] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18528.0 | max reserved: 18528.0[Rank 6] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18528.0 | max reserved: 18528.0 + +[Rank 1] (after 1 iterations) memory (MB) | allocated: 7475.97705078125 | max allocated: 17159.11376953125 | reserved: 18528.0 | max reserved: 18528.0 +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens batch tensor: tokens torch.Size([4, 32768])torch.Size([4, 32768]) + +batch tensor: labels batch tensor:torch.Size([4, 32768]) +labelsbatch tensor: torch.Size([4, 32768])loss_mask + batch tensor:torch.Size([4, 32768]) +loss_mask torch.Size([4, 32768])batch tensor: + batch tensor:attention_mask attention_mask torch.Size([4, 1, 32768, 32768]) +torch.Size([4, 1, 32768, 32768]) +batch tensor: batch tensor:position_ids position_ids torch.Size([4, 32768]) +torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) 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tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 1 +Done exporting trace 1 + [2025-06-21 21:17:59] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 829.3 | 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, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768])batch tensor after cp: +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) + tokensbatch tensor: torch.Size([4, 16384])labels + batch tensor after cp:torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +labels batch tensor: torch.Size([4, 16384])loss_mask + batch tensor after cp:torch.Size([4, 32768]) +loss_mask batch tensor:torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +attention_maskbatch tensor after cp: attention_mask torch.Size([4, 1, 32768, 32768]) +torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor:batch tensor after cp: position_idsposition_ids torch.Size([4, 32768])torch.Size([4, 16384]) + +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 2 +Done exporting trace 2 + [2025-06-21 21:17:59] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 810.4 | 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, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) 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tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) 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32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 3 +Done exporting trace 3 + [2025-06-21 21:18:00] iteration 4/ 10 | consumed samples: 4 | elapsed time per iteration (ms): 803.2 | 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, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 4 +Done exporting trace 4 + [2025-06-21 21:18:01] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 808.4 | 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, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 5 +Done exporting trace 5 + [2025-06-21 21:18:02] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 801.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 134217728.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 6 +Done exporting trace 6 + [2025-06-21 21:18:03] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 803.1 | 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, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 7 +Done exporting trace 7 + [2025-06-21 21:18:03] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 802.8 | 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, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) 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tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) 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32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 8 +Done exporting trace 8 + [2025-06-21 21:18:04] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 811.4 | 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, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) 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tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) 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torch.Size([4, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: batch tensor:tokens labelstorch.Size([4, 16384]) +torch.Size([4, 32768])batch tensor after cp: +batch tensor: loss_mask torch.Size([4, 32768]) + batch tensor:labels loss_masktorch.Size([4, 16384]) +torch.Size([4, 32768])batch tensor after cp: + loss_mask batch tensor:torch.Size([4, 16384]) +attention_mask batch tensor after cp: attention_masktorch.Size([4, 1, 32768, 32768]) +batch tensor:torch.Size([4, 1, 16384, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +position_ids batch tensor after cp: torch.Size([4, 32768])position_ids + torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 9 +Done exporting trace 9 + [2025-06-21 21:18:05] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 816.6 | 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 21:18:05 +saving checkpoint at iteration 10 to gpt-checkpoint in torch_dist format +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.027934789657592773 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.02830338478088379 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.02830791473388672 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.02831554412841797 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.028330326080322266 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.028434038162231445 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.028593778610229492 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.028633594512939453 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.02865886688232422 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.028678178787231445 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.028684616088867188 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.029598474502563477 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.028784990310668945 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.029225587844848633 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.03193783760070801 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.03473639488220215 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.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +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(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(209748992), 0), (np.int64(211812352), 1)] +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(310756352), 0), (np.int64(312475648), 1)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(310756352), 0), (np.int64(312475648), 1)] +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2518837451934814 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2692174911499023 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.246706485748291 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2469241619110107 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.247143268585205 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2577247619628906 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2528297901153564 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2528393268585205 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.247096300125122 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2525315284729004 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2475941181182861 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2698299884796143 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.248030424118042 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2485525608062744 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.2551679611206055 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 9, 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: 15, 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: 8, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 0.012876272201538086 +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: 14, 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: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: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: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: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:loaded global metadata reuse verification: no loaded plans passed +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:rank: 4, starting state dict save +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:rank: 12, 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: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:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 2, 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:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 6, starting state dict save 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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:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 10, plan time: 0.01046442985534668 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 15, plan time: 0.01041269302368164 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 9, plan time: 0.010485410690307617 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 4, plan time: 0.0064411163330078125 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 7, plan time: 0.006659030914306641 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 2, plan time: 0.00569605827331543 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 3, plan time: 0.00619959831237793 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 6, plan time: 0.005338191986083984 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 1, plan time: 0.0036013126373291016 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 13, plan time: 0.010318994522094727 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750540687.077555 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750540687.0775568 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 11, plan time: 0.009985208511352539 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750540687.0772562 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750540687.077262 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 8, plan time: 0.010329723358154297 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 12, plan time: 0.007360696792602539 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750540687.0775564 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750540687.0772696 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 14, plan time: 0.009545326232910156 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 5, plan time: 0.004721164703369141 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750540687.0772786 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 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0.04387235641479492 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04370427131652832 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1216576 rank: 4, write(async) time: 0.04410076141357422 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04477047920227051 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1224923 rank: 14, write(async) time: 0.045187950134277344 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.0452115535736084 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1228569 rank: 15, write(async) time: 0.04559445381164551 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04527616500854492 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04545116424560547 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.123016 rank: 12, write(async) time: 0.04572129249572754 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04687380790710449 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1230788 rank: 10, write(async) time: 0.04582333564758301 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1248317 rank: 2, write(async) time: 0.04727602005004883 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.050858497619628906 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.128907 rank: 5, write(async) time: 0.05130743980407715 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.052622318267822266 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1306286 rank: 1, write(async) time: 0.053053855895996094 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.047566890716552734 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1309693 rank: 0, write(async) time: 0.048018693923950195 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05401301383972168 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.131994 rank: 3, write(async) time: 0.05442643165588379 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.054290056228637695 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1322808 rank: 6, write(async) time: 0.054711103439331055 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.054473876953125 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.132146 rank: 9, write(async) time: 0.05487489700317383 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05534648895263672 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1330233 rank: 13, write(async) time: 0.055745601654052734 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.059241533279418945 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1369283 rank: 11, write(async) time: 0.05964541435241699 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.06319689750671387 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.1409068 rank: 8, write(async) time: 0.06361269950866699 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, takes 1.4066696166992188e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, takes 1.7404556274414062e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 10, takes 1.5974044799804688e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, takes 1.6927719116210938e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 9, takes 1.4066696166992188e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, takes 1.5497207641601562e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 7, takes 1.6450881958007812e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 4, takes 1.5974044799804688e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 2, takes 1.5974044799804688e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 11, takes 1.6689300537109375e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 1, takes 1.621246337890625e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 5, takes 1.5020370483398438e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 6, takes 1.8358230590820312e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 3, takes 1.5735626220703125e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, takes 0.023319244384765625 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, takes 0.021721363067626953 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, takes 0.021948575973510742 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 10, takes 0.024384021759033203 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 7, takes 0.02356553077697754 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 4, takes 0.023459196090698242 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 2, takes 0.024720430374145508 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 9, takes 0.030389070510864258 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 11, takes 0.023252248764038086 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, takes 0.0335085391998291 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 1, takes 0.029809951782226562 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 5, takes 0.030182600021362305 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 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 6, takes 0.030675411224365234 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 3, takes 0.031054258346557617 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, takes 1.2874603271484375e-05 to finish D2H +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.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:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +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:0 started +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:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +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:0 started +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:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 0, takes 1.5735626220703125e-05 to finish D2H +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: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 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, takes 0.04099774360656738 to schedule async ckpt +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.filesystem_async:FileSystemWriterAsync: collecting worker results... +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:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 0, takes 0.03066539764404297 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: 2, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 9, joining self.process 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before: 1706303488, after: 1815834624 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109871104, before: 1696878592, after: 1806749696 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 111886336, before: 1700319232, after: 1812205568 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109649920, before: 1703530496, after: 1813180416 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109862912, before: 1693806592, after: 1803669504 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110145536, before: 1711521792, after: 1821667328 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109887488, before: 1700286464, after: 1810173952 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 112300032, before: 1711456256, after: 1823756288 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.5678608, rank: 2, write(sync,parallel): 0.3188765048980713 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109731840, before: 1694650368, after: 1804382208 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.57575, rank: 1, write(sync,parallel): 0.31416821479797363 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.579215, rank: 5, write(sync,parallel): 0.31743645668029785 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110125056, before: 1699016704, after: 1809141760 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109797376, before: 1731325952, after: 1841123328 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109785088, before: 1691467776, after: 1801252864 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109867008, before: 1691467776, after: 1801334784 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109719552, before: 1706303488, after: 1816023040 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.589418, rank: 10, write(sync,parallel): 0.3476243019104004 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110059520, before: 1699332096, after: 1809391616 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.38s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110096384, before: 1718771712, after: 1828868096 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.5997057, rank: 11, write(sync,parallel): 0.34383678436279297 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 111038464, before: 1700593664, after: 1811632128 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.6023445, rank: 7, write(sync,parallel): 0.35721755027770996 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109953024, before: 1708265472, after: 1818218496 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.39s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.615113, rank: 15, write(sync,parallel): 0.38002848625183105 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.39s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.6182857, rank: 4, write(sync,parallel): 0.37221503257751465 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.41s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 112271360, before: 1708265472, after: 1820536832 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.41s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110063616, before: 1700585472, after: 1810649088 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.6312046, rank: 14, write(sync,parallel): 0.3925209045410156 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.632325, rank: 12, write(sync,parallel): 0.39579176902770996 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.41s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.640664, rank: 3, write(sync,parallel): 0.3729135990142822 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.44s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.43s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.647614, rank: 6, write(sync,parallel): 0.38133716583251953 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 158949376, before: 1738989568, after: 1897938944 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.45s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.45s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.6629586, rank: 9, write(sync,parallel): 0.4114072322845459 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.6674993, rank: 13, write(sync,parallel): 0.41269683837890625 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.45s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 162324480, before: 1738989568, after: 1901314048 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.48s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 162091008, before: 2008236032, after: 2170327040 +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:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.724965, rank: 8, write(sync,parallel): 0.40958595275878906 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 158437376, before: 2008236032, after: 2166673408 +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:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750540687.771154, rank: 0, write(sync,parallel): 0.4352750778198242 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.51s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8158782, 7, gather: 0.18332433700561523 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8159595, 9, gather: 0.11860132217407227 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8159926, 6, gather: 0.1344921588897705 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8159926, 4, gather: 0.1668229103088379 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8161678, 10, gather: 0.19211101531982422 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8160748, 3, gather: 0.1255509853363037 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8162053, 11, gather: 0.17675995826721191 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.816087, 5, gather: 0.19779324531555176 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.816341, 12, gather: 0.15399169921875 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8162534, 2, gather: 0.21779084205627441 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.816435, 15, gather: 0.17068219184875488 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8163273, 1, gather: 0.19930386543273926 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.816444, 13, gather: 0.11200404167175293 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.818716, 0, gather: 0.005397796630859375 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.816528, 14, gather: 0.15584707260131836 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8167243, 8, gather: 0.05571436882019043 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540687.8495052, metadata_write: 0.030664443969726562 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0394s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2030s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1624s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2352s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2339s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2194s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1541s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2275s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1704s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2537s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0903s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2121s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2056s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1466s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1893s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1907s + successfully saved checkpoint from iteration 10 to gpt-checkpoint [ t 1/8, p 1/1 ] +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.0046956539154052734 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.004681110382080078 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.004699230194091797 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.004683256149291992 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.00467371940612793 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.004673957824707031 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.00467371940612793 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.004681587219238281 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.0046710968017578125 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.004567623138427734 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.0046825408935546875 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.0046977996826171875 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.00467371940612793 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.00468754768371582 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.0046961307525634766 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.0046541690826416016 to finalize ckpt save +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +Evaluating on 1 samples +Evaluating iter 1/1 +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask 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32768]) +batch tensor after cp: batch tensor after cp:position_ids position_ids torch.Size([4, 16384]) +torch.Size([4, 16384]) +Start exporting trace 10 +Done exporting trace 10 +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +(min, max) time across ranks (ms): + evaluate .......................................: (2692.33, 2692.82) +---------------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on validation set | lm loss value: 1.358179E+01 | lm loss PPL: 7.915852E+05 | +---------------------------------------------------------------------------------------------------------------- +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +Evaluating on 1 samples +Evaluating iter 1/1 +batch tensor: tokens torch.Size([4, 32768]) 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+batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 16384, 32768]) +batch tensor after cp: position_ids torch.Size([4, 16384]) +Start exporting trace 11 +Done exporting trace 11 +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +(min, max) time across ranks (ms): + evaluate .......................................: (630.64, 631.10) +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +---------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on test set | lm loss value: 1.358179E+01 | lm loss PPL: 7.915852E+05 | +---------------------------------------------------------------------------------------------------------- +Running ctx_length=12288, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=4 +Cleaning up checkpoint directory: gpt-checkpoint +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 12288 +TP_SIZE: 8 +CP_SIZE: 2 +CHECKPOINT_PATH: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 12288 +TP_SIZE: 8 +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +CP_SIZE: 2 +CHECKPOINT_PATH: gpt-checkpoint +-------------------------------- +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- 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