diff --git "a/attnserver.run_attnserver.slurm.sh.343220.out.log" "b/attnserver.run_attnserver.slurm.sh.343220.out.log" new file mode 100644--- /dev/null +++ "b/attnserver.run_attnserver.slurm.sh.343220.out.log" @@ -0,0 +1,13016 @@ +Running ctx_length=1024, TP_SIZE=4, CP_SIZE=4, BATCH_SIZE=2 +Cleaning up checkpoint directory: gpt-checkpoint +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +using world size: 16, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 4, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 1024 + encoder_tensor_model_parallel_size .............. 0 + end_weight_decay ................................ 0.1 + eod_mask_loss ................................... False + error_injection_rate ............................ 0 + error_injection_type ............................ transient_error + eval_interval ................................... 16 + eval_iters ...................................... 1 + evidence_data_path .............................. None + exit_duration_in_mins ........................... None + exit_interval ................................... None + exit_on_missing_checkpoint ...................... False + exit_signal_handler ............................. False + exp_avg_dtype ................................... torch.float32 + exp_avg_sq_dtype ................................ torch.float32 + expert_model_parallel_size ...................... 1 + expert_tensor_parallel_size ..................... 4 + external_cuda_graph ............................. False + ffn_hidden_size ................................. 16384 + finetune ........................................ False + first_last_layers_bf16 .......................... False + flash_decode .................................... False + fp16 ............................................ True + fp16_lm_cross_entropy ........................... False + fp32_residual_connection ........................ False + fp8 ............................................. None + fp8_amax_compute_algo ........................... most_recent + fp8_amax_history_len ............................ 1 + fp8_interval .................................... 1 + fp8_margin ...................................... 0 + fp8_param_gather ................................ False + fp8_recipe ...................................... delayed + fp8_wgrad ....................................... True + fsdp_double_buffer .............................. False + global_batch_size ............................... 1 + grad_reduce_in_bf16 ............................. False + gradient_accumulation_fusion .................... True + gradient_reduce_div_fusion ...................... True + group_query_attention ........................... True + head_lr_mult .................................... 1.0 + heterogeneous_layers_config_encoded_json ........ None + heterogeneous_layers_config_path ................ None + hidden_dropout .................................. 0.1 + hidden_size ..................................... 4096 + hierarchical_context_parallel_sizes ............. None + high_priority_stream_groups ..................... [] + hybrid_attention_ratio .......................... 0.0 + hybrid_mlp_ratio ................................ 0.0 + hybrid_override_pattern ......................... None + hysteresis ...................................... 2 + ict_head_size ................................... None + ict_load ........................................ None + img_h ........................................... 224 + img_w ........................................... 224 + indexer_batch_size .............................. 128 + indexer_log_interval ............................ 1000 + inference_batch_times_seqlen_threshold .......... -1 + inference_dynamic_batching ...................... False + inference_dynamic_batching_buffer_guaranteed_fraction 0.2 + inference_dynamic_batching_buffer_overflow_factor None + inference_dynamic_batching_buffer_size_gb ....... 40.0 + inference_dynamic_batching_chunk_size ........... 256 + inference_dynamic_batching_max_requests_override None + inference_dynamic_batching_max_tokens_override .. None + inference_max_batch_size ........................ 8 + inference_max_seq_length ........................ 2560 + inference_rng_tracker ........................... False + init_method_std ................................. 0.02 + init_method_xavier_uniform ...................... False + init_model_with_meta_device ..................... False + initial_loss_scale .............................. 4294967296 + inprocess_active_world_size ..................... 16 + inprocess_barrier_timeout ....................... 120 + inprocess_completion_timeout .................... 120 + inprocess_empty_cuda_cache ...................... False + inprocess_granularity ........................... node + inprocess_hard_timeout .......................... 90 + inprocess_heartbeat_interval .................... 30 + inprocess_heartbeat_timeout ..................... 60 + inprocess_last_call_wait ........................ 1 + inprocess_max_iterations ........................ None + inprocess_monitor_process_interval .............. 1.0 + inprocess_monitor_thread_interval ............... 1.0 + inprocess_progress_watchdog_interval ............ 1.0 + inprocess_restart ............................... False + inprocess_soft_timeout .......................... 60 + inprocess_termination_grace_time ................ 1 + is_hybrid_model ................................. False + iter_per_epoch .................................. 1250 + iterations_to_skip .............................. [] + keep_fp8_transpose_cache_when_using_custom_fsdp . False + kv_channels ..................................... 64 + kv_lora_rank .................................... 32 + lazy_mpu_init ................................... None + load ............................................ gpt-checkpoint + load_model_opt_format ........................... False + local_rank ...................................... 0 + log_interval .................................... 1 + log_loss_scale_to_tensorboard ................... True + log_memory_to_tensorboard ....................... False + log_num_zeros_in_grad ........................... False + log_params_norm ................................. False + log_progress .................................... False + log_straggler ................................... False + log_throughput .................................. False + log_timers_to_tensorboard ....................... False + log_validation_ppl_to_tensorboard ............... False + log_world_size_to_tensorboard ................... False + logging_level ................................... 0 + loss_scale ...................................... None + loss_scale_window ............................... 1000 + lr .............................................. 0.0005 + lr_decay_iters .................................. 150000 + lr_decay_samples ................................ None + lr_decay_style .................................. cosine + lr_warmup_fraction .............................. None + lr_warmup_init .................................. 0.0 + lr_warmup_iters ................................. 2 + lr_warmup_samples ............................... 0 + lr_wsd_decay_iters .............................. None + lr_wsd_decay_samples ............................ None + lr_wsd_decay_style .............................. exponential + main_grads_dtype ................................ torch.float32 + main_params_dtype ............................... torch.float32 + make_vocab_size_divisible_by .................... 128 + mamba_head_dim .................................. 64 + mamba_num_groups ................................ 8 + mamba_num_heads ................................. None + mamba_state_dim ................................. 128 + manual_gc ....................................... False + manual_gc_eval .................................. True + manual_gc_interval .............................. 0 + mask_factor ..................................... 1.0 + mask_prob ....................................... 0.15 + mask_type ....................................... random + masked_softmax_fusion ........................... True + max_position_embeddings ......................... 1024 + max_tokens_to_oom ............................... 12000 + memory_snapshot_path ............................ snapshot.pickle + merge_file ...................................... merges.txt + micro_batch_size ................................ 1 + microbatch_group_size_per_vp_stage .............. None + mid_level_dataset_surplus ....................... 0.005 + min_loss_scale .................................. 1.0 + min_lr .......................................... 0.0 + mlp_chunks_for_prefill .......................... 1 + mmap_bin_files .................................. True + mock_data ....................................... True + moe_apply_probs_on_input ........................ False + moe_aux_loss_coeff .............................. 0.0 + moe_enable_deepep ............................... False + moe_expert_capacity_factor ...................... None + moe_extended_tp ................................. False + moe_ffn_hidden_size ............................. None + moe_grouped_gemm ................................ False + moe_input_jitter_eps ............................ None + moe_layer_freq .................................. 1 + moe_layer_recompute ............................. False + moe_pad_expert_input_to_capacity ................ False + moe_per_layer_logging ........................... False + moe_permute_fusion .............................. False + moe_router_bias_update_rate ..................... 0.001 + moe_router_dtype ................................ None + moe_router_enable_expert_bias ................... False + moe_router_force_load_balancing ................. False + moe_router_group_topk ........................... None + moe_router_load_balancing_type .................. aux_loss + moe_router_num_groups ........................... None + moe_router_padding_for_fp8 ...................... False + moe_router_pre_softmax .......................... False + moe_router_score_function ....................... softmax + moe_router_topk ................................. 2 + moe_router_topk_scaling_factor .................. None + moe_shared_expert_intermediate_size ............. None + moe_shared_expert_overlap ....................... False + moe_token_dispatcher_type ....................... allgather + moe_token_drop_policy ........................... probs + moe_use_legacy_grouped_gemm ..................... False + moe_use_upcycling ............................... False + moe_z_loss_coeff ................................ None + mrope_section ................................... None + mscale .......................................... 1.0 + mscale_all_dim .................................. 1.0 + mtp_loss_scaling_factor ......................... 0.1 + mtp_num_layers .................................. None + multi_latent_attention .......................... False + nccl_all_reduce_for_prefill ..................... False + nccl_communicator_config_path ................... None + nccl_ub ......................................... False + no_load_optim ................................... None + no_load_rng ..................................... None + no_persist_layer_norm ........................... False + no_rope_freq .................................... None + no_save_optim ................................... None + no_save_rng ..................................... None + non_persistent_ckpt_type ........................ None + non_persistent_global_ckpt_dir .................. None + non_persistent_local_ckpt_algo .................. fully_parallel + non_persistent_local_ckpt_dir ................... None + non_persistent_save_interval .................... None + norm_epsilon .................................... 1e-05 + normalization ................................... LayerNorm + num_attention_heads ............................. 64 + num_channels .................................... 3 + num_classes ..................................... 1000 + num_dataset_builder_threads ..................... 1 + num_distributed_optimizer_instances ............. 1 + num_experts ..................................... None + num_layers ...................................... 2 + num_layers_at_end_in_bf16 ....................... 1 + num_layers_at_start_in_bf16 ..................... 1 + num_layers_per_virtual_pipeline_stage ........... None + num_query_groups ................................ 16 + num_virtual_stages_per_pipeline_rank ............ None + num_workers ..................................... 2 + object_storage_cache_path ....................... None + one_logger_async ................................ False + one_logger_project .............................. megatron-lm + one_logger_run_name ............................. None + onnx_safe ....................................... None + openai_gelu ..................................... False + optimizer ....................................... adam + optimizer_cpu_offload ........................... False + optimizer_offload_fraction ...................... 1.0 + output_bert_embeddings .......................... False + overlap_cpu_optimizer_d2h_h2d ................... False + overlap_grad_reduce ............................. False + overlap_p2p_comm ................................ False + overlap_p2p_comm_warmup_flush ................... False + overlap_param_gather ............................ False + overlap_param_gather_with_optimizer_step ........ False + override_opt_param_scheduler .................... False + params_dtype .................................... torch.float16 + patch_dim ....................................... 16 + per_split_data_args_path ........................ None + perform_initialization .......................... True + pin_cpu_grads ................................... True + pin_cpu_params .................................. True + pipeline_model_parallel_comm_backend ............ None + pipeline_model_parallel_size .................... 1 + pipeline_model_parallel_split_rank .............. None + position_embedding_type ......................... learned_absolute + pretrained_checkpoint ........................... None + profile ......................................... False + profile_ranks ................................... [0] + profile_step_end ................................ 12 + profile_step_start .............................. 10 + q_lora_rank ..................................... None + qk_head_dim ..................................... 128 + qk_l2_norm ...................................... False + qk_layernorm .................................... False + qk_pos_emb_head_dim ............................. 64 + query_in_block_prob ............................. 0.1 + rampup_batch_size ............................... None + rank ............................................ 0 + recompute_granularity ........................... None + recompute_method ................................ None + recompute_modules ............................... None + recompute_num_layers ............................ None + record_memory_history ........................... False + relative_attention_max_distance ................. 128 + relative_attention_num_buckets .................. 32 + replication ..................................... False + replication_factor .............................. 2 + replication_jump ................................ None + rerun_mode ...................................... disabled + reset_attention_mask ............................ False + reset_position_ids .............................. False + result_rejected_tracker_filename ................ None + retriever_report_topk_accuracies ................ [] + retriever_score_scaling ......................... False + retriever_seq_length ............................ 256 + retro_add_retriever ............................. False + retro_attention_gate ............................ 1 + retro_cyclic_train_iters ........................ None + retro_encoder_attention_dropout ................. 0.1 + retro_encoder_hidden_dropout .................... 0.1 + retro_encoder_layers ............................ 2 + retro_num_neighbors ............................. 2 + retro_num_retrieved_chunks ...................... 2 + retro_project_dir ............................... None + retro_verify_neighbor_count ..................... True + rope_scaling_factor ............................. 8.0 + rotary_base ..................................... 10000 + rotary_interleaved .............................. False + rotary_percent .................................. 1.0 + rotary_scaling_factor ........................... 1.0 + rotary_seq_len_interpolation_factor ............. None + run_workload_inspector_server ................... False + sample_rate ..................................... 1.0 + save ............................................ gpt-checkpoint + save_interval ................................... 16 + scatter_gather_tensors_in_pipeline .............. True + seed ............................................ 1234 + seq_length ...................................... 1024 + sequence_parallel ............................... False + sgd_momentum .................................... 0.9 + short_seq_prob .................................. 0.1 + skip_train ...................................... False + skipped_train_samples ........................... 0 + spec ............................................ None + split ........................................... None + squared_relu .................................... False + start_weight_decay .............................. 0.1 + straggler_ctrlr_port ............................ 65535 + straggler_minmax_count .......................... 1 + suggested_communication_unit_size ............... None + swiglu .......................................... False + swin_backbone_type .............................. tiny + symmetric_ar_type ............................... None + te_rng_tracker .................................. False + tensor_model_parallel_size ...................... 4 + 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 + > padded vocab (size: 50257) with 431 dummy tokens (new size: 50688) +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 +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 +> initialized tensor model parallel with size 4 +> initialized pipeline model parallel with size 1 +> setting random seeds to 1234 ... +> compiling dataset index builder ... +make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +make: Nothing to be done for 'default'. +make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +>>> done with dataset index builder. Compilation time: 0.045 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.426 seconds +time to initialize megatron (seconds): 8.747 +[after megatron is initialized] datetime: 2025-06-21 21:24:55 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 144247808 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 144247808 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 144247808 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 144247808 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 144247808 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 144247808 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 144247808 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 144247808 +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 (144247808 elements, 144247808 padded size): + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.embedding.word_embeddings.weight + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.embedding.position_embeddings.weight + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.decoder.final_layernorm.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 +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 +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 144247808 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 144247808 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 144247808 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> embedding>>> decoder + +>>> output_layer +>>> decoder +>>> output_layer +>>> embedding + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 144247808>>> decoder + +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 144247808 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 144247808 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 144247808 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 144247808 +WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (3.36, 3.75) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:24:55 +> building train, validation, and test datasets ... + > datasets target sizes (minimum size): + train: 10 + validation: 1 + test: 1 +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)] +> building train, validation, and test datasets for GPT ... +INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=1024, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=, 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: 0.006436 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 66592 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.003645 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 66562 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.003430 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 66686 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 21:24:56 +done with setup ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (721.87, 752.74) + train/valid/test-data-iterators-setup ..........: (21.71, 142.68) +training ... +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 21:24:56 +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: labels batch tensor:torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) + batch tensor:tokens loss_mask torch.Size([2, 2048]) +batch tensor: attention_masktorch.Size([2, 2048]) +torch.Size([2, 1, 2048, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor:batch tensor: batch tensor:position_ids labelstorch.Size([2, 2048]) + torch.Size([2, 2048]) +tokens batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +torch.Size([2, 2048])batch tensor: +position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor:batch tensor: attention_mask tokenstorch.Size([2, 1, 2048, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +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: 0.1400 +Theoretical memory footprints: weight and optimizer=2403.18 MB + [2025-06-21 21:25:12] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 16654.1 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[Rank 3] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2144.0 | max reserved: 2144.0 +[Rank 11] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2162.0 | max reserved: 2162.0 +[Rank 14] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2160.0 | max reserved: 2160.0 +[Rank 1] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2144.0 | max reserved: 2144.0 +[Rank 7] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2164.0 | max reserved: 2164.0 +[Rank 0] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2144.0 | max reserved: 2144.0[Rank 6] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2164.0 | max reserved: 2164.0 + +[Rank 12] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2160.0 | max reserved: 2160.0 +[Rank 8] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2162.0 | max reserved: 2162.0 +[Rank 9] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2162.0 | max reserved: 2162.0[Rank 13] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2160.0 | max reserved: 2160.0 + +[Rank 4] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2104.0 | max reserved: 2104.0 +[Rank 15] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2160.0 | max reserved: 2160.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2144.0 | max reserved: 2144.0 +[Rank 10] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2162.0 | max reserved: 2162.0 +[Rank 5] (after 1 iterations) memory (MB) | allocated: 1810.79736328125 | max allocated: 1810.79833984375 | reserved: 2164.0 | max reserved: 2164.0 +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:batch tensor: tokens tokens torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor:batch tensor: labels labelstorch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: + batch tensor:loss_mask loss_mask torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: attention_mask attention_masktorch.Size([2, 1, 2048, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +torch.Size([2, 1, 2048, 2048])batch tensor: + position_idsbatch tensor: torch.Size([2, 2048])position_ids + torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp:batch tensor: tokens tokenstorch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 512])torch.Size([2, 2048]) + +batch tensor after cp: loss_maskbatch tensor: torch.Size([2, 512])labels +batch tensor: labels torch.Size([2, 2048]) + batch tensor after cp:torch.Size([2, 2048]) +attention_maskbatch tensor: loss_masktorch.Size([2, 1, 512, 2048]) +torch.Size([2, 2048])batch tensor after cp: +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + position_idsbatch tensor: torch.Size([2, 512])attention_mask + torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor:batch tensor after cp:batch tensor after cp: loss_mask tokens tokens torch.Size([2, 512]) +batch tensor after cp:torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +attention_masktorch.Size([2, 2048])batch tensor after cp: torch.Size([2, 1, 512, 2048]) +labels + batch tensor:batch tensor after cp:torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +labelsposition_ids batch tensor after cp:torch.Size([2, 2048]) + batch tensor:torch.Size([2, 512])loss_mask +batch tensor: tokens torch.Size([2, 2048]) +loss_mask torch.Size([2, 512])torch.Size([2, 2048]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: batch tensor:attention_mask attention_masktorch.Size([2, 1, 512, 2048]) torch.Size([2, 1, 2048, 2048]) + +batch tensor after cp: position_idsbatch tensor: position_ids torch.Size([2, 2048])torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + +batch tensor:batch tensor after cp: tokenstokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +torch.Size([2, 2048])batch tensor after cp: + loss_mask torch.Size([2, 512])batch tensor: + labelsbatch tensor after cp: attention_masktorch.Size([2, 2048]) +torch.Size([2, 1, 512, 2048])batch tensor: +batch tensor after cp: labels torch.Size([2, 512]) +loss_maskbatch tensor after cp: torch.Size([2, 2048])position_ids + torch.Size([2, 512])batch tensor: +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) + attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_idsbatch tensor after cp: torch.Size([2, 2048])tokens + torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor:batch tensor: tokens tokens torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: torch.Size([2, 2048])labels +torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor:batch tensor: loss_masklabels torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor:batch tensor: loss_maskattention_mask torch.Size([2, 2048]) +torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: batch tensor:attention_mask position_ids torch.Size([2, 1, 2048, 2048])torch.Size([2, 2048]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor: position_ids batch tensor after cp:torch.Size([2, 2048]) + tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +Start exporting trace 1 +Done exporting trace 1 + [2025-06-21 21:25:12] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 74.7 | 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([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:batch tensor: tokenstokens torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor:batch tensor: labelslabels torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor:batch tensor: loss_maskloss_mask torch.Size([2, 2048]) +torch.Size([2, 2048]) +batch tensor: batch tensor:attention_mask attention_mask torch.Size([2, 1, 2048, 2048]) +torch.Size([2, 1, 2048, 2048]) +batch tensor: batch tensor:position_ids position_idstorch.Size([2, 2048]) +torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor:batch tensor: labels torch.Size([2, 2048])tokens +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) + batch tensor: loss_mask torch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: +attention_mask torch.Size([2, 1, 2048, 2048])batch tensor: + labelsbatch tensor: torch.Size([2, 2048])position_ids +batch tensor after cp: position_ids torch.Size([2, 512]) + batch tensor:torch.Size([2, 2048]) +loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels batch tensor after cp:torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +tokensbatch tensor:batch tensor after cp: tokensloss_mask torch.Size([2, 512])torch.Size([2, 2048]) +torch.Size([2, 512]) + +batch tensor after cp: batch tensor:batch tensor after cp:labels labels attention_masktorch.Size([2, 512])torch.Size([2, 512]) + +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +torch.Size([2, 1, 2048, 2048])batch tensor after cp:batch tensor after cp: + loss_maskloss_maskbatch tensor: torch.Size([2, 512])position_ids +torch.Size([2, 512]) batch tensor after cp: +torch.Size([2, 2048]) +attention_maskbatch tensor after cp: attention_masktorch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp:torch.Size([2, 1, 512, 2048]) +position_idsbatch tensor after cp: torch.Size([2, 512])position_ids + torch.Size([2, 512]) +batch tensor after cp: batch tensor:loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens batch tensor:torch.Size([2, 512]) + batch tensor after cp:tokens attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: torch.Size([2, 2048])position_ids +batch tensor after cp: tokenslabels torch.Size([2, 512]) + torch.Size([2, 512])batch tensor: + labels torch.Size([2, 2048]) +batch tensor after cp: torch.Size([2, 2048])loss_maskbatch tensor after cp: + torch.Size([2, 512])tokensbatch tensor: + torch.Size([2, 512])labelsbatch tensor after cp: +batch tensor: loss_mask batch tensor:torch.Size([2, 2048]) + batch tensor after cp:torch.Size([2, 2048]) attention_mask +labels batch tensor:batch tensor:torch.Size([2, 1, 512, 2048])torch.Size([2, 512]) +loss_mask + batch tensor after cp:batch tensor after cp:tokens loss_mask torch.Size([2, 512]) +batch tensor after cp: torch.Size([2, 2048])position_ids + batch tensor:tokens attention_mask torch.Size([2, 1, 2048, 2048]) +torch.Size([2, 2048])batch tensor: +position_ids torch.Size([2, 2048])batch tensor: +batch tensor: torch.Size([2, 2048]) batch tensor: + attention_maskattention_masktorch.Size([2, 512])batch tensor: tokens torch.Size([2, 1, 512, 2048]) + labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) + labels torch.Size([2, 1, 2048, 2048]) +torch.Size([2, 2048]) +batch tensor after cp: +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: batch tensor: +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) + loss_maskposition_idsposition_idsbatch tensor: torch.Size([2, 2048])labelstorch.Size([2, 2048])torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: + +batch tensor: tokens batch tensor after cp: tokens torch.Size([2, 512])torch.Size([2, 2048]) + +torch.Size([2, 2048])attention_mask +torch.Size([2, 1, 2048, 2048])batch tensor: +batch tensor after cp: labels batch tensor:torch.Size([2, 512]) labels + batch tensor after cp:torch.Size([2, 2048]) + batch tensor:loss_mask position_idstorch.Size([2, 2048]) +torch.Size([2, 2048]) +loss_mask batch tensor:torch.Size([2, 512]) loss_mask + batch tensor after cp:torch.Size([2, 2048])batch tensor after cp: +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +attention_masktokensbatch tensor: torch.Size([2, 1, 512, 2048])attention_mask + torch.Size([2, 512])batch tensor after cp:torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp:position_idsbatch tensor: torch.Size([2, 512])labels position_idstorch.Size([2, 512]) + +torch.Size([2, 2048])batch tensor after cp: +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) + loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_maskbatch tensor after cp: torch.Size([2, 1, 512, 2048])tokens + batch tensor after cp: torch.Size([2, 512])position_ids + torch.Size([2, 512])batch tensor after cp: +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) + labels torch.Size([2, 512])batch tensor after cp: + batch tensor after cp:tokens loss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: attention_masklabels torch.Size([2, 512])torch.Size([2, 1, 512, 2048]) + +batch tensor after cp:batch tensor after cp: loss_maskposition_ids torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + [2025-06-21 21:25:12] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 41.8 | 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: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +Start exporting trace 2 +batch tensor: labels torch.Size([2, 2048]) +Done exporting trace 2 +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor:batch tensor: attention_mask torch.Size([2, 1, 2048, 2048])tokens +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048])batch tensor after cp: +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) + batch tensor after cp:tokens position_ids torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:batch tensor: labels torch.Size([2, 2048]) +tokensbatch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: torch.Size([2, 2048])attention_mask +torch.Size([2, 1, 2048, 2048]) +batch tensor:batch tensor: position_idslabels torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: batch tensor after cp:attention_mask tokenstorch.Size([2, 1, 2048, 2048]) +torch.Size([2, 512])batch tensor: +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) + batch tensor after cp: batch tensor after cp:position_idstokens labelstorch.Size([2, 2048])torch.Size([2, 512]) + +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +torch.Size([2, 512])batch tensor after cp: +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) + batch tensor after cp:labels loss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp:batch tensor after cp: loss_maskattention_mask torch.Size([2, 512])torch.Size([2, 1, 512, 2048]) + +batch tensor after cp:batch tensor after cp: attention_maskposition_ids torch.Size([2, 1, 512, 2048])torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048])batch tensor: + +batch tensor after cp: position_ids torch.Size([2, 512]) + batch tensor:tokens labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor:torch.Size([2, 2048]) loss_mask + torch.Size([2, 2048]) +batch tensor:batch tensor: labelsattention_mask torch.Size([2, 2048])torch.Size([2, 1, 2048, 2048]) + +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: batch tensor:loss_mask position_ids torch.Size([2, 2048])torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: + attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) + [2025-06-21 21:25:12] iteration 4/ 10 | consumed samples: 4 | elapsed time per iteration (ms): 42.4 | 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 after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp:batch tensor after cp: position_idstokens torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: batch tensor:position_ids torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) + tokens torch.Size([2, 2048]) +Start exporting trace 3 +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +Done exporting trace 3 +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor:batch tensor: tokens tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +torch.Size([2, 2048])batch tensor: +batch tensor:batch tensor: tokenstokens torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: batch tensor:labels labelstorch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: + labelsbatch tensor: torch.Size([2, 2048])labels + batch tensor:torch.Size([2, 2048]) + loss_maskbatch tensor: torch.Size([2, 2048])loss_mask + torch.Size([2, 2048])batch tensor: + attention_mask batch tensor: torch.Size([2, 1, 2048, 2048])attention_mask + batch tensor:torch.Size([2, 1, 2048, 2048]) +loss_maskbatch tensor: torch.Size([2, 2048])loss_mask +position_idsbatch tensor: torch.Size([2, 2048])position_ids +torch.Size([2, 2048]) + torch.Size([2, 2048])batch tensor: +batch tensor: tokens torch.Size([2, 2048]) + attention_maskbatch tensor: torch.Size([2, 1, 2048, 2048])attention_mask +batch tensor: labels torch.Size([2, 2048]) + batch tensor:torch.Size([2, 1, 2048, 2048]) +position_ids batch tensor:torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([2, 1, 512, 2048])torch.Size([2, 1, 512, 2048]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens batch tensor after cp:torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +tokens batch tensor: torch.Size([2, 512])labels +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) + batch tensor after cp:torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +labels batch tensor:torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +loss_maskbatch tensor after cp: torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:loss_mask attention_masktorch.Size([2, 512]) +batch tensor: labels torch.Size([2, 2048]) +torch.Size([2, 1, 2048, 2048])batch tensor after cp: +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + batch tensor:attention_mask position_idstorch.Size([2, 1, 512, 2048]) +torch.Size([2, 2048])batch tensor after cp: +batch tensor: position_ids torch.Size([2, 2048]) + position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) + [2025-06-21 21:25:12] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 39.7 | 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 after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor:batch tensor: tokenstokens torch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: + labels batch tensor:torch.Size([2, 2048]) +labelsbatch tensor: loss_masktorch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + batch tensor:loss_mask attention_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +torch.Size([2, 1, 2048, 2048])batch tensor: +batch tensor: labels torch.Size([2, 2048]) + attention_maskbatch tensor: torch.Size([2, 1, 2048, 2048])position_ids +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + batch tensor:torch.Size([2, 2048]) +position_ids torch.Size([2, 2048]) +batch tensor after cp:batch tensor: tokensposition_ids torch.Size([2, 512])torch.Size([2, 2048]) + +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512])batch tensor after cp: + batch tensor after cp:tokens loss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: attention_masklabels torch.Size([2, 1, 512, 2048])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: position_idsloss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +Start exporting trace 4 +batch tensor:batch tensor: tokens tokens torch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: +Done exporting trace 4 + labels batch tensor:torch.Size([2, 2048]) +labels batch tensor: torch.Size([2, 2048])loss_mask +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + torch.Size([2, 2048])batch tensor: + loss_maskbatch tensor: torch.Size([2, 2048])attention_mask +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) + batch tensor:torch.Size([2, 1, 2048, 2048]) +attention_mask batch tensor: torch.Size([2, 1, 2048, 2048])position_ids + batch tensor:torch.Size([2, 2048]) +position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokensbatch tensor after cp: torch.Size([2, 512])tokens + batch tensor after cp:torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +labelsbatch tensor:batch tensor after cp: batch tensor:torch.Size([2, 512]) labels +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + batch tensor after cp: tokens tokenstorch.Size([2, 512]) loss_mask +batch tensor: position_ids torch.Size([2, 2048]) + batch tensor after cp:torch.Size([2, 512]) +loss_masktorch.Size([2, 2048])torch.Size([2, 2048]) batch tensor after cp: + +torch.Size([2, 512])attention_maskbatch tensor: +batch tensor: tokens torch.Size([2, 2048]) + batch tensor:batch tensor after cp:labels torch.Size([2, 1, 512, 2048]) labelsattention_mask + torch.Size([2, 2048])torch.Size([2, 2048])batch tensor after cp:torch.Size([2, 1, 512, 2048]) + + +batch tensor:position_ids batch tensor after cp:batch tensor: loss_mask torch.Size([2, 512]) position_ids +batch tensor: labels torch.Size([2, 2048]) +torch.Size([2, 2048]) loss_mask +torch.Size([2, 512]) batch tensor: + torch.Size([2, 2048])attention_mask + torch.Size([2, 1, 2048, 2048])batch tensor: +batch tensor after cp:batch tensor: loss_masktokens torch.Size([2, 2048]) +torch.Size([2, 512])batch tensor: + batch tensor:attention_mask position_ids torch.Size([2, 1, 2048, 2048])torch.Size([2, 2048]) + +batch tensor: position_ids torch.Size([2, 2048]) + attention_maskbatch tensor after cp: labelstorch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:torch.Size([2, 512]) +batch tensor: labels torch.Size([2, 2048]) +position_idsbatch tensor after cp: torch.Size([2, 2048])loss_mask + torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask batch tensor:torch.Size([2, 1, 512, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + batch tensor after cp:tokens batch tensor: position_ids batch tensor after cp:tokenstorch.Size([2, 2048]) torch.Size([2, 512]) +tokens + torch.Size([2, 2048])torch.Size([2, 512]) + +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor:batch tensor after cp:batch tensor: labelslabelslabels torch.Size([2, 512])torch.Size([2, 2048])torch.Size([2, 2048]) +batch tensor after cp: batch tensor after cp:tokens tokenstorch.Size([2, 512]) +torch.Size([2, 512])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([2, 512])labels + + + batch tensor after cp:torch.Size([2, 512]) +loss_maskbatch tensor after cp: torch.Size([2, 512])loss_mask + torch.Size([2, 512])batch tensor after cp: +batch tensor after cp:batch tensor:batch tensor: loss_maskloss_maskloss_mask torch.Size([2, 2048]) +torch.Size([2, 512])torch.Size([2, 2048]) +batch tensor: + batch tensor after cp:attention_mask attention_mask torch.Size([2, 1, 512, 2048]) +torch.Size([2, 1, 512, 2048]) +batch tensor after cp: batch tensor after cp:position_ids position_ids torch.Size([2, 512])torch.Size([2, 512]) + + batch tensor after cp:attention_maskbatch tensor: attention_masktorch.Size([2, 1, 2048, 2048])attention_mask +batch tensor after cp: tokens torch.Size([2, 512]) + batch tensor:torch.Size([2, 1, 512, 2048])torch.Size([2, 1, 2048, 2048]) + +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +position_idsbatch tensor after cp: batch tensor:position_ids torch.Size([2, 2048]) +position_idstorch.Size([2, 512]) +torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512])batch tensor: + tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: batch tensor after cp:labels tokenstorch.Size([2, 512]) +batch tensor after cp:torch.Size([2, 512]) loss_mask + batch tensor after cp:torch.Size([2, 512]) +labelsbatch tensor after cp: torch.Size([2, 512])attention_mask + batch tensor after cp:torch.Size([2, 1, 512, 2048]) +loss_maskbatch tensor after cp: torch.Size([2, 512])position_ids + batch tensor after cp:torch.Size([2, 512]) +attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +Start exporting trace 5 +Done exporting trace 5 + [2025-06-21 21:25:12] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 41.1 | 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([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor:batch tensor: tokenstokens torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: labelslabels torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:batch tensor: attention_maskattention_mask torch.Size([2, 1, 2048, 2048])torch.Size([2, 1, 2048, 2048]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp:batch tensor after cp: labelstokens torch.Size([2, 512]) +torch.Size([2, 512])batch tensor after cp: +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + batch tensor after cp:loss_mask labelstorch.Size([2, 512]) +torch.Size([2, 512]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: batch tensor after cp:attention_mask loss_mask torch.Size([2, 1, 512, 2048])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: position_idsattention_mask torch.Size([2, 512])torch.Size([2, 1, 512, 2048]) + +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp:batch tensor: position_ids torch.Size([2, 512]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +tokens torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:batch tensor: tokenstokens torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: labelslabels torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor:batch tensor: attention_maskattention_mask torch.Size([2, 1, 2048, 2048])torch.Size([2, 1, 2048, 2048]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: position_idsbatch tensor: torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) + tokens torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048])batch tensor after cp: +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + batch tensor:tokens loss_masktorch.Size([2, 512]) +torch.Size([2, 2048])batch tensor after cp: + labelsbatch tensor: torch.Size([2, 512])attention_mask +batch tensor: position_ids torch.Size([2, 2048]) + batch tensor after cp:torch.Size([2, 1, 2048, 2048]) +loss_mask batch tensor:torch.Size([2, 512]) +position_ids batch tensor after cp:torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor:batch tensor after cp: tokenstokens torch.Size([2, 512]) +batch tensor after cp: torch.Size([2, 2048])labels +torch.Size([2, 512]) +batch tensor:batch tensor after cp: labelsloss_mask torch.Size([2, 2048])torch.Size([2, 512]) + +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp:batch tensor: batch tensor after cp:tokens torch.Size([2, 512])tokens +tokens batch tensor after cp: torch.Size([2, 512])labels +torch.Size([2, 512]) +batch tensor: batch tensor after cp:loss_mask attention_masktorch.Size([2, 2048])batch tensor after cp: + batch tensor:tokenstorch.Size([2, 1, 512, 2048]) attention_mask + batch tensor after cp:torch.Size([2, 1, 2048, 2048])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: torch.Size([2, 2048]) +loss_masklabels batch tensor: torch.Size([2, 512]) torch.Size([2, 512])labels + + batch tensor after cp:batch tensor after cp: torch.Size([2, 2048]) attention_mask +loss_mask batch tensor: torch.Size([2, 1, 512, 2048])torch.Size([2, 512]) +loss_maskbatch tensor after cp: batch tensor after cp: + batch tensor after cp: attention_mask position_ids torch.Size([2, 512]) +batch tensor:batch tensor after cp:position_ids position_idstorch.Size([2, 512])labels + torch.Size([2, 2048])torch.Size([2, 512]) +torch.Size([2, 2048]) +batch tensor: attention_masktokens torch.Size([2, 1, 512, 2048]) torch.Size([2, 512]) + +torch.Size([2, 1, 2048, 2048]) + +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp:batch tensor after cp: batch tensor:position_idslabels position_idstorch.Size([2, 512])torch.Size([2, 512]) + +torch.Size([2, 2048])batch tensor after cp: +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) + loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +Start exporting trace 6 +Done exporting trace 6 + [2025-06-21 21:25:12] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 38.6 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 67108864.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask batch tensor:torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: attention_maskbatch tensor:tokens tokenstorch.Size([2, 1, 512, 2048]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: torch.Size([2, 2048])torch.Size([2, 2048])position_ids + + torch.Size([2, 512])batch tensor:batch tensor: + labelslabels torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([2, 1, 2048, 2048])torch.Size([2, 1, 2048, 2048]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labelsbatch tensor: torch.Size([2, 2048]) + batch tensor:tokens loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: torch.Size([2, 2048])attention_mask +torch.Size([2, 1, 2048, 2048]) +batch tensor: batch tensor:labels position_idstorch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) + loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: batch tensor:labels torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) + batch tensor after cp:tokens loss_mask torch.Size([2, 512]) +torch.Size([2, 2048])batch tensor after cp: +batch tensor: labels torch.Size([2, 2048]) +attention_mask batch tensor:torch.Size([2, 1, 512, 2048]) +batch tensor after cp:labelsbatch tensor after cp: tokenstorch.Size([2, 2048]) position_ids + torch.Size([2, 512])batch tensor:torch.Size([2, 512]) + +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp:loss_mask labels torch.Size([2, 2048]) +torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor:batch tensor after cp: attention_maskloss_mask torch.Size([2, 512])torch.Size([2, 1, 2048, 2048]) + +batch tensor after cp:batch tensor: attention_maskposition_ids torch.Size([2, 1, 512, 2048])torch.Size([2, 2048]) + +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: batch tensor after cp:attention_mask attention_mask torch.Size([2, 1, 512, 2048]) +torch.Size([2, 1, 512, 2048])batch tensor after cp: + batch tensor after cp:position_ids position_idstorch.Size([2, 512]) +torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512])batch tensor after cp: +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) + batch tensor after cp:tokens loss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: labelsattention_mask torch.Size([2, 512])torch.Size([2, 1, 512, 2048]) + +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp:batch tensor after cp: loss_maskposition_ids torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +Start exporting trace 7 +Done exporting trace 7 + [2025-06-21 21:25:13] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 39.9 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 33554432.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids batch tensor:torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) + tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: tokensbatch tensor: tokens torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +torch.Size([2, 2048])batch tensor: +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) + labels batch tensor: torch.Size([2, 2048])labels + batch tensor:torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +loss_maskbatch tensor: torch.Size([2, 2048])loss_mask +batch tensor after cp: tokens torch.Size([2, 512]) + batch tensor:torch.Size([2, 2048]) +attention_mask batch tensor:torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +attention_maskbatch tensor: position_idstorch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: + batch tensor:position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: batch tensor:loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:tokens attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor:torch.Size([2, 2048]) position_ids + torch.Size([2, 2048])batch tensor: +batch tensor: loss_mask torch.Size([2, 2048]) + labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_maskbatch tensor after cp: torch.Size([2, 1, 2048, 2048])tokens + batch tensor: torch.Size([2, 512])position_ids + batch tensor after cp:torch.Size([2, 2048]) +labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([2, 1, 512, 2048])torch.Size([2, 1, 512, 2048]) + +batch tensor after cp: loss_mask batch tensor:torch.Size([2, 512]) +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512])batch tensor after cp: + batch tensor after cp:tokens loss_mask torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp:batch tensor after cp: labelsattention_mask torch.Size([2, 512])torch.Size([2, 1, 512, 2048]) + +batch tensor after cp:batch tensor after cp: loss_maskposition_ids torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) + batch tensor after cp:tokens attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp:torch.Size([2, 2048]) position_ids +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) + torch.Size([2, 512]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: batch tensor after cp:position_ids torch.Size([2, 2048])tokens +torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp:batch tensor: attention_mask tokenstorch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512])torch.Size([2, 2048]) +batch tensor:batch tensor after cp: labels tokenstorch.Size([2, 512]) + +batch tensor: labels torch.Size([2, 2048]) +batch tensor after cp: loss_masktorch.Size([2, 2048]) +torch.Size([2, 512])batch tensor: + labels batch tensor after cp:torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +attention_maskbatch tensor: loss_masktorch.Size([2, 1, 512, 2048]) +torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp:batch tensor: position_idsattention_mask torch.Size([2, 512])torch.Size([2, 1, 2048, 2048]) + +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +Start exporting trace 8 +batch tensor after cp: position_ids torch.Size([2, 512]) +Done exporting trace 8 +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor:batch tensor: tokenstokens torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor:batch tensor: labels labels torch.Size([2, 2048]) +torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor:batch tensor: loss_maskloss_mask torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([2, 1, 2048, 2048])torch.Size([2, 1, 2048, 2048]) + +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor:batch tensor: position_idsposition_ids torch.Size([2, 2048])torch.Size([2, 2048]) + +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor:batch tensor: batch tensor after cp: tokens tokens tokens torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp:torch.Size([2, 2048])torch.Size([2, 2048]) + +labels batch tensor:batch tensor: torch.Size([2, 512])labelslabels +batch tensor after cp: position_ids torch.Size([2, 512]) + batch tensor after cp: torch.Size([2, 2048])torch.Size([2, 2048])loss_mask + + batch tensor:batch tensor: torch.Size([2, 512]) loss_maskloss_mask + [2025-06-21 21:25:13] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 39.4 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 16777216.0 | number of skipped iterations: 1 | number of nan iterations: 0 | + torch.Size([2, 2048])batch tensor after cp:torch.Size([2, 2048]) + +attention_mask batch tensor:batch tensor: torch.Size([2, 1, 512, 2048]) attention_mask +batch tensor: tokens torch.Size([2, 2048]) +batch tensor:batch tensor: labels torch.Size([2, 2048])tokens + batch tensor: loss_mask torch.Size([2, 2048]) +torch.Size([2, 2048])batch tensor: + attention_mask batch tensor: torch.Size([2, 1, 2048, 2048])labels +attention_maskbatch tensor: batch tensor after cp: torch.Size([2, 1, 2048, 2048])position_idstorch.Size([2, 1, 2048, 2048]) + batch tensor:torch.Size([2, 2048]) +position_idsbatch tensor: torch.Size([2, 2048])loss_mask + torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) + +batch tensor:tokenstorch.Size([2, 512])batch tensor: +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) + position_idsposition_ids torch.Size([2, 2048])torch.Size([2, 2048]) + +torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: tokensbatch tensor: batch tensor after cp: batch tensor after cp:torch.Size([2, 2048])tokenstokens +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) + tokensbatch tensor:torch.Size([2, 512]) torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +torch.Size([2, 512])labelsbatch tensor: batch tensor after cp:torch.Size([2, 2048]) + + batch tensor after cp: +tokensbatch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens batch tensor after cp:torch.Size([2, 512]) +labelsbatch tensor: labels torch.Size([2, 512])torch.Size([2, 512])labels + + torch.Size([2, 2048])batch tensor after cp:batch tensor after cp:torch.Size([2, 2048]) + +tokensbatch tensor after cp: torch.Size([2, 512])labels + batch tensor after cp:torch.Size([2, 512]) +labelsbatch tensor after cp: torch.Size([2, 512])loss_mask + loss_maskloss_mask batch tensor:batch tensor: batch tensor:torch.Size([2, 512])torch.Size([2, 512]) loss_masktokens torch.Size([2, 2048])labels + + + torch.Size([2, 512])batch tensor after cp: +batch tensor after cp:batch tensor after cp:torch.Size([2, 2048]) torch.Size([2, 2048]) attention_maskbatch tensor: + +attention_mask attention_maskbatch tensor: torch.Size([2, 1, 512, 2048]) + batch tensor:loss_maskbatch tensor after cp: attention_mask torch.Size([2, 512]) + tokensbatch tensor after cp:torch.Size([2, 1, 512, 2048]) + batch tensor:torch.Size([2, 1, 512, 2048]) loss_maskbatch tensor after cp:torch.Size([2, 1, 2048, 2048]) +labelsbatch tensor after cp: + position_idstorch.Size([2, 2048])position_idstorch.Size([2, 2048]) batch tensor:torch.Size([2, 512]) position_ids +attention_maskbatch tensor after cp: torch.Size([2, 1, 512, 2048])position_idstorch.Size([2, 2048]) + +torch.Size([2, 512])batch tensor after cp: +torch.Size([2, 512]) + + + batch tensor:batch tensor: torch.Size([2, 2048])loss_maskattention_mask + batch tensor:position_ids labelstorch.Size([2, 512]) +torch.Size([2, 2048]) + torch.Size([2, 2048])torch.Size([2, 1, 2048, 2048]) + +batch tensor:batch tensor: attention_maskposition_ids torch.Size([2, 1, 2048, 2048])torch.Size([2, 2048]) + +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) + batch tensor after cp:tokens attention_mask torch.Size([2, 512])torch.Size([2, 1, 512, 2048]) + +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp:batch tensor after cp: labelsposition_ids torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: batch tensor after cp:attention_mask tokenstorch.Size([2, 1, 512, 2048]) +batch tensor after cp:torch.Size([2, 512]) +position_ids batch tensor after cp:batch tensor after cp:torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) + tokenslabels torch.Size([2, 512])torch.Size([2, 512]) + +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: batch tensor after cp:loss_mask labelstorch.Size([2, 512]) +torch.Size([2, 512])batch tensor after cp: +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) + batch tensor after cp:attention_mask loss_masktorch.Size([2, 1, 512, 2048]) +torch.Size([2, 512])batch tensor after cp: + position_idsbatch tensor after cp: torch.Size([2, 512])attention_mask +batch tensor: tokens torch.Size([2, 2048]) + torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 2048, 2048]) +batch tensor: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: tokens torch.Size([2, 512]) +batch tensor after cp: labels torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: loss_mask torch.Size([2, 512]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 512, 2048]) +batch tensor after cp: position_ids torch.Size([2, 512]) +batch tensor after cp: position_ids torch.Size([2, 512]) +Start exporting trace 9 + [2025-06-21 21:25:13] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 39.2 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 8388608.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +Done exporting trace 9 +[after training is done] datetime: 2025-06-21 21:25:13 +saving checkpoint at iteration 10 to gpt-checkpoint in torch_dist format +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.027734041213989258 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.027746200561523438 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.02782130241394043 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.027846097946166992 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.02797722816467285 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.027994155883789062 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.02918410301208496 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.030463218688964844 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.030538082122802734 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.03054666519165039 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.030524730682373047 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.030597925186157227 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.030668973922729492 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.031094789505004883 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.037865400314331055 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.3192934989929199 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.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +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(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +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(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +Running ctx_length=2048, TP_SIZE=4, CP_SIZE=4, BATCH_SIZE=2 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 2048 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 2048 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written. +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +using world size: 16, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 4, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 2048 + 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 ..................... 4 + 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 ......................... 2048 + 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 ...................................... 2048 + 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 ...................... 4 + 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 431 dummy tokens (new size: 50688) +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 +> initialized tensor model parallel with size 4 +> initialized pipeline model parallel with size 1 +> setting random seeds to 1234 ... +> compiling dataset index builder ... +make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +make: Nothing to be done for 'default'. +make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +>>> done with dataset index builder. Compilation time: 0.042 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.465 seconds +time to initialize megatron (seconds): 8.975 +[after megatron is initialized] datetime: 2025-06-21 21:26:25 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> embedding +>>> decoder +>>> output_layer +>>> embedding>>> decoder + +>>> output_layer +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 148442112 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 148442112>>> output_layer + + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 148442112 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 148442112 + +>>> embedding>>> decoder + +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 148442112 +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 148442112 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 148442112 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 148442112 +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 (148442112 elements, 148442112 padded size): + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.self_attention.linear_qkv.weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.embedding.position_embeddings.weight + module.decoder.final_layernorm.weight + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.embedding.word_embeddings.weight + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight +INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=, config_logger_dir='') +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 148442112 +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (4.28, 4.42) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:26:26 +> 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=2048, 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: 0.005436 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 33296 +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: 0.002632 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 33281 +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: 0.002561 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 33343 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 21:26:26 +done with setup ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (457.49, 482.09) + train/valid/test-data-iterators-setup ..........: (18.39, 133.35) +training ... +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 21:26:26 +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor:batch tensor: tokens tokens torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor:batch tensor: labelslabels torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor: tokens torch.Size([2, 4096]) +batch tensor:batch tensor: loss_maskloss_mask torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor: labels torch.Size([2, 4096]) +batch tensor:batch tensor:batch tensor: attention_mask attention_masktorch.Size([2, 1, 4096, 4096]) tokens + torch.Size([2, 1, 4096, 4096])batch tensor: + position_idsbatch tensor: torch.Size([2, 4096])position_ids +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) + torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask batch tensor:torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokensattention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor:batch tensor: position_ids torch.Size([2, 4096])tokens +batch tensor: position_ids torch.Size([2, 4096]) + torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp:batch tensor after cp: tokens tokens torch.Size([2, 1024]) +torch.Size([2, 1024])batch tensor after cp: + labels batch tensor after cp:torch.Size([2, 1024]) +labels batch tensor after cp:torch.Size([2, 1024]) +loss_mask batch tensor after cp:torch.Size([2, 1024]) +loss_maskbatch tensor after cp: torch.Size([2, 1024])attention_mask + batch tensor after cp: torch.Size([2, 1, 1024, 4096])attention_mask + batch tensor after cp:torch.Size([2, 1, 1024, 4096]) +position_ids batch tensor after cp: torch.Size([2, 1024])position_ids + torch.Size([2, 1024]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([2, 1, 1024, 4096])torch.Size([2, 1, 1024, 4096]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([2, 1024]) +torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp:batch tensor after cp: loss_mask tokenstorch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +torch.Size([2, 1024])batch tensor after cp: + position_ids batch tensor after cp:torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +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: 0.1400 +Theoretical memory footprints: weight and optimizer=2403.18 MB +[Rank 0] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2280.0 | max reserved: 2280.0 + [2025-06-21 21:26:41] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 14537.6 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[Rank 6] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2372.0 | max reserved: 2372.0[Rank 3] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2280.0 | max reserved: 2280.0 + +[Rank 10] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2280.0 | max reserved: 2280.0[Rank 14] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2280.0 | max reserved: 2280.0[Rank 11] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2280.0 | max reserved: 2280.0 + + +[Rank 12] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2280.0 | max reserved: 2280.0 +[Rank 8] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +[Rank 15] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +[Rank 7] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +[Rank 13] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +[Rank 1] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +[Rank 9] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2280.0 | max reserved: 2280.0 +[Rank 5] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +[Rank 4] (after 1 iterations) memory (MB) | allocated: 1888.93408203125 | max allocated: 1888.93505859375 | reserved: 2296.0 | max reserved: 2296.0 +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor:batch tensor: tokens tokens torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor: labels batch tensor:torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +labelsbatch tensor: loss_mask torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor: batch tensor:loss_mask attention_masktorch.Size([2, 4096]) torch.Size([2, 1, 4096, 4096]) + +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor:batch tensor: attention_maskposition_ids torch.Size([2, 4096]) +torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) 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1024]) +batch tensor after cp:batch tensor after cp: loss_maskattention_mask torch.Size([2, 1024])torch.Size([2, 1, 1024, 4096]) + +batch tensor after cp:batch tensor after cp: attention_maskposition_ids torch.Size([2, 1, 1024, 4096])torch.Size([2, 1024]) + +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) + tokensbatch tensor after cp: attention_mask torch.Size([2, 1024])torch.Size([2, 1, 1024, 4096]) + +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp:batch tensor after cp: labelsposition_ids torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +Start exporting trace 1 +Done exporting trace 1 + [2025-06-21 21:26:41] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 96.7 | 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([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096])batch tensor: +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) + tokens torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor:batch tensor: position_ids tokenstorch.Size([2, 4096]) +torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens batch tensor:torch.Size([2, 1024]) + batch tensor after cp:tokens labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: loss_masktorch.Size([2, 4096]) torch.Size([2, 1024]) + +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_maskbatch tensor: torch.Size([2, 1, 1024, 4096])labels + batch tensor after cp:torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +position_idsbatch tensor: batch tensor after cp: torch.Size([2, 1024]) loss_masktokens + torch.Size([2, 4096]) +torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor:batch tensor after cp: attention_masklabels torch.Size([2, 1, 4096, 4096])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor: loss_maskposition_ids torch.Size([2, 1024])torch.Size([2, 4096]) + +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +Start exporting trace 2 +Done exporting trace 2 + [2025-06-21 21:26:41] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 67.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 1073741824.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp:batch tensor: tokensattention_mask torch.Size([2, 1, 4096, 4096])torch.Size([2, 1024]) + +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor:batch tensor after cp: position_idslabels torch.Size([2, 4096])torch.Size([2, 1024]) + +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor:batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096])tokens + batch tensor after cp: position_ids torch.Size([2, 1024]) +torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +Start exporting trace 3 + [2025-06-21 21:26:41] iteration 4/ 10 | consumed samples: 4 | elapsed time per iteration (ms): 53.3 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 536870912.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +Done exporting trace 3 +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor:batch tensor: labels torch.Size([2, 4096])tokens +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) + batch tensor: loss_mask torch.Size([2, 4096]) +torch.Size([2, 4096])batch tensor: + attention_mask batch tensor:torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +labels batch tensor: torch.Size([2, 4096])position_ids + batch tensor:torch.Size([2, 4096]) +loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024])batch tensor after cp: + batch tensor after cp:tokens labels torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor: tokens tokenstorch.Size([2, 1024]) +batch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor after cp: attention_maskloss_mask torch.Size([2, 1, 1024, 4096])torch.Size([2, 1024]) + +batch tensor after cp: labels torch.Size([2, 1024]) +torch.Size([2, 4096])batch tensor after cp:batch tensor: +batch tensor after cp:batch tensor after cp: attention_maskposition_ids torch.Size([2, 1, 1024, 4096])torch.Size([2, 1024]) + +batch tensor after cp: position_ids torch.Size([2, 1024]) + loss_mask batch tensor:torch.Size([2, 1024]) + tokensbatch tensor after cp:labels attention_masktorch.Size([2, 4096]) +torch.Size([2, 1, 1024, 4096])batch tensor: + torch.Size([2, 4096])loss_maskbatch tensor after cp: + torch.Size([2, 4096])position_ids + batch tensor:batch tensor:torch.Size([2, 1024]) +attention_masklabels torch.Size([2, 1, 4096, 4096])torch.Size([2, 4096]) + +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: batch tensor:position_ids torch.Size([2, 4096])loss_mask + torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([2, 1024])torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) + +batch tensor after cp: batch tensor after cp:loss_mask loss_masktorch.Size([2, 1024]) +torch.Size([2, 1024])batch tensor after cp: +batch tensor: labels torch.Size([2, 4096]) + batch tensor after cp:attention_mask attention_mask torch.Size([2, 1, 1024, 4096]) +torch.Size([2, 1, 1024, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +Start exporting trace 4 +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +Done exporting trace 4 +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) + [2025-06-21 21:26:41] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 49.6 | 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([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens batch tensor after cp: tokens torch.Size([2, 1024])torch.Size([2, 4096]) + +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: batch tensor:labels labelstorch.Size([2, 1024]) +torch.Size([2, 4096]) +batch tensor after cp: batch tensor:loss_mask loss_masktorch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +torch.Size([2, 4096])batch tensor after cp: + attention_maskbatch tensor: torch.Size([2, 1, 1024, 4096])attention_mask + batch tensor after cp:torch.Size([2, 1, 4096, 4096]) +position_ids batch tensor: torch.Size([2, 1024])position_ids + torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens batch tensor:torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) + tokensbatch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: torch.Size([2, 4096])loss_mask +torch.Size([2, 4096]) +batch tensor:batch tensor: labelsattention_mask torch.Size([2, 4096]) +torch.Size([2, 1, 4096, 4096])batch tensor: + loss_maskbatch tensor: torch.Size([2, 4096])position_ids +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) + torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp:batch tensor after cp: labelstokens torch.Size([2, 1024]) +torch.Size([2, 1024])batch tensor after cp: +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) + loss_maskbatch tensor after cp: torch.Size([2, 1024])labels + torch.Size([2, 1024])batch tensor after cp: + attention_maskbatch tensor after cp: loss_masktorch.Size([2, 1, 1024, 4096]) +torch.Size([2, 1024])batch tensor after cp: +batch tensor: position_ids torch.Size([2, 4096]) + batch tensor after cp:position_ids attention_masktorch.Size([2, 1024]) +torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +Start exporting trace 5 +batch tensor after cp: position_ids torch.Size([2, 1024]) +Done exporting trace 5 + [2025-06-21 21:26:41] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 50.3 | 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([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024])batch tensor: +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: tokenslabels torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_masktorch.Size([2, 4096]) +torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp:batch tensor: attention_mask labelstorch.Size([2, 1, 1024, 4096]) +torch.Size([2, 4096])batch tensor after cp: +batch tensor after cp: loss_mask torch.Size([2, 1024]) + batch tensor:position_ids loss_masktorch.Size([2, 1024]) +torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +Start exporting trace 6 +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +Done exporting trace 6 + [2025-06-21 21:26:41] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 48.6 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 67108864.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: batch tensor:tokens tokens torch.Size([2, 4096]) +batch tensor:torch.Size([2, 4096]) labels + torch.Size([2, 4096]) +batch tensor:batch tensor: labelsloss_mask torch.Size([2, 4096])torch.Size([2, 4096]) + +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: batch tensor:loss_mask attention_masktorch.Size([2, 4096]) +torch.Size([2, 1, 4096, 4096]) +batch tensor:batch tensor: attention_maskposition_ids torch.Size([2, 1, 4096, 4096])torch.Size([2, 4096]) + +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) + batch tensor after cp: tokensattention_mask torch.Size([2, 1, 1024, 4096])torch.Size([2, 1024]) + +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp:batch tensor after cp: position_idslabels torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +Start exporting trace 7 +batch tensor: labels torch.Size([2, 4096]) +Done exporting trace 7 +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) + [2025-06-21 21:26:41] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 50.6 | 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([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor:batch tensor: tokens tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +torch.Size([2, 4096])batch tensor: + labels batch tensor:torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +labels batch tensor:torch.Size([2, 4096]) +loss_maskbatch tensor: torch.Size([2, 4096])loss_mask +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) + torch.Size([2, 4096])batch tensor: +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) + attention_maskbatch tensor: attention_masktorch.Size([2, 1, 4096, 4096]) +torch.Size([2, 1, 4096, 4096])batch tensor: +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) + batch tensor:position_ids batch tensor:torch.Size([2, 4096])position_ids +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) + torch.Size([2, 4096])tokens +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) + torch.Size([2, 4096]) +batch tensor after cp:batch tensor: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +tokens torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp:batch tensor: tokensattention_mask torch.Size([2, 1, 4096, 4096]) +torch.Size([2, 1024]) +batch tensor: batch tensor after cp:position_ids labels torch.Size([2, 4096]) +torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens batch tensor after cp:torch.Size([2, 1024]) +tokens batch tensor after cp: labels torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp:batch tensor after cp: loss_maskattention_mask torch.Size([2, 1024])torch.Size([2, 1, 1024, 4096]) + +batch tensor after cp:batch tensor after cp: attention_maskposition_ids torch.Size([2, 1, 1024, 4096])torch.Size([2, 1024]) + +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +Start exporting trace 8 +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +Done exporting trace 8 + [2025-06-21 21:26:41] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 49.6 | 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([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: batch tensor:attention_mask torch.Size([2, 1, 4096, 4096]) +tokens batch tensor: position_ids torch.Size([2, 4096]) +torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labelsbatch tensor after cp: torch.Size([2, 1024])tokens + batch tensor after cp: torch.Size([2, 1024])loss_mask +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) + batch tensor after cp:torch.Size([2, 1024]) +labels batch tensor after cp: torch.Size([2, 1024])attention_mask + batch tensor after cp:torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +loss_maskbatch tensor after cp: torch.Size([2, 1024])position_ids + batch tensor after cp: attention_mask torch.Size([2, 1024]) +torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: batch tensor:tokens attention_mask torch.Size([2, 1024]) +torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp:batch tensor: labelsposition_ids torch.Size([2, 1024])torch.Size([2, 4096]) + +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: tokens batch tensor:torch.Size([2, 4096]) +batch tensor: tokens torch.Size([2, 4096]) + batch tensor:tokens labels torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor: torch.Size([2, 4096])loss_mask +torch.Size([2, 4096]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: batch tensor:labels attention_mask torch.Size([2, 4096]) +torch.Size([2, 1, 4096, 4096])batch tensor: +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) + loss_maskbatch tensor: torch.Size([2, 4096])position_ids + torch.Size([2, 4096])batch tensor: + attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor: tokens torch.Size([2, 4096]) +batch tensor: labels torch.Size([2, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor: loss_mask torch.Size([2, 4096]) +batch tensor: attention_mask torch.Size([2, 1, 4096, 4096]) +batch tensor: position_ids torch.Size([2, 4096]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: tokens torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: labels torch.Size([2, 1024]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: loss_mask torch.Size([2, 1024]) +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +Start exporting trace 9 +batch tensor after cp: tokens torch.Size([2, 1024]) +Done exporting trace 9 +batch tensor after cp: labels batch tensor after cp:torch.Size([2, 1024]) +[after training is done] datetime: 2025-06-21 21:26:41 + batch tensor after cp:tokens loss_mask torch.Size([2, 1024])torch.Size([2, 1024]) + +batch tensor after cp:batch tensor after cp: labelsattention_mask torch.Size([2, 1024])torch.Size([2, 1, 1024, 4096]) + +batch tensor after cp:batch tensor after cp: loss_maskposition_ids torch.Size([2, 1024])torch.Size([2, 1024]) +saving checkpoint at iteration 10 to gpt-checkpoint in torch_dist format + +batch tensor after cp: attention_mask torch.Size([2, 1, 1024, 4096]) +batch tensor after cp: position_ids torch.Size([2, 1024]) +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.026499271392822266 to prepare state dict for ckpt + [2025-06-21 21:26:41] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 50.5 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 8388608.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.026578426361083984 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.02660369873046875 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.026607990264892578 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.03094172477722168 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.026666879653930664 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.030977487564086914 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.02707076072692871 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.031093835830688477 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.027416706085205078 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.031195640563964844 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.02897953987121582 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.03126859664916992 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.031272172927856445 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.03154253959655762 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.03176164627075195 to prepare state dict for ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +Running ctx_length=4096, TP_SIZE=4, CP_SIZE=4, BATCH_SIZE=2 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 4096 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 4096 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +using world size: 16, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 4, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 4096 + 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 ..................... 4 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 ......................... 4096 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 ...................................... 4096 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 ...................... 4 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 ... + > padded vocab (size: 50257) with 431 dummy tokens (new size: 50688) +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED +> initializing torch distributed ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written. +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +> initialized tensor model parallel with size 4 +> 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: 0.046 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.370 seconds +time to initialize megatron (seconds): 7.404 +[after megatron is initialized] datetime: 2025-06-21 21:27:52 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 156830720 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 156830720 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 156830720 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> embedding +>>> decoder +>>> output_layer +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 156830720 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 156830720 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 156830720 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 156830720 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 156830720 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 156830720 +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 (156830720 elements, 156830720 padded size): + 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.embedding.position_embeddings.weight + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.final_layernorm.weight + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_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.embedding.word_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 +WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (3.24, 3.69) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:27:52 +> 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=4096, 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: 0.006694 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 16648 +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: 0.002194 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 16640 +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: 0.002023 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 16671 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 21:27:52 +done with setup ... +training ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (599.21, 617.14) + train/valid/test-data-iterators-setup ..........: (17.86, 136.11) +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 21:27:52 +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp:batch tensor after cp: attention_mask tokens torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +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: 0.1400 +Theoretical memory footprints: weight and optimizer=2403.18 MB +[Rank 0] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2808.0 | max reserved: 2808.0 + [2025-06-21 21:28:09] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 16205.1 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[Rank 7] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2808.0 | max reserved: 2808.0 +[Rank 14] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2754.0 | max reserved: 2754.0[Rank 13] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2754.0 | max reserved: 2754.0 + +[Rank 9] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2754.0 | max reserved: 2754.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2808.0 | max reserved: 2808.0 +[Rank 8] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2792.0 | max reserved: 2792.0 +[Rank 11] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2792.0 | max reserved: 2792.0 +[Rank 5] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2808.0 | max reserved: 2808.0 +[Rank 6] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2808.0 | max reserved: 2808.0 +[Rank 15] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2792.0 | max reserved: 2792.0 +[Rank 4] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2770.0 | max reserved: 2770.0 +[Rank 12] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2754.0 | max reserved: 2754.0 +[Rank 1] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2808.0 | max reserved: 2808.0 +[Rank 3] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2808.0 | max reserved: 2808.0 +[Rank 10] (after 1 iterations) memory (MB) | allocated: 2105.20751953125 | max allocated: 2289.78564453125 | reserved: 2754.0 | max reserved: 2754.0 +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels batch tensor:torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_masktokens torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_idstorch.Size([2, 8192]) torch.Size([2, 2048]) + +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) 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torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 1 +Done exporting trace 1 + [2025-06-21 21:28:09] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 134.1 | 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192])batch tensor: +batch tensor after cp: position_ids torch.Size([2, 2048]) + batch tensor:tokens labels torch.Size([2, 8192]) +batch tensor: torch.Size([2, 8192])loss_mask + torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labelsbatch tensor: attention_masktorch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +torch.Size([2, 1, 8192, 8192])batch tensor: +batch tensor: position_ids torch.Size([2, 8192]) + loss_maskbatch tensor: torch.Size([2, 8192])position_ids + torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokensbatch tensor after cp: torch.Size([2, 2048])tokens + batch tensor after cp:torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +labelsbatch tensor after cp: torch.Size([2, 2048])labels + batch tensor after cp:torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +loss_maskbatch tensor after cp: torch.Size([2, 2048])loss_mask + batch tensor after cp:torch.Size([2, 2048]) +attention_mask batch tensor after cp: torch.Size([2, 1, 2048, 8192])attention_mask + batch tensor after cp:torch.Size([2, 1, 2048, 8192]) +position_ids batch tensor after cp: torch.Size([2, 2048])position_ids + torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 2 +Done exporting trace 2 + [2025-06-21 21:28:09] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 111.4 | 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048])batch tensor: + tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 3 +Done exporting trace 3 + [2025-06-21 21:28:09] iteration 4/ 10 | consumed samples: 4 | elapsed time per iteration (ms): 96.5 | 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp:batch tensor: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +tokensbatch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +torch.Size([2, 8192])batch tensor after cp: + position_ids batch tensor:torch.Size([2, 2048]) +labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 4 +Done exporting trace 4 + [2025-06-21 21:28:09] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 96.9 | 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 5 +Done exporting trace 5 + [2025-06-21 21:28:09] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 101.0 | 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +tokensbatch tensor after cp: attention_masktorch.Size([2, 2048]) +torch.Size([2, 1, 2048, 8192])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([2, 2048])position_ids +batch tensor: tokens torch.Size([2, 8192]) + batch tensor after cp:torch.Size([2, 2048]) +loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 6 +Done exporting trace 6 + [2025-06-21 21:28:09] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 98.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 67108864.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) 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attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 7 +Done exporting trace 7 + [2025-06-21 21:28:09] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 98.2 | 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens batch tensor:torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) + tokensbatch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 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torch.Size([2, 1, 8192, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: tokensbatch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labelstorch.Size([2, 8192]) torch.Size([2, 2048]) + +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor:batch tensor after cp: labelsloss_mask torch.Size([2, 8192])torch.Size([2, 2048]) + +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor:batch tensor after cp: loss_mask torch.Size([2, 8192])attention_mask + torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 8 +Done exporting trace 8 + [2025-06-21 21:28:10] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 96.1 | 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor:batch tensor after cp: tokenstokens torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: labels torch.Size([2, 8192])torch.Size([2, 2048]) + +batch tensor after cp: loss_maskbatch tensor: torch.Size([2, 2048])labels +batch tensor after cp: position_ids torch.Size([2, 2048]) + batch tensor after cp:torch.Size([2, 8192]) +attention_mask batch tensor: torch.Size([2, 1, 2048, 8192])loss_mask + batch tensor after cp:torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +position_ids torch.Size([2, 2048])batch tensor: + attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +Start exporting trace 9 +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Done exporting trace 9 + [2025-06-21 21:28:10] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 99.1 | 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 21:28:10 +saving checkpoint at iteration 10 to gpt-checkpoint in torch_dist format +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.028484344482421875 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.028470516204833984 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.02848339080810547 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.028439760208129883 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.028613805770874023 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.029155731201171875 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.032686471939086914 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.03283262252807617 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.03284740447998047 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.032926321029663086 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.032959699630737305 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.03305315971374512 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.033384084701538086 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.033490657806396484 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.0365297794342041 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.038352251052856445 to prepare state dict for ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +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(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +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(236978176), 0), (np.int64(234244096), 1), (np.int64(234881024), 2), (np.int64(234881024), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(236978176), 0), (np.int64(234244096), 1), (np.int64(234881024), 2), (np.int64(234881024), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(236978176), 0), (np.int64(234244096), 1), (np.int64(234881024), 2), (np.int64(234881024), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(236978176), 0), (np.int64(234244096), 1), (np.int64(234881024), 2), (np.int64(234881024), 3)] +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1288526058197021 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1228728294372559 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1292126178741455 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.129863977432251 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.143646001815796 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1315422058105469 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.122816562652588 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1227803230285645 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1231229305267334 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1231951713562012 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1292660236358643 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1236369609832764 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1302053928375244 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1236395835876465 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1296489238739014 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 0.011177539825439453 +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: 5, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 8, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 6, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 9, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 7, 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:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 12, 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: 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:rank: 11, 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: 15, 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:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver: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:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 0, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata 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of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 1, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata 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114405376, before: 1852350464, after: 1966755840 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 114204672, before: 1845878784, after: 1960083456 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 105910272, before: 1821274112, after: 1927184384 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 105906176, before: 1838907392, after: 1944813568 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 114495488, before: 1808105472, after: 1922600960 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 113557504, before: 1857929216, after: 1971486720 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109993984, before: 1857929216, after: 1967923200 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109182976, before: 1852350464, after: 1961533440 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109973504, before: 1838907392, after: 1948880896 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110272512, before: 1790623744, after: 1900896256 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.923234, rank: 5, write(sync,parallel): 0.3168978691101074 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110166016, before: 1808105472, after: 1918271488 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.9262025, rank: 7, write(sync,parallel): 0.3389778137207031 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 113438720, before: 1869754368, after: 1983193088 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 114262016, before: 1837793280, after: 1952055296 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110096384, before: 1821274112, after: 1931370496 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 131117056, before: 1869754368, after: 2000871424 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 131039232, before: 1837793280, after: 1968832512 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.9507565, rank: 6, write(sync,parallel): 0.34789490699768066 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.9595282, rank: 11, write(sync,parallel): 0.3622012138366699 +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:1750541291.961779, rank: 9, write(sync,parallel): 0.3621821403503418 +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:1750541291.9685247, rank: 15, write(sync,parallel): 0.37357616424560547 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 121929728, before: 1814130688, after: 1936060416 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.9716303, rank: 10, write(sync,parallel): 0.37426090240478516 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.9735224, rank: 13, write(sync,parallel): 0.3828136920928955 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 122523648, before: 1814126592, after: 1936650240 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.98258, rank: 8, write(sync,parallel): 0.37773799896240234 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.9854226, rank: 14, write(sync,parallel): 0.38834309577941895 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.42s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541291.9954317, rank: 12, write(sync,parallel): 0.38338613510131836 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.44s 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.async_utils:TemporalAsyncCaller: Async process join finished after 0.45s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212373504, before: 1803464704, after: 2015838208 +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 0.46s 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 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541292.0246654, rank: 4, write(sync,parallel): 0.41351842880249023 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212381696, before: 2039046144, after: 2251427840 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212316160, before: 1819062272, after: 2031378432 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212250624, before: 1894281216, after: 2106531840 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541292.0560756, rank: 2, write(sync,parallel): 0.4492523670196533 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.50s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541292.0758467, rank: 0, write(sync,parallel): 0.41569948196411133 +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 0.52s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541292.0943015, rank: 3, write(sync,parallel): 0.43990588188171387 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541292.0987601, rank: 1, write(sync,parallel): 0.4406402111053467 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.49s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.52s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.53s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1442602, 1, gather: 0.0020987987518310547 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1447875, 9, gather: 0.13370180130004883 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1448247, 3, gather: 0.009159564971923828 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.144864, 2, gather: 0.050169944763183594 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1448655, 4, gather: 0.07200956344604492 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.144922, 10, gather: 0.12523818016052246 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1450226, 11, gather: 0.1454486846923828 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1449065, 6, gather: 0.15255045890808105 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1452029, 13, gather: 0.1192317008972168 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1449282, 5, gather: 0.18052268028259277 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1452365, 14, gather: 0.11240696907043457 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1449528, 7, gather: 0.17794394493103027 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1452618, 12, gather: 0.11094999313354492 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1453874, 15, gather: 0.13736534118652344 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.1454313, 8, gather: 0.12065410614013672 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.146803, 0, gather: 0.0322263240814209 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541292.155664, metadata_write: 0.00872039794921875 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0432s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0220s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1910s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1937s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1660s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0630s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0155s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0855s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1337s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1258s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1326s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1390s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1588s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1474s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1503s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1243s + successfully saved checkpoint from iteration 10 to gpt-checkpoint [ t 1/4, p 1/1 ] +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.002740144729614258 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.002752065658569336 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.0027284622192382812 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.002738475799560547 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.0027337074279785156 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.002796173095703125 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.0027921199798583984 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.0028295516967773438 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.002775430679321289 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.0027332305908203125 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.0027742385864257812 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.0027604103088378906 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.0027437210083007812 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.0027861595153808594 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.0027208328247070312 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.002751588821411133 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([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +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 .......................................: (3175.29, 3177.01) +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +---------------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on validation set | lm loss value: 1.249277E+01 | lm loss PPL: 2.664055E+05 | +---------------------------------------------------------------------------------------------------------------- +Evaluating on 1 samples +Evaluating iter 1/1 +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +(min, max) time across ranks (ms): + evaluate .......................................: (89.51, 91.52) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +---------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on test set | lm loss value: 1.249277E+01 | lm loss PPL: 2.664055E+05 | +---------------------------------------------------------------------------------------------------------- +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +batch tensor: tokens torch.Size([2, 8192]) +batch tensor: labels torch.Size([2, 8192]) +batch tensor: loss_mask torch.Size([2, 8192]) +batch tensor: attention_mask torch.Size([2, 1, 8192, 8192]) +batch tensor: position_ids torch.Size([2, 8192]) +batch tensor after cp: tokens torch.Size([2, 2048]) +batch tensor after cp: labels torch.Size([2, 2048]) +batch tensor after cp: loss_mask torch.Size([2, 2048]) +batch tensor after cp: attention_mask torch.Size([2, 1, 2048, 8192]) +batch tensor after cp: position_ids torch.Size([2, 2048]) +Start exporting trace 11 +Done exporting trace 11 +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +Running ctx_length=8192, TP_SIZE=4, CP_SIZE=4, BATCH_SIZE=2 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 8192 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 8192 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +using world size: 16, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 4, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 8192 + encoder_tensor_model_parallel_size .............. 0 + end_weight_decay ................................ 0.1 + eod_mask_loss ................................... False + error_injection_rate ............................ 0 + error_injection_type ............................ transient_error + eval_interval ................................... 16 + eval_iters ...................................... 1 + evidence_data_path .............................. None + exit_duration_in_mins ........................... None + exit_interval ................................... None + exit_on_missing_checkpoint ...................... False + exit_signal_handler ............................. False + exp_avg_dtype ................................... torch.float32 + exp_avg_sq_dtype ................................ torch.float32 + expert_model_parallel_size ...................... 1 + expert_tensor_parallel_size ..................... 4 + 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 ...................... 4 + 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 + > padded vocab (size: 50257) with 431 dummy tokens (new size: 50688) +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED +> initializing torch distributed ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written. +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +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 +> initialized tensor model parallel with size 4 +> initialized pipeline model parallel with size 1 +> setting random seeds to 1234 ... +> compiling dataset index builder ... +make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +make: Nothing to be done for 'default'. +make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +>>> done with dataset index builder. Compilation time: 0.042 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.473 seconds +time to initialize megatron (seconds): 8.461 +[after megatron is initialized] datetime: 2025-06-21 21:28:53 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 173607936 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 173607936 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 173607936 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 173607936 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 173607936 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> embedding>>> decoder + +>>> output_layer>>> decoder + +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 173607936 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 173607936 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 173607936 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 173607936 +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 (173607936 elements, 173607936 padded size): + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.embedding.position_embeddings.weight + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.decoder.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.final_layernorm.bias + 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.weight + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.embedding.word_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 + loading distributed checkpoint from gpt-checkpoint at iteration 10 +Running ctx_length=12288, TP_SIZE=4, CP_SIZE=4, BATCH_SIZE=2 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 12288 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 12288 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +using world size: 16, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 4, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 12288 + encoder_tensor_model_parallel_size .............. 0 + end_weight_decay ................................ 0.1 + eod_mask_loss ................................... False + error_injection_rate ............................ 0 + error_injection_type ............................ transient_error + eval_interval ................................... 16 + eval_iters ...................................... 1 + evidence_data_path .............................. None + exit_duration_in_mins ........................... None + exit_interval ................................... None + exit_on_missing_checkpoint ...................... False + exit_signal_handler ............................. False + exp_avg_dtype ................................... torch.float32 + exp_avg_sq_dtype ................................ torch.float32 + expert_model_parallel_size ...................... 1 + expert_tensor_parallel_size ..................... 4 + external_cuda_graph ............................. False + ffn_hidden_size ................................. 16384 + finetune ........................................ False + first_last_layers_bf16 .......................... False + flash_decode .................................... False + fp16 ............................................ True + fp16_lm_cross_entropy ........................... False + fp32_residual_connection ........................ False + fp8 ............................................. None + fp8_amax_compute_algo ........................... most_recent + fp8_amax_history_len ............................ 1 + fp8_interval .................................... 1 + fp8_margin ...................................... 0 + fp8_param_gather ................................ False + fp8_recipe ...................................... delayed + fp8_wgrad ....................................... True + fsdp_double_buffer .............................. False + global_batch_size ............................... 1 + grad_reduce_in_bf16 ............................. False + gradient_accumulation_fusion .................... True + gradient_reduce_div_fusion ...................... True + group_query_attention ........................... True + head_lr_mult .................................... 1.0 + heterogeneous_layers_config_encoded_json ........ None + heterogeneous_layers_config_path ................ None + hidden_dropout .................................. 0.1 + hidden_size ..................................... 4096 + hierarchical_context_parallel_sizes ............. None + high_priority_stream_groups ..................... [] + hybrid_attention_ratio .......................... 0.0 + hybrid_mlp_ratio ................................ 0.0 + hybrid_override_pattern ......................... None + hysteresis ...................................... 2 + ict_head_size ................................... None + ict_load ........................................ None + img_h ........................................... 224 + img_w ........................................... 224 + indexer_batch_size .............................. 128 + indexer_log_interval ............................ 1000 + inference_batch_times_seqlen_threshold .......... -1 + inference_dynamic_batching ...................... False + inference_dynamic_batching_buffer_guaranteed_fraction 0.2 + inference_dynamic_batching_buffer_overflow_factor None + inference_dynamic_batching_buffer_size_gb ....... 40.0 + inference_dynamic_batching_chunk_size ........... 256 + inference_dynamic_batching_max_requests_override None + inference_dynamic_batching_max_tokens_override .. None + inference_max_batch_size ........................ 8 + inference_max_seq_length ........................ 2560 + inference_rng_tracker ........................... False + init_method_std ................................. 0.02 + init_method_xavier_uniform ...................... False + init_model_with_meta_device ..................... False + initial_loss_scale .............................. 4294967296 + inprocess_active_world_size ..................... 16 + inprocess_barrier_timeout ....................... 120 + inprocess_completion_timeout .................... 120 + inprocess_empty_cuda_cache ...................... False + inprocess_granularity ........................... node + inprocess_hard_timeout .......................... 90 + inprocess_heartbeat_interval .................... 30 + inprocess_heartbeat_timeout ..................... 60 + inprocess_last_call_wait ........................ 1 + inprocess_max_iterations ........................ None + inprocess_monitor_process_interval .............. 1.0 + inprocess_monitor_thread_interval ............... 1.0 + inprocess_progress_watchdog_interval ............ 1.0 + inprocess_restart ............................... False + inprocess_soft_timeout .......................... 60 + inprocess_termination_grace_time ................ 1 + is_hybrid_model ................................. False + iter_per_epoch .................................. 1250 + iterations_to_skip .............................. [] + keep_fp8_transpose_cache_when_using_custom_fsdp . False + kv_channels ..................................... 64 + kv_lora_rank .................................... 32 + lazy_mpu_init ................................... None + load ............................................ gpt-checkpoint + load_model_opt_format ........................... False + local_rank ...................................... 0 + log_interval .................................... 1 + log_loss_scale_to_tensorboard ................... True + log_memory_to_tensorboard ....................... False + log_num_zeros_in_grad ........................... False + log_params_norm ................................. False + log_progress .................................... False + log_straggler ................................... False + log_throughput .................................. False + log_timers_to_tensorboard ....................... False + log_validation_ppl_to_tensorboard ............... False + log_world_size_to_tensorboard ................... False + logging_level ................................... 0 + loss_scale ...................................... None + loss_scale_window ............................... 1000 + lr .............................................. 0.0005 + lr_decay_iters .................................. 150000 + lr_decay_samples ................................ None + lr_decay_style .................................. cosine + lr_warmup_fraction .............................. None + lr_warmup_init .................................. 0.0 + lr_warmup_iters ................................. 2 + lr_warmup_samples ............................... 0 + lr_wsd_decay_iters .............................. None + lr_wsd_decay_samples ............................ None + lr_wsd_decay_style .............................. exponential + main_grads_dtype ................................ torch.float32 + main_params_dtype ............................... torch.float32 + make_vocab_size_divisible_by .................... 128 + mamba_head_dim .................................. 64 + mamba_num_groups ................................ 8 + mamba_num_heads ................................. None + mamba_state_dim ................................. 128 + manual_gc ....................................... False + manual_gc_eval .................................. True + manual_gc_interval .............................. 0 + mask_factor ..................................... 1.0 + mask_prob ....................................... 0.15 + mask_type ....................................... random + masked_softmax_fusion ........................... True + max_position_embeddings ......................... 12288 + max_tokens_to_oom ............................... 12000 + memory_snapshot_path ............................ snapshot.pickle + merge_file ...................................... merges.txt + micro_batch_size ................................ 1 + microbatch_group_size_per_vp_stage .............. None + mid_level_dataset_surplus ....................... 0.005 + min_loss_scale .................................. 1.0 + min_lr .......................................... 0.0 + mlp_chunks_for_prefill .......................... 1 + mmap_bin_files .................................. True + mock_data ....................................... True + moe_apply_probs_on_input ........................ False + moe_aux_loss_coeff .............................. 0.0 + moe_enable_deepep ............................... False + moe_expert_capacity_factor ...................... None + moe_extended_tp ................................. False + moe_ffn_hidden_size ............................. None + moe_grouped_gemm ................................ False + moe_input_jitter_eps ............................ None + moe_layer_freq .................................. 1 + moe_layer_recompute ............................. False + moe_pad_expert_input_to_capacity ................ False + moe_per_layer_logging ........................... False + moe_permute_fusion .............................. False + moe_router_bias_update_rate ..................... 0.001 + moe_router_dtype ................................ None + moe_router_enable_expert_bias ................... False + moe_router_force_load_balancing ................. False + moe_router_group_topk ........................... None + moe_router_load_balancing_type .................. aux_loss + moe_router_num_groups ........................... None + moe_router_padding_for_fp8 ...................... False + moe_router_pre_softmax .......................... False + moe_router_score_function ....................... softmax + moe_router_topk ................................. 2 + moe_router_topk_scaling_factor .................. None + moe_shared_expert_intermediate_size ............. None + moe_shared_expert_overlap ....................... False + moe_token_dispatcher_type ....................... allgather + moe_token_drop_policy ........................... probs + moe_use_legacy_grouped_gemm ..................... False + moe_use_upcycling ............................... False + moe_z_loss_coeff ................................ None + mrope_section ................................... None + mscale .......................................... 1.0 + mscale_all_dim .................................. 1.0 + mtp_loss_scaling_factor ......................... 0.1 + mtp_num_layers .................................. None + multi_latent_attention .......................... False + nccl_all_reduce_for_prefill ..................... False + nccl_communicator_config_path ................... None + nccl_ub ......................................... False + no_load_optim ................................... None + no_load_rng ..................................... None + no_persist_layer_norm ........................... False + no_rope_freq .................................... None + no_save_optim ................................... None + no_save_rng ..................................... None + non_persistent_ckpt_type ........................ None + non_persistent_global_ckpt_dir .................. None + non_persistent_local_ckpt_algo .................. fully_parallel + non_persistent_local_ckpt_dir ................... None + non_persistent_save_interval .................... None + norm_epsilon .................................... 1e-05 + normalization ................................... LayerNorm + num_attention_heads ............................. 64 + num_channels .................................... 3 + num_classes ..................................... 1000 + num_dataset_builder_threads ..................... 1 + num_distributed_optimizer_instances ............. 1 + num_experts ..................................... None + num_layers ...................................... 2 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 + query_in_block_prob ............................. 0.1 + rampup_batch_size ............................... None + rank ............................................ 0 + recompute_granularity ........................... None + recompute_method ................................ None + recompute_modules ............................... None + recompute_num_layers ............................ None + record_memory_history ........................... False + relative_attention_max_distance ................. 128 + relative_attention_num_buckets .................. 32 + replication ..................................... False + replication_factor .............................. 2 + replication_jump ................................ None + rerun_mode ...................................... disabled + reset_attention_mask ............................ False + reset_position_ids .............................. False + result_rejected_tracker_filename ................ None + retriever_report_topk_accuracies ................ [] + retriever_score_scaling ......................... False + retriever_seq_length ............................ 256 + retro_add_retriever ............................. False + retro_attention_gate ............................ 1 + retro_cyclic_train_iters ........................ None + retro_encoder_attention_dropout ................. 0.1 + retro_encoder_hidden_dropout .................... 0.1 + retro_encoder_layers ............................ 2 + retro_num_neighbors ............................. 2 + retro_num_retrieved_chunks ...................... 2 + retro_project_dir ............................... None + retro_verify_neighbor_count ..................... True + rope_scaling_factor ............................. 8.0 + rotary_base ..................................... 10000 + rotary_interleaved .............................. False + rotary_percent .................................. 1.0 + rotary_scaling_factor ........................... 1.0 + rotary_seq_len_interpolation_factor ............. None + run_workload_inspector_server ................... False + sample_rate ..................................... 1.0 + save ............................................ gpt-checkpoint + save_interval ................................... 16 + scatter_gather_tensors_in_pipeline .............. True + seed ............................................ 1234 + seq_length ...................................... 12288 + sequence_parallel ............................... False + sgd_momentum .................................... 0.9 + short_seq_prob .................................. 0.1 + skip_train ...................................... False + skipped_train_samples ........................... 0 + spec ............................................ None + split ........................................... None + squared_relu .................................... False + start_weight_decay .............................. 0.1 + straggler_ctrlr_port ............................ 65535 + straggler_minmax_count .......................... 1 + suggested_communication_unit_size ............... None + swiglu .......................................... False + swin_backbone_type .............................. tiny + symmetric_ar_type ............................... None + te_rng_tracker .................................. False + tensor_model_parallel_size ...................... 4 + 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.training.initialize:Setting logging level to 0 +INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1 +> building GPT2BPETokenizer tokenizer ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 + > padded vocab (size: 50257) with 431 dummy tokens (new size: 50688) +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:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED +> initializing torch distributed ... +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +> initialized tensor model parallel with size 4 +> 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: 0.048 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.688 seconds +time to initialize megatron (seconds): 8.328 +[after megatron is initialized] datetime: 2025-06-21 21:29:32 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 190385152 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 190385152 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 190385152 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 190385152 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 190385152 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 190385152 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 190385152 +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 (190385152 elements, 190385152 padded size): + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.embedding.position_embeddings.weight + module.embedding.word_embeddings.weight + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.bias + module.decoder.final_layernorm.weight + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias +INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=, config_logger_dir='') +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (3.02, 3.42) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:29:33 +> building train, validation, and test datasets ... + > datasets target sizes (minimum size): + train: 10 + validation: 1 + test: 1 +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)] +> building train, validation, and test datasets for GPT ... +INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=12288, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None) +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.005125 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 5549 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.001795 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 5546 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.001710 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 5557 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 21:29:33 +done with setup ... +training ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (916.85, 936.11) + train/valid/test-data-iterators-setup ..........: (16.41, 125.85) +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 21:29:33 +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +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: 0.1400 +Theoretical memory footprints: weight and optimizer=2403.18 MB +[Rank 1] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6022.0 | max reserved: 6022.0 +[Rank 5] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6036.0 | max reserved: 6036.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 5926.0 | max reserved: 5926.0[Rank 3] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6022.0 | max reserved: 6022.0 + [2025-06-21 21:29:48] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 14628.3 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | + +[Rank 15] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 5952.0 | max reserved: 5952.0 +[Rank 4] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6036.0 | max reserved: 6036.0[Rank 7] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 5940.0 | max reserved: 5940.0 + +[Rank 10] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6012.0 | max reserved: 6012.0 +[Rank 0] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 5926.0 | max reserved: 5926.0 +[Rank 8] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6012.0 | max reserved: 6012.0 +[Rank 6] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6036.0 | max reserved: 6036.0 +[Rank 13] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6048.0 | max reserved: 6048.0 +[Rank 12] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 5952.0 | max reserved: 5952.0[Rank 9] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6108.0 | max reserved: 6108.0 + +[Rank 14] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 5952.0 | max reserved: 5952.0 +[Rank 11] (after 1 iterations) memory (MB) | allocated: 3770.30126953125 | max allocated: 5558.55126953125 | reserved: 6012.0 | max reserved: 6012.0 +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after 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tokens torch.Size([2, 6144])tokens + batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +torch.Size([2, 24576])batch tensor after cp: + position_ids batch tensor:torch.Size([2, 6144]) + labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) 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torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp:batch tensor: loss_masktokens torch.Size([2, 24576]) +torch.Size([2, 6144]) +batch tensor after cp: batch tensor:labels attention_masktorch.Size([2, 6144]) +torch.Size([2, 1, 24576, 24576])batch tensor after cp: + loss_maskbatch tensor: torch.Size([2, 6144])position_ids + torch.Size([2, 24576])batch tensor after cp: + attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 1 +Done exporting trace 1 + [2025-06-21 21:29:48] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 759.2 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 2147483648.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) 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attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 2 +Done exporting trace 2 + [2025-06-21 21:29:49] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 737.4 | 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([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor:batch tensor after cp: tokens torch.Size([2, 6144])tokens + batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp:torch.Size([2, 24576]) +attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor:batch tensor after cp: labelsposition_ids torch.Size([2, 24576])torch.Size([2, 6144]) + +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 3 +Done exporting trace 3 + [2025-06-21 21:29:50] iteration 4/ 10 | consumed samples: 4 | elapsed time per iteration (ms): 721.8 | 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([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor 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6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 4 +Done exporting trace 4 + [2025-06-21 21:29:50] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 718.6 | 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([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) 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after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 5 +Done exporting trace 5 + [2025-06-21 21:29:51] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 720.6 | 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([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) 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torch.Size([2, 6144]) +batch tensor after cp:batch tensor after cp: attention_masktokens torch.Size([2, 1, 6144, 24576]) +torch.Size([2, 6144])batch tensor after cp: + position_idsbatch tensor after cp: torch.Size([2, 6144])labels + torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 6 +Done exporting trace 6 + [2025-06-21 21:29:52] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 726.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 67108864.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 7 +Done exporting trace 7 + [2025-06-21 21:29:53] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 709.6 | 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([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: tokens batch tensor:torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) + batch tensor:tokens labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576])torch.Size([2, 24576]) + +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: batch tensor:attention_mask labels torch.Size([2, 1, 24576, 24576])torch.Size([2, 24576]) + +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor:batch tensor: loss_maskposition_ids torch.Size([2, 24576])torch.Size([2, 24576]) + +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: attention_mask batch tensor after cp:torch.Size([2, 1, 6144, 24576]) +tokensbatch tensor after cp: position_idstorch.Size([2, 6144]) +torch.Size([2, 6144])batch tensor after cp: +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) + labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 8 +Done exporting trace 8 + [2025-06-21 21:29:53] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 710.6 | 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([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576])batch tensor: +batch tensor after cp: position_ids torch.Size([2, 6144])tokens + torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens 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tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 9 +Done exporting trace 9 + [2025-06-21 21:29:54] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 711.5 | 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 21:29:54 +saving checkpoint at iteration 10 to gpt-checkpoint in torch_dist format +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.029028654098510742 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.02905106544494629 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.029170989990234375 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.029161453247070312 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.029192209243774414 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.02983713150024414 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.03052830696105957 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.029851675033569336 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.030559301376342773 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.03059554100036621 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.030605554580688477 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.03066396713256836 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.030834436416625977 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.03107738494873047 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.031137704849243164 to prepare state dict for ckpt +WARNING:megatron.core.dist_checkpointing.serialization:Overwriting old incomplete / corrupted checkpoint... +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.03382301330566406 to prepare state dict for ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(207618048), 0), (np.int64(212860928), 1), (np.int64(213909504), 2), (np.int64(205588480), 3)] +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(283115520), 0), (np.int64(289406976), 1), (np.int64(288358400), 2), (np.int64(281430016), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(283115520), 0), (np.int64(289406976), 1), (np.int64(288358400), 2), (np.int64(281430016), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(283115520), 0), (np.int64(289406976), 1), (np.int64(288358400), 2), (np.int64(281430016), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(283115520), 0), (np.int64(289406976), 1), (np.int64(288358400), 2), (np.int64(281430016), 3)] +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4593236446380615 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4596493244171143 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.459573745727539 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.498276948928833 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4648265838623047 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.465041160583496 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4638030529022217 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4601385593414307 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4656970500946045 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4605937004089355 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4958171844482422 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.460658311843872 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4677798748016357 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 0.012742757797241211 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.4612007141113281 +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: 4, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 9, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 5, 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:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.467801570892334 +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: 8, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 7, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 6, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 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:rank: 2, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 12, starting state dict save +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:loaded global metadata reuse verification: no loaded plans passed +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 13, 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:verifying reuse of global metadata +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 0, 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: 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: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: 1, 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: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:rank: 10, 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: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: 3, plan time: 0.002505064010620117 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 5, plan time: 0.006957054138183594 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 7, plan time: 0.005794525146484375 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 1, plan time: 0.002057313919067383 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 2, plan time: 0.003878355026245117 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 4, plan time: 0.007035732269287109 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750541396.1863625 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 14, plan time: 0.005266666412353516 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 12, plan time: 0.005021572113037109 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750541396.1863685 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:thread_count: 2, time: 1750541396.1863694 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:rank: 8, plan time: 0.005592823028564453 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+DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04500603675842285 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.231756 rank: 5, write(async) time: 0.04538416862487793 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04621529579162598 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.233003 rank: 4, write(async) time: 0.046613454818725586 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.048488616943359375 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04851055145263672 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04665255546569824 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04858994483947754 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0.049721479415893555 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2364893 rank: 8, write(async) time: 0.0496068000793457 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2365446 rank: 7, write(async) time: 0.05017209053039551 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.04927968978881836 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05494570732116699 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2369328 rank: 11, write(async) time: 0.04975581169128418 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2418213 rank: 3, write(async) time: 0.055454254150390625 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05032944679260254 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05144906044006348 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2376416 rank: 10, write(async) time: 0.05074119567871094 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2425091 rank: 0, write(async) time: 0.05200552940368652 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05142378807067871 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05591773986816406 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2388108 rank: 15, write(async) time: 0.051895856857299805 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2427661 rank: 6, write(async) time: 0.05633974075317383 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:D2H and push, time: 0.05208230018615723 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541396.2394104 rank: 13, write(async) time: 0.05250048637390137 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, takes 1.5020370483398438e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, takes 2.2172927856445312e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 9, takes 1.5020370483398438e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 10, takes 1.71661376953125e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, takes 1.7404556274414062e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 11, takes 1.5974044799804688e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, takes 0.030148029327392578 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, takes 1.6927719116210938e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, takes 1.6927719116210938e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, takes 0.029630184173583984 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 9, takes 0.028817176818847656 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 10, takes 0.03137016296386719 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, takes 0.03162789344787598 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 11, takes 0.04046058654785156 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, takes 0.028689146041870117 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, takes 0.03021836280822754 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.async_utils:rank: 5, takes 1.6927719116210938e-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:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 7, takes 1.6927719116210938e-05 to finish D2H +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.async_utils:rank: 6, takes 1.621246337890625e-05 to finish D2H +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:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 1, takes 1.9550323486328125e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 2, takes 2.2172927856445312e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 3, takes 2.5033950805664062e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 5, takes 0.030405759811401367 to schedule async ckpt +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: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.async_utils:rank: 4, takes 1.7881393432617188e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 1, takes 0.02942490577697754 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 7, takes 0.038716793060302734 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 6, takes 0.03489804267883301 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 2, takes 0.028594017028808594 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 3, takes 0.031177759170532227 to schedule async ckpt +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.async_utils:rank: 4, takes 0.02883291244506836 to schedule async ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 0, takes 1.5974044799804688e-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: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:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 143360, before: 1692086272, after: 1692229632 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 241664, before: 1714057216, after: 1714298880 +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.async_utils:rank: 0, takes 0.03365492820739746 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: 1, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 9, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 8, 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: 11, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 3, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 12, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 4, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 10, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 13, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 5, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 14, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 7, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 6, joining self.process +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 15, joining self.process +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 consumed: 294912, before: 1691332608, after: 1691627520 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collecting worker results... +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 started +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 106254336, before: 1700741120, after: 1806995456 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 114233344, before: 1717440512, after: 1831673856 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 105988096, before: 1712996352, after: 1818984448 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110182400, before: 1732476928, after: 1842659328 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 114278400, before: 1705664512, after: 1819942912 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 106020864, before: 1701052416, after: 1807073280 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 114135040, before: 1732476928, after: 1846611968 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110088192, before: 1701052416, after: 1811140608 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110067712, before: 1712996352, after: 1823064064 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 110227456, before: 1700741120, after: 1810968576 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109318144, before: 1705664512, after: 1814982656 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 109944832, before: 1717440512, after: 1827385344 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 93446144, before: 1702645760, after: 1796091904 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.8492794, rank: 9, write(sync,parallel): 0.4944765567779541 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109117440, before: 1700507648, after: 1809625088 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 108769280, before: 1711792128, after: 1820561408 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.8509219, rank: 14, write(sync,parallel): 0.5027482509613037 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 80142336, before: 2007638016, after: 2087780352 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.8661096, rank: 15, write(sync,parallel): 0.5130569934844971 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 108990464, before: 1697370112, after: 1806360576 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 114364416, before: 1700503552, after: 1814867968 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.8764153, rank: 13, write(sync,parallel): 0.5145294666290283 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.877774, rank: 11, write(sync,parallel): 0.5080456733703613 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.57s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.8857462, rank: 10, write(sync,parallel): 0.5245614051818848 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.56s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.58s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.906377, rank: 5, write(sync,parallel): 0.4882216453552246 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.59s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.59s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 114212864, before: 1711792128, after: 1826004992 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.61s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.56s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 114253824, before: 1697370112, after: 1811623936 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 150827008, before: 1753513984, after: 1904340992 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.981525, rank: 7, write(sync,parallel): 0.5375828742980957 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541396.9869268, rank: 6, write(sync,parallel): 0.5413198471069336 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 140038144, before: 1722236928, after: 1862275072 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 147804160, before: 1753513984, after: 1901318144 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.62s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.62s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 152162304, before: 1722236928, after: 1874399232 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541397.0610635, rank: 8, write(sync,parallel): 0.6804425716400146 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541397.089616, rank: 12, write(sync,parallel): 0.7097141742706299 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.75s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.78s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212017152, before: 1691332608, after: 1903349760 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 206675968, before: 1702645760, after: 1909321728 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212430848, before: 1692086272, after: 1904517120 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212353024, before: 1714053120, after: 1926406144 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541397.2523339, rank: 3, write(sync,parallel): 0.8032364845275879 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541397.266473, rank: 4, write(sync,parallel): 0.7934322357177734 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541397.2666383, rank: 2, write(sync,parallel): 0.8213050365447998 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 212434944, before: 2007638016, after: 2220072960 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541397.2817323, rank: 1, write(sync,parallel): 0.8386623859405518 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.89s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.89s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.86s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.91s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750541397.3187394, rank: 0, write(sync,parallel): 0.8100254535675049 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.89s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.358741, 4, gather: 0.05474996566772461 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3587508, 5, gather: 0.4185044765472412 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3588352, 3, gather: 0.05862140655517578 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3588057, 6, gather: 0.3276185989379883 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.358859, 7, gather: 0.33531975746154785 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.359539, 9, gather: 0.4722015857696533 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3589282, 1, gather: 0.0425419807434082 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.359622, 12, gather: 0.23671817779541016 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3589694, 2, gather: 0.057381629943847656 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3596826, 10, gather: 0.4263875484466553 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3597128, 11, gather: 0.43857693672180176 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3598182, 13, gather: 0.43857836723327637 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3599043, 15, gather: 0.4571108818054199 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3599823, 14, gather: 0.47424888610839844 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3600802, 8, gather: 0.2649519443511963 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.3615236, 0, gather: 0.005007505416870117 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750541397.37151, metadata_write: 0.009841442108154297 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0698s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0719s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0736s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0172s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0573s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.3501s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.4333s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.3425s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2515s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.2790s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.4713s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.4885s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.4408s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.4532s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.4534s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.4867s + successfully saved checkpoint from iteration 10 to gpt-checkpoint [ t 1/4, p 1/1 ] +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.002473592758178711 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.0024971961975097656 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.0025119781494140625 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.002469778060913086 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.002451181411743164 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.0024673938751220703 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.0024623870849609375 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.0024781227111816406 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.002458333969116211 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.002486705780029297 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.002483367919921875 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.00251007080078125 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.0025014877319335938 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.0024840831756591797 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.002488851547241211 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.0024983882904052734 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([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor:batch tensor: loss_mask torch.Size([2, 24576]) + tokensbatch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576])torch.Size([2, 24576]) + +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor:batch tensor after cp: attention_mask tokenstorch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 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tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +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 .......................................: (3651.84, 3656.12) +---------------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on validation set | lm loss value: 1.094644E+01 | lm loss PPL: 5.675180E+04 | +---------------------------------------------------------------------------------------------------------------- +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +Evaluating on 1 samples +Evaluating iter 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torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: 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after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +batch tensor: tokens torch.Size([2, 24576]) +batch tensor: labels torch.Size([2, 24576]) +batch tensor: loss_mask torch.Size([2, 24576]) +batch tensor: attention_mask torch.Size([2, 1, 24576, 24576]) +batch tensor: position_ids torch.Size([2, 24576]) +batch tensor after cp: tokens torch.Size([2, 6144]) +batch tensor after cp: labels torch.Size([2, 6144]) +batch tensor after cp: loss_mask torch.Size([2, 6144]) +batch tensor after cp: attention_mask torch.Size([2, 1, 6144, 24576]) +batch tensor after cp: position_ids torch.Size([2, 6144]) +Start exporting trace 11 +Done exporting trace 11 +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +(min, max) time across ranks (ms): + evaluate .......................................: (675.63, 678.82) +---------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on test set | lm loss value: 1.094644E+01 | lm loss PPL: 5.675180E+04 | +---------------------------------------------------------------------------------------------------------- +Running ctx_length=16384, TP_SIZE=4, CP_SIZE=4, BATCH_SIZE=2 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 16384 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 16384 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written. +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +using world size: 16, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 4, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 16384 + 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 ..................... 4 + 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 ......................... 16384 + 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 ...................................... 16384 + 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 ...................... 4 + tensorboard_dir ................................. tensorboard-logs/ + tensorboard_log_interval ........................ 1 + tensorboard_queue_size .......................... 1000 + test_data_path .................................. None + test_mode ....................................... False + tiktoken_num_special_tokens ..................... 1000 + tiktoken_pattern ................................ None + tiktoken_special_tokens ......................... None + timing_log_level ................................ 0 + timing_log_option ............................... minmax + titles_data_path ................................ None + tokenizer_model ................................. None + tokenizer_type .................................. GPT2BPETokenizer + torch_fsdp2_reshard_after_forward ............... True + tp_comm_bootstrap_backend ....................... nccl + tp_comm_bulk_dgrad .............................. True + tp_comm_bulk_wgrad .............................. True + tp_comm_overlap ................................. False + tp_comm_overlap_ag .............................. True + tp_comm_overlap_cfg ............................. None + tp_comm_overlap_rs .............................. True + tp_comm_overlap_rs_dgrad ........................ False + tp_comm_split_ag ................................ True + tp_comm_split_rs ................................ True + train_data_path ................................. None + train_iters ..................................... 10 + train_samples ................................... None + train_sync_interval ............................. None + transformer_impl ................................ transformer_engine + transformer_pipeline_model_parallel_size ........ 1 + untie_embeddings_and_output_weights ............. False + use_checkpoint_args ............................. False + use_checkpoint_opt_param_scheduler .............. False + use_cpu_initialization .......................... None + use_custom_fsdp ................................. False + use_dist_ckpt ................................... True + use_dist_ckpt_deprecated ........................ False + use_distributed_optimizer ....................... False + use_flash_attn .................................. False + use_legacy_models ............................... False + use_mp_args_from_checkpoint_args ................ False + use_one_sent_docs ............................... False + use_persistent_ckpt_worker ...................... False + use_precision_aware_optimizer ................... False + use_pytorch_profiler ............................ False + use_ring_exchange_p2p ........................... False + use_rope_scaling ................................ False + use_rotary_position_embeddings .................. False + use_sharp ....................................... False + use_tokenizer_model_from_checkpoint_args ........ True + use_torch_fsdp2 ................................. False + use_torch_optimizer_for_cpu_offload ............. False + use_tp_pp_dp_mapping ............................ False + v_head_dim ...................................... 128 + valid_data_path ................................. None + variable_seq_lengths ............................ False + virtual_pipeline_model_parallel_size ............ None + vision_backbone_type ............................ vit + vision_pretraining .............................. False + vision_pretraining_type ......................... classify + vocab_extra_ids ................................. 0 + vocab_file ...................................... vocab.json + vocab_size ...................................... None + wandb_exp_name .................................. + wandb_project ................................... + wandb_save_dir .................................. + weight_decay .................................... 0.1 + weight_decay_incr_style ......................... constant + wgrad_deferral_limit ............................ 0 + world_size ...................................... 16 + yaml_cfg ........................................ None +-------------------- end of arguments --------------------- +INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1 +> building GPT2BPETokenizer tokenizer ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 + > padded vocab (size: 50257) with 431 dummy tokens (new size: 50688) +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED +> initializing torch distributed ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +> initialized tensor model parallel with size 4 +> 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: 0.046 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.479 seconds +time to initialize megatron (seconds): 8.270 +[after megatron is initialized] datetime: 2025-06-21 21:30:40 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 207162368 +>>> embedding>>> embedding +>>> embedding +>>> decoder +>>> output_layer + +>>> decoder +>>> decoder>>> output_layer + +>>> embedding>>> embedding + +>>> decoder>>> decoder + +>>> output_layer +>>> output_layer +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 207162368 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 207162368 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 207162368 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 207162368 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 207162368 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 207162368 +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 (207162368 elements, 207162368 padded size): + 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.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.final_layernorm.weight + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.embedding.position_embeddings.weight + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.self_attention.linear_qkv.weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.embedding.word_embeddings.weight +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='') +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 207162368 +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +(TP, PP, encoder TP, encoder PP) mismatch after resume ((4, 1, 0, 0) vs (8, 1, 0, 0) from checkpoint): RNG state will be ignored +(TP, PP, encoder TP, encoder PP) mismatch after resume ((4, 1, 0, 0) vs (8, 1, 0, 0) from checkpoint): Rerun state will be ignored + loading distributed checkpoint from gpt-checkpoint at iteration 10 +Running ctx_length=24576, TP_SIZE=4, CP_SIZE=4, BATCH_SIZE=2 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 24576 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 24576 +TP_SIZE: 4 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +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: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 4, 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 ------------------------ +INFO:megatron.training.initialize:Setting logging level to 0 + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 24576 + encoder_tensor_model_parallel_size .............. 0 + end_weight_decay ................................ 0.1 + eod_mask_loss ................................... False + error_injection_rate ............................ 0 + error_injection_type ............................ transient_error + eval_interval ................................... 16 + eval_iters ...................................... 1 + evidence_data_path .............................. None + exit_duration_in_mins ........................... None + exit_interval ................................... None + exit_on_missing_checkpoint ...................... False + exit_signal_handler ............................. False + exp_avg_dtype ................................... torch.float32 + exp_avg_sq_dtype ................................ torch.float32 + expert_model_parallel_size ...................... 1 + expert_tensor_parallel_size ..................... 4 + 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 ......................... 24576 + max_tokens_to_oom ............................... 12000 + memory_snapshot_path ............................ snapshot.pickle + merge_file ...................................... merges.txt + micro_batch_size ................................ 1 + microbatch_group_size_per_vp_stage .............. None + mid_level_dataset_surplus ....................... 0.005 + min_loss_scale .................................. 1.0 + min_lr .......................................... 0.0 + mlp_chunks_for_prefill .......................... 1 + mmap_bin_files .................................. True + mock_data ....................................... True + moe_apply_probs_on_input ........................ False + moe_aux_loss_coeff .............................. 0.0 + moe_enable_deepep ............................... False + moe_expert_capacity_factor ...................... None + moe_extended_tp ................................. False + moe_ffn_hidden_size ............................. None + moe_grouped_gemm ................................ False + moe_input_jitter_eps ............................ None + moe_layer_freq .................................. 1 + moe_layer_recompute ............................. False + moe_pad_expert_input_to_capacity ................ False + moe_per_layer_logging ........................... False + moe_permute_fusion .............................. False + moe_router_bias_update_rate ..................... 0.001 + moe_router_dtype ................................ None + moe_router_enable_expert_bias ................... False + moe_router_force_load_balancing ................. False + moe_router_group_topk ........................... None + moe_router_load_balancing_type .................. aux_loss + moe_router_num_groups ........................... None + moe_router_padding_for_fp8 ...................... False + moe_router_pre_softmax .......................... False + moe_router_score_function ....................... softmax + moe_router_topk ................................. 2 + moe_router_topk_scaling_factor .................. None + moe_shared_expert_intermediate_size ............. None + moe_shared_expert_overlap ....................... False + moe_token_dispatcher_type ....................... allgather + moe_token_drop_policy ........................... probs + moe_use_legacy_grouped_gemm ..................... False + moe_use_upcycling ............................... False + moe_z_loss_coeff ................................ None + mrope_section ................................... None + mscale .......................................... 1.0 + mscale_all_dim .................................. 1.0 + mtp_loss_scaling_factor ......................... 0.1 + mtp_num_layers .................................. None + multi_latent_attention .......................... False + nccl_all_reduce_for_prefill ..................... False + nccl_communicator_config_path ................... None + nccl_ub ......................................... False + no_load_optim ................................... None + no_load_rng ..................................... None + no_persist_layer_norm ........................... False + no_rope_freq .................................... None + no_save_optim ................................... None + no_save_rng ..................................... None + non_persistent_ckpt_type ........................ None + non_persistent_global_ckpt_dir .................. None + non_persistent_local_ckpt_algo .................. fully_parallel + non_persistent_local_ckpt_dir ................... None + non_persistent_save_interval .................... None + norm_epsilon .................................... 1e-05 + normalization ................................... LayerNorm + num_attention_heads ............................. 64 + num_channels .................................... 3 + num_classes ..................................... 1000 + num_dataset_builder_threads ..................... 1 + num_distributed_optimizer_instances ............. 1 + num_experts ..................................... None + num_layers ...................................... 2 + num_layers_at_end_in_bf16 ....................... 1 + num_layers_at_start_in_bf16 ..................... 1 + num_layers_per_virtual_pipeline_stage ........... None + num_query_groups ................................ 16 + num_virtual_stages_per_pipeline_rank ............ None + num_workers ..................................... 2 + object_storage_cache_path ....................... None + one_logger_async ................................ False + one_logger_project .............................. megatron-lm + one_logger_run_name ............................. None + onnx_safe ....................................... None + openai_gelu ..................................... False + optimizer ....................................... adam + optimizer_cpu_offload ........................... False + optimizer_offload_fraction ...................... 1.0 + output_bert_embeddings .......................... False + overlap_cpu_optimizer_d2h_h2d ................... False + overlap_grad_reduce ............................. False + overlap_p2p_comm ................................ False + overlap_p2p_comm_warmup_flush ................... False + overlap_param_gather ............................ False + overlap_param_gather_with_optimizer_step ........ False + override_opt_param_scheduler .................... False + params_dtype .................................... torch.float16 + patch_dim ....................................... 16 + per_split_data_args_path ........................ None + perform_initialization .......................... True + pin_cpu_grads ................................... True + pin_cpu_params .................................. True + pipeline_model_parallel_comm_backend ............ None + pipeline_model_parallel_size .................... 1 + pipeline_model_parallel_split_rank .............. None + position_embedding_type ......................... learned_absolute + pretrained_checkpoint ........................... None + profile ......................................... False + profile_ranks ................................... [0] + profile_step_end ................................ 12 + profile_step_start .............................. 10 + q_lora_rank ..................................... None + qk_head_dim ..................................... 128 + qk_l2_norm ...................................... False + qk_layernorm .................................... False + qk_pos_emb_head_dim ............................. 64 + query_in_block_prob ............................. 0.1 + rampup_batch_size ............................... None + rank ............................................ 0 + recompute_granularity ........................... None + recompute_method ................................ None + recompute_modules ............................... None + recompute_num_layers ............................ None + record_memory_history ........................... False + relative_attention_max_distance ................. 128 + relative_attention_num_buckets .................. 32 + replication ..................................... False + replication_factor .............................. 2 + replication_jump ................................ None + rerun_mode ...................................... disabled + reset_attention_mask ............................ False + reset_position_ids .............................. False + result_rejected_tracker_filename ................ None + retriever_report_topk_accuracies ................ [] + retriever_score_scaling ......................... False + retriever_seq_length ............................ 256 + retro_add_retriever ............................. False + retro_attention_gate ............................ 1 + retro_cyclic_train_iters ........................ None + retro_encoder_attention_dropout ................. 0.1 + retro_encoder_hidden_dropout .................... 0.1 + retro_encoder_layers ............................ 2 + retro_num_neighbors ............................. 2 + retro_num_retrieved_chunks ...................... 2 + retro_project_dir ............................... None + retro_verify_neighbor_count ..................... True + rope_scaling_factor ............................. 8.0 + rotary_base ..................................... 10000 + rotary_interleaved .............................. False + rotary_percent .................................. 1.0 + rotary_scaling_factor ........................... 1.0 + rotary_seq_len_interpolation_factor ............. None + run_workload_inspector_server ................... False + sample_rate ..................................... 1.0 + save ............................................ gpt-checkpoint + save_interval ................................... 16 + scatter_gather_tensors_in_pipeline .............. True + seed ............................................ 1234 + seq_length ...................................... 24576 + sequence_parallel ............................... False + sgd_momentum .................................... 0.9 + short_seq_prob .................................. 0.1 + skip_train ...................................... False + skipped_train_samples ........................... 0 + spec ............................................ None + split ........................................... None + squared_relu .................................... False + start_weight_decay .............................. 0.1 + straggler_ctrlr_port ............................ 65535 + straggler_minmax_count .......................... 1 + suggested_communication_unit_size ............... None + swiglu .......................................... False + swin_backbone_type .............................. tiny + symmetric_ar_type ............................... None + te_rng_tracker .................................. False + tensor_model_parallel_size ...................... 4 + tensorboard_dir ................................. tensorboard-logs/ + tensorboard_log_interval ........................ 1 + tensorboard_queue_size .......................... 1000 + test_data_path .................................. None + test_mode ....................................... False + tiktoken_num_special_tokens ..................... 1000 + tiktoken_pattern ................................ None + tiktoken_special_tokens ......................... None + timing_log_level ................................ 0 + timing_log_option ............................... minmax + titles_data_path ................................ None + tokenizer_model ................................. None + tokenizer_type .................................. GPT2BPETokenizer + torch_fsdp2_reshard_after_forward ............... True + tp_comm_bootstrap_backend ....................... nccl + tp_comm_bulk_dgrad .............................. True + tp_comm_bulk_wgrad .............................. True + tp_comm_overlap ................................. False + tp_comm_overlap_ag .............................. True + tp_comm_overlap_cfg ............................. None + tp_comm_overlap_rs .............................. True + tp_comm_overlap_rs_dgrad ........................ False + tp_comm_split_ag ................................ True + tp_comm_split_rs ................................ True + train_data_path ................................. None + train_iters ..................................... 10 + train_samples ................................... None + train_sync_interval ............................. None + transformer_impl ................................ transformer_engine + transformer_pipeline_model_parallel_size ........ 1 + untie_embeddings_and_output_weights ............. False + use_checkpoint_args ............................. False + use_checkpoint_opt_param_scheduler .............. False + use_cpu_initialization .......................... None + use_custom_fsdp ................................. False + use_dist_ckpt ................................... True + use_dist_ckpt_deprecated ........................ False + use_distributed_optimizer ....................... False + use_flash_attn .................................. False + use_legacy_models ............................... False + use_mp_args_from_checkpoint_args ................ False + use_one_sent_docs ............................... False + use_persistent_ckpt_worker ...................... False + use_precision_aware_optimizer ................... False + use_pytorch_profiler ............................ False + use_ring_exchange_p2p ........................... False + use_rope_scaling ................................ False + use_rotary_position_embeddings .................. False + use_sharp ....................................... False + use_tokenizer_model_from_checkpoint_args ........ True + use_torch_fsdp2 ................................. False + use_torch_optimizer_for_cpu_offload ............. False + use_tp_pp_dp_mapping ............................ False + v_head_dim ...................................... 128 + valid_data_path ................................. None + variable_seq_lengths ............................ False + virtual_pipeline_model_parallel_size ............ None + vision_backbone_type ............................ vit + vision_pretraining .............................. False + vision_pretraining_type ......................... classify + vocab_extra_ids ................................. 0 + vocab_file ...................................... vocab.json + vocab_size ...................................... None + wandb_exp_name .................................. + wandb_project ................................... + wandb_save_dir .................................. + weight_decay .................................... 0.1 + weight_decay_incr_style ......................... constant + wgrad_deferral_limit ............................ 0 + world_size ...................................... 16 + yaml_cfg ........................................ None +-------------------- end of arguments --------------------- +INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1 +> building GPT2BPETokenizer tokenizer ... +INFO:megatron.training.initialize:Setting logging level to 0 + > padded vocab (size: 50257) with 431 dummy tokens (new size: 50688) +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED +> initializing torch distributed ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written. +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +> initialized tensor model parallel with size 4 +> 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: 0.052 seconds +WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations. +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 2.349 seconds +time to initialize megatron (seconds): 8.143 +[after megatron is initialized] datetime: 2025-06-21 21:31:18 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 240716800 +>>> embedding>>> embedding + +>>> decoder>>> decoder + +>>> output_layer>>> output_layer + +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 240716800 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 240716800 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 240716800 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 240716800 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 240716800 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 240716800 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 240716800 +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 (240716800 elements, 240716800 padded size): + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.embedding.word_embeddings.weight + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.final_layernorm.weight + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.self_attention.linear_qkv.weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.embedding.position_embeddings.weight +INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=, config_logger_dir='') +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (3.56, 4.06) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:31:20 +> building train, validation, and test datasets ... + > datasets target sizes (minimum size): + train: 10 + validation: 1 + test: 1 +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)] +> building train, validation, and test datasets for GPT ... +INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=24576, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=, 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: 0.004996 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 2774 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.001765 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 2773 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.001512 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 2778 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 21:31:20 +done with setup ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (1497.79, 1525.44) + train/valid/test-data-iterators-setup ..........: (17.33, 123.43) +training ... +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 21:31:20