diff --git "a/attnserver.run_attnserver.slurm.sh.343374.out.log" "b/attnserver.run_attnserver.slurm.sh.343374.out.log" new file mode 100644--- /dev/null +++ "b/attnserver.run_attnserver.slurm.sh.343374.out.log" @@ -0,0 +1,10445 @@ +Running ctx_length=1024, TP_SIZE=8, CP_SIZE=8, BATCH_SIZE=1 +Cleaning up checkpoint directory: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +CP_SIZE: 8 +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +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: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +Cleaning up checkpoint directory: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +Cleaning up checkpoint directory: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +Cleaning up checkpoint directory: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +Cleaning up checkpoint directory: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +CP_SIZE: 8 +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +CP_SIZE: 8 +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +-------------------------------- +CP_SIZE: 8 +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +CP_SIZE: 8 +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +CP_SIZE: 8 +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +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 +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +CP_SIZE: 8 +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +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: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +-------------------------------- +CTX_LENGTH: 1024 +TP_SIZE: 8 +CP_SIZE: 8 +CHECKPOINT_PATH: /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs +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 +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 +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 +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: 64, data-parallel size: 1, context-parallel size: 8, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 8, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 8 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 1024 + encoder_tensor_model_parallel_size .............. 0 + end_weight_decay ................................ 0.1 + eod_mask_loss ................................... False + error_injection_rate ............................ 0 + error_injection_type ............................ transient_error + eval_interval ................................... 16 + eval_iters ...................................... 1 + evidence_data_path .............................. None + exit_duration_in_mins ........................... None + exit_interval ................................... None + exit_on_missing_checkpoint ...................... False + exit_signal_handler ............................. False + exp_avg_dtype ................................... torch.float32 + exp_avg_sq_dtype ................................ torch.float32 + expert_model_parallel_size ...................... 1 + expert_tensor_parallel_size ..................... 8 + external_cuda_graph ............................. False + ffn_hidden_size ................................. 16384 + finetune ........................................ False + first_last_layers_bf16 .......................... False + flash_decode .................................... False + fp16 ............................................ True + fp16_lm_cross_entropy ........................... False + fp32_residual_connection ........................ False + fp8 ............................................. None + fp8_amax_compute_algo ........................... most_recent + fp8_amax_history_len ............................ 1 + fp8_interval .................................... 1 + fp8_margin ...................................... 0 + fp8_param_gather ................................ False + fp8_recipe ...................................... delayed + fp8_wgrad ....................................... True + fsdp_double_buffer .............................. False + global_batch_size ............................... 1 +INFO:megatron.training.initialize:Setting logging level to 0 + 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 ..................... 64 + 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 ............................................ /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs + 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 ............................................ /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs + 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 ...................... 8 + tensorboard_dir ................................. tensorboard-logs/ + tensorboard_log_interval ........................ 1 + tensorboard_queue_size .......................... 1000 + test_data_path .................................. None + test_mode ....................................... False + tiktoken_num_special_tokens ..................... 1000 + tiktoken_pattern ................................ None + tiktoken_special_tokens ......................... None + timing_log_level ................................ 0 + timing_log_option ............................... minmax + titles_data_path ................................ None + tokenizer_model ................................. None + tokenizer_type .................................. GPT2BPETokenizer + torch_fsdp2_reshard_after_forward ............... True + tp_comm_bootstrap_backend ....................... nccl + tp_comm_bulk_dgrad .............................. True + tp_comm_bulk_wgrad .............................. True + tp_comm_overlap ................................. False + tp_comm_overlap_ag .............................. True + tp_comm_overlap_cfg ............................. None + tp_comm_overlap_rs .............................. True + tp_comm_overlap_rs_dgrad ........................ False + tp_comm_split_ag ................................ True + tp_comm_split_rs ................................ True + train_data_path ................................. None + train_iters ..................................... 10 + train_samples ................................... None + train_sync_interval ............................. None + transformer_impl ................................ transformer_engine + transformer_pipeline_model_parallel_size ........ 1 + untie_embeddings_and_output_weights ............. False + use_checkpoint_args ............................. False + use_checkpoint_opt_param_scheduler .............. False + use_cpu_initialization .......................... None + use_custom_fsdp ................................. False + use_dist_ckpt ................................... True + use_dist_ckpt_deprecated ........................ False + use_distributed_optimizer ....................... False + use_flash_attn .................................. False + use_legacy_models ............................... False + use_mp_args_from_checkpoint_args ................ False + use_one_sent_docs ............................... False + use_persistent_ckpt_worker ...................... False + use_precision_aware_optimizer ................... False + use_pytorch_profiler ............................ False + use_ring_exchange_p2p ........................... False + use_rope_scaling ................................ False + use_rotary_position_embeddings .................. False + use_sharp ....................................... False + use_tokenizer_model_from_checkpoint_args ........ True + use_torch_fsdp2 ................................. False + use_torch_optimizer_for_cpu_offload ............. False + use_tp_pp_dp_mapping ............................ False + v_head_dim ...................................... 128 + valid_data_path ................................. None + variable_seq_lengths ............................ False + virtual_pipeline_model_parallel_size ............ None + vision_backbone_type ............................ vit + vision_pretraining .............................. False + vision_pretraining_type ......................... classify + vocab_extra_ids ................................. 0 + vocab_file ...................................... vocab.json + vocab_size ...................................... None + wandb_exp_name .................................. + wandb_project ................................... + wandb_save_dir .................................. + weight_decay .................................... 0.1 + weight_decay_incr_style ......................... constant + wgrad_deferral_limit ............................ 0 + world_size ...................................... 64 + 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 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 + > padded vocab (size: 50257) with 943 dummy tokens (new size: 51200) +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED +> initializing torch distributed ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +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 +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 +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 +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 8 +> initialized pipeline model parallel with size 1 +> setting random seeds to 1234 ... +INFO:megatron.training.initialize:Setting logging level to 0 +> 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: 0.077 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 6.493 seconds +time to initialize megatron (seconds): 18.023 +[after megatron is initialized] datetime: 2025-06-21 22:42:02 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 +>>> decoder + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 +>>> embedding +>>> embedding +>>> decoder +>>> embedding>>> output_layer + +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> decoder +>>> output_layer +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 +>>> embedding>>> embedding + +>>> decoder +>>> decoder +>>> output_layer +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 +>>> 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 (5, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 +>>> embedding +>>> decoder + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 74511872 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +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) +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 74511872 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 74511872 +INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1 +Params for bucket 1 (74511872 elements, 74511872 padded size): + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.embedding.word_embeddings.weight + module.decoder.final_layernorm.weight + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.embedding.position_embeddings.weight + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 74511872 + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.final_layernorm.bias + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.self_attention.linear_qkv.weight + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 74511872 +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='') + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 74511872 +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +WARNING: could not find the metadata file /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (6.51, 6.71) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 22:42:02 +> 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: 0.006826 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: 0.003538 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: 0.003497 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 22:42:02 +done with setup ... +training ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (394.20, 469.67) + train/valid/test-data-iterators-setup ..........: (23.67, 170.72) +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 22:42:02 +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens + batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) attention_mask + torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: position_idslabels torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024]) + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: batch tensor:tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: labelsloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: attention_maskloss_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: position_ids batch tensor:torch.Size([1, 1024]) +attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024]) + batch tensor:tokens position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) 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tensor:batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])tokens + batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: loss_mask torch.Size([1, 1024])tokens + batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: +position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens batch tensor: torch.Size([1, 1024])tokens +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor: labels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: loss_mask batch tensor:torch.Size([1, 1024]) +labels batch tensor:torch.Size([1, 1024]) +attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +position_ids torch.Size([1, 1024])batch tensor: +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokensposition_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: position_ids torch.Size([1, 128])tokens + torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels batch tensor after cp:torch.Size([1, 128]) + batch tensor after cp:tokens loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) 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cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: position_ids position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens batch tensor after cp: torch.Size([1, 128])tokens +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor after cp:batch tensor after cp: labels torch.Size([1, 128])tokenstorch.Size([1, 128]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp:batch tensor after cp:batch tensor after cp: loss_maskattention_masklabels torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp:batch tensor after cp: attention_maskposition_idsloss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp:batch tensor after cp: position_idsattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokens tokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:position_ids position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: tokens torch.Size([1, 128])tokens +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp: labels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_maskbatch tensor after cp: torch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128]) +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])loss_mask + batch tensor after cp:torch.Size([1, 128]) +position_ids batch tensor after cp: torch.Size([1, 128])attention_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Start exporting trace 0 +Done exporting trace 0 +Number of parameters in transformer block in billions: 0.35 +Number of parameters in embedding layers in billions: 0.21 +Total number of parameters in billions: 0.56 +Number of parameters in most loaded shard in billions: 0.0703 +Theoretical memory footprints: weight and optimizer=1206.09 MB +[Rank 5] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 0] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 6] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 3] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 7] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 1] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 16] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 40] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 43] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 49] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 48] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 52] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 32] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 34] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 33] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 12] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 10] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 8] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 + [2025-06-21 22:42:17] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 14667.1 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[Rank 58] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 62] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 61] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 59] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 60] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 26] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0[Rank 29] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 + +[Rank 24] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 31] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 4] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 19] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0[Rank 23] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 + +[Rank 46] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 47] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 54] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 37] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 14] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 56] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 57] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 63] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 28] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +[Rank 20] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 44] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0[Rank 41] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 + +[Rank 50] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 53] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 38] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 11] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +[Rank 25] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +[Rank 21] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 42] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 51] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 36] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 9] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 15] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +batch tensor:batch tensor: batch tensor: tokenstokens tokens torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024]) + + +[Rank 30] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +batch tensor: tokens torch.Size([1, 1024]) +[Rank 18] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 17] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 22] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +[Rank 45] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 55] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 39] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +[Rank 13] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +batch tensor:batch tensor: batch tensor: labelslabelsbatch tensor:labels torch.Size([1, 1024])torch.Size([1, 1024])tokens +[Rank 27] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1090.0 | max reserved: 1090.0 +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +tokens batch tensor: labels torch.Size([1, 1024]) +[Rank 35] (after 1 iterations) memory (MB) | allocated: 991.8828125 | max allocated: 991.8837890625 | reserved: 1088.0 | max reserved: 1088.0 +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + batch tensor: + batch tensor:loss_maskloss_mask loss_masktorch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])torch.Size([1, 1024])batch tensor:batch tensor:batch tensor: + +batch tensor: tokens batch tensor: tokens torch.Size([1, 1024]) + batch tensor:tokens attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 1024]) +labels batch tensor:torch.Size([1, 1024]) +attention_maskbatch tensor: loss_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + attention_mask batch tensor:attention_mask batch tensor:tokenstorch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) +attention_mask +batch tensor: torch.Size([1, 1024])labels + torch.Size([1, 1024]) +batch tensor: batch tensor:labels loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_mask batch tensor:torch.Size([1, 1024]) +attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) + position_idsbatch tensor: labelstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: batch tensor:position_ids torch.Size([1, 1024])attention_mask + torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor: labelsbatch tensor:torch.Size([1, 1, 1024, 1024])position_ids torch.Size([1, 1024]) +position_ids batch tensor: + torch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])position_idsbatch tensor:batch tensor: +position_idsbatch tensor: torch.Size([1, 1024])position_ids + torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labelsbatch tensor: torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor:batch tensor: labelslabelstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) + labelsloss_masktorch.Size([1, 1024]) torch.Size([1, 1024]) +torch.Size([1, 1024]) + +batch tensor: batch tensor:loss_mask attention_maskbatch tensor after cp: torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])tokens +batch tensor: tokensbatch tensor: batch tensor: tokens tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor after cp:tokens loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024]) torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor:batch tensor: batch tensor: attention_maskattention_mask labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024])batch tensor: + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])tokens +batch tensor: attention_maskbatch tensor: torch.Size([1, 128]) +torch.Size([1, 1, 1024, 1024])position_idsbatch tensor after cp: + labelsbatch tensor:torch.Size([1, 1024]) +position_idstorch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor: + torch.Size([1, 1024])labels + batch tensor:torch.Size([1, 1024]) batch tensor: +labels labelsbatch tensor: torch.Size([1, 1024]) torch.Size([1, 1024]) +batch tensor:batch tensor:batch tensor after cp: tokens tokenstokens torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + labels torch.Size([1, 1024])batch tensor:torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1024]) + +batch tensor after cp: position_ids batch tensor:torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + + batch tensor:batch tensor:batch tensor:position_ids position_ids torch.Size([1, 1024])loss_masktokenstorch.Size([1, 1024]) + +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + + batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens batch tensor after cp:batch tensor after cp:torch.Size([1, 1024]) tokenstokens +loss_mask + batch tensor:batch tensor:torch.Size([1, 1024]) +loss_maskloss_mask batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +attention_mask batch tensor:batch tensor: torch.Size([1, 1, 1024, 1024]) attention_mask +attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1, 1024, 1024]) +position_ids +batch tensor: batch tensor: torch.Size([1, 1024]) position_ids + labelsbatch tensor after cp: batch tensor:torch.Size([1, 1024])loss_mask + torch.Size([1, 128])batch tensor:labels + batch tensor after cp:loss_masktorch.Size([1, 1024]) batch tensor after cp: +batch tensor: position_ids torch.Size([1, 1024]) +labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor:batch tensor:batch tensor: position_ids labelstorch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + torch.Size([1, 128])torch.Size([1, 128])batch tensor: + +position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +attention_mask batch tensor:tokenstorch.Size([1, 1024]) loss_mask + torch.Size([1, 1, 128, 1024])batch tensor:torch.Size([1, 1024])batch tensor:torch.Size([1, 128]) + + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokens tokens torch.Size([1, 1024]) torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor after cp:labelsbatch tensor after cp: labelstorch.Size([1, 1024])labels +torch.Size([1, 128]) batch tensor after cp: +batch tensor: torch.Size([1, 128])batch tensor after cp: tokensloss_mask + batch tensor after cp: torch.Size([1, 1024])torch.Size([1, 128])loss_maskbatch tensor after cp: + batch tensor after cp: +torch.Size([1, 128]) loss_mask +batch tensor: tokens torch.Size([1, 1024]) + batch tensor after cp:tokensbatch tensor:attention_mask batch tensor after cp:position_ids labels attention_mask torch.Size([1, 1, 1024, 1024]) torch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 1024]) +batch tensor after cp:torch.Size([1, 1, 1024, 1024])batch tensor: + batch tensor: +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: loss_masklabels torch.Size([1, 1024]) torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024])batch tensor: + + batch tensor:attention_maskbatch tensor:tokens loss_masklabels torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +labelsbatch tensor:batch tensor after cp: tokensattention_mask torch.Size([1, 128]) + torch.Size([1, 128])attention_masktorch.Size([1, 1, 128, 1024]) + +batch tensor after cp:torch.Size([1, 1, 1024, 1024])torch.Size([1, 128]) + +batch tensor: labels torch.Size([1, 1024]) + batch tensor:batch tensor:loss_mask position_idsposition_ids labels tokens torch.Size([1, 1024])torch.Size([1, 128])torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: batch tensor after cp: position_idsbatch tensor after cp: batch tensor:attention_masktorch.Size([1, 128]) loss_mask +torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])labelstokens position_ids +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + + + +torch.Size([1, 1024])batch tensor after cp:batch tensor: + attention_maskloss_mask batch tensor: torch.Size([1, 1, 128, 1024]) torch.Size([1, 1024]) +labels +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:position_idsattention_mask batch tensor: loss_masktorch.Size([1, 1024])labels +torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids batch tensor:torch.Size([1, 128]) +batch tensor after cp: torch.Size([1, 128]) batch tensor after cp: torch.Size([1, 128]) +torch.Size([1, 1024])position_idsattention_mask + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:tokens labels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: batch tensor: torch.Size([1, 1024])attention_maskposition_ids + batch tensor:torch.Size([1, 128])torch.Size([1, 1, 1024, 1024]) + +loss_mask batch tensor:torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: batch tensor: position_ids loss_mask attention_mask torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + + +batch tensor: batch tensor:attention_mask position_idstorch.Size([1, 1, 1024, 1024]) + torch.Size([1, 1024])batch tensor: + position_ids torch.Size([1, 1024]) + tokens batch tensor:torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp: torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +loss_maskbatch tensor after cp:labels position_idstorch.Size([1, 128]) torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: +batch tensor after cp:batch tensor after cp: loss_mask labelstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:attention_mask loss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: position_idsattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_masktokens torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor:tokens labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + + batch tensor after cp:attention_mask batch tensor:torch.Size([1, 1, 128, 1024])loss_mask + torch.Size([1, 128])batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])labels + batch tensor after cp:torch.Size([1, 128]) +position_idsbatch tensor after cp: torch.Size([1, 128])loss_mask +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: batch tensor:attention_mask labels torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor:batch tensor: position_idsloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + + tokensbatch tensor after cp:position_ids attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])batch tensor after cp: position_ids +torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: batch tensor after cp: attention_mask tokenstokenstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])torch.Size([1, 128])batch tensor after cp: + +position_idsbatch tensor after cp: batch tensor after cp: torch.Size([1, 128]) labels +batch tensor: position_idsbatch tensor: torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +labels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokensbatch tensor after cp: torch.Size([1, 128])tokens + batch tensor after cp:torch.Size([1, 128]) +labels batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +labelsbatch tensor after cp: torch.Size([1, 128])loss_mask + batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])tokens +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:loss_mask torch.Size([1, 128])tokens +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_mask batch tensor after cp:batch tensor:torch.Size([1, 128]) +attention_mask batch tensor after cp:batch tensor after cp:tokenstorch.Size([1, 1, 128, 1024]) +attention_masktokens batch tensor after cp: torch.Size([1, 1, 128, 1024])position_ids +torch.Size([1, 128]) batch tensor after cp: +torch.Size([1, 128]) batch tensor after cp:position_ids + labelstorch.Size([1, 128]) torch.Size([1, 1024]) +torch.Size([1, 128]) + + batch tensor after cp: torch.Size([1, 128])batch tensor after cp:position_ids +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor after cp: torch.Size([1, 128])attention_mask +torch.Size([1, 1, 128, 1024])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 128]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: batch tensor:loss_mask labelstorch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + batch tensor after cp:torch.Size([1, 128])tokens +labels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:batch tensor after cp: tokensposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: batch tensor after cp: tokensloss_mask tokenstorch.Size([1, 128]) torch.Size([1, 128]) + +torch.Size([1, 128])batch tensor after cp:batch tensor after cp: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor:attention_mask loss_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])batch tensor after cp: + position_idsbatch tensor: torch.Size([1, 128])attention_mask + torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + labelsbatch tensor after cp:attention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) +labels +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: attention_maskloss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: torch.Size([1, 128]) +loss_maskposition_idsbatch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])loss_mask + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor after cp: torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + position_idsbatch tensor after cp: torch.Size([1, 128])attention_mask + torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: loss_maskbatch tensor after cp: torch.Size([1, 128])tokens +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])position_ids + batch tensor after cp:torch.Size([1, 128]) +batch tensor:batch tensor after cp: tokensbatch tensor after cp: torch.Size([1, 128])tokens +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor after cp: torch.Size([1, 128])attention_mask +torch.Size([1, 1, 128, 1024])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 128]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +tokenstorch.Size([1, 128])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: attention_mask tokenstorch.Size([1, 1, 128, 1024]) +batch tensor after cp:torch.Size([1, 128]) +position_ids batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +loss_maskbatch tensor after cp: loss_masktorch.Size([1, 128]) +torch.Size([1, 1024])torch.Size([1, 128])batch tensor after cp: + + batch tensor after cp:attention_mask attention_maskbatch tensor: torch.Size([1, 1, 128, 1024])labels +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])batch tensor after cp: +batch tensor after cp: position_idsbatch tensor:position_ids torch.Size([1, 128])torch.Size([1, 128])loss_mask + + torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Start exporting trace 1 +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels batch tensor after cp:torch.Size([1, 128]) +batch tensor:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +Done exporting trace 1 +batch tensor after cp: labels torch.Size([1, 128]) + tokensbatch tensor after cp: loss_masktorch.Size([1, 128]) +torch.Size([1, 128]) +labels batch tensor: torch.Size([1, 1024])labels + batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024])batch tensor: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels attention_mask torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + batch tensor after cp:loss_mask position_idstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + attention_maskbatch tensor: attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + attention_mask torch.Size([1, 1, 128, 1024]) + position_idsbatch tensor: position_idstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokens tokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:loss_mask loss_masktorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:attention_mask attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor after cp:position_ids position_ids torch.Size([1, 128]) +torch.Size([1, 128]) + [2025-06-21 22:42:17] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 95.1 | 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([1, 1024])batch tensor: +batch tensor: batch tensor:tokens tokens labelsbatch tensor: torch.Size([1, 1024]) torch.Size([1, 1024]) +tokenstorch.Size([1, 1024]) +batch tensor: +batch tensor:batch tensor:batch tensor: batch tensor: torch.Size([1, 1024]) loss_mask tokens tokens + labelslabels torch.Size([1, 1024])batch tensor: +torch.Size([1, 1024]) torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024])batch tensor:labelsbatch tensor: + + + + batch tensor: attention_maskbatch tensor: batch tensor:batch tensor:torch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024])labelstokensbatch tensor: +loss_mask + loss_masklabels batch tensor:batch tensor:torch.Size([1, 1024]) torch.Size([1, 1024]) torch.Size([1, 1024]) +tokens +torch.Size([1, 1024])torch.Size([1, 1024]) +loss_maskposition_idsbatch tensor: + +batch tensor: batch tensor: batch tensor:batch tensor: torch.Size([1, 1024])torch.Size([1, 1024]) torch.Size([1, 1024])loss_mask labels + +attention_mask attention_mask +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) + batch tensor:tokensbatch tensor: position_ids tokenstorch.Size([1, 1024]) batch tensor: +torch.Size([1, 1024]) + torch.Size([1, 1024])tokensbatch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: batch tensor:tokenstokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024]) + +batch tensor:batch tensor: tokenstokens batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + + tokens batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor:batch tensor: batch tensor:loss_maskloss_mask labelstorch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor: +batch tensor: batch tensor: attention_mask attention_mask loss_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + + +batch tensor:batch tensor:batch tensor: position_idsposition_idsattention_mask torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + loss_maskbatch tensor: torch.Size([1, 1024]) torch.Size([1, 1024]) batch tensor: +torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])attention_mask batch tensor: + +batch tensor: + labels batch tensor:batch tensor: attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024]) loss_maskposition_idstorch.Size([1, 1024])position_idstorch.Size([1, 1, 1024, 1024]) + +attention_mask +torch.Size([1, 1024])batch tensor:batch tensor: batch tensor: torch.Size([1, 1024])torch.Size([1, 1024]) +loss_mask + + labelsbatch tensor: torch.Size([1, 1024])labelstorch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor:batch tensor: + loss_maskbatch tensor:labels loss_masktorch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024]) + +batch tensor:batch tensor: batch tensor: attention_maskloss_mask attention_masktorch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1, 1024, 1024])batch tensor:batch tensor: + batch tensor:position_ids attention_mask position_ids torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: batch tensor:labels batch tensor:batch tensor: labels torch.Size([1, 1024])labelstorch.Size([1, 1024]) tokens + + torch.Size([1, 1024])batch tensor:batch tensor: + batch tensor:loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024])loss_mask + +torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024])batch tensor:batch tensor: + attention_maskattention_masklabelsbatch tensor: torch.Size([1, 1, 1024, 1024])attention_masktorch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])batch tensor: +batch tensor: batch tensor:attention_mask torch.Size([1, 1, 1024, 1024]) + tokensbatch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +position_idsposition_idsbatch tensor: torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1024])torch.Size([1, 1024])attention_mask +torch.Size([1, 1024]) + +batch tensor: + position_idstorch.Size([1, 1, 1024, 1024])batch tensor: + batch tensor:torch.Size([1, 1024])attention_mask + position_ids torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +torch.Size([1, 1, 1024, 1024]) batch tensor: loss_mask +batch tensor:position_ids batch tensor: torch.Size([1, 1024]) position_idstorch.Size([1, 1024])position_ids + +batch tensor:torch.Size([1, 1024])batch tensor:batch tensor: + tokenstorch.Size([1, 1024])attention_mask + tokens torch.Size([1, 1, 1024, 1024]) +batch tensor: torch.Size([1, 1024])position_ids +torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:labelsbatch tensor: labelstorch.Size([1, 1024])tokens +torch.Size([1, 1024]) +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024]) torch.Size([1, 1024]) +torch.Size([1, 1024]) + +batch tensor:batch tensor:batch tensor: attention_masklabelsattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + + +batch tensor: attention_mask tokensbatch tensor: torch.Size([1, 1, 1024, 1024]) +tokens batch tensor: position_idstorch.Size([1, 1024]) torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor: + labels batch tensor:torch.Size([1, 1024]) +labelsbatch tensor:batch tensor:batch tensor: torch.Size([1, 1024])loss_mask + tokenstokenstorch.Size([1, 1024]) batch tensor: + batch tensor:loss_mask attention_masktorch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor:batch tensor: labels torch.Size([1, 1024]) +tokensbatch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])attention_mask +torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024]) + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor: batch tensor:position_ids position_idsloss_mask torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor:batch tensor: +batch tensor: labels batch tensor:attention_masklabels position_idstorch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) + +batch tensor:batch tensor:loss_mask position_idsloss_masktorch.Size([1, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024])batch tensor: + + batch tensor:attention_mask attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: + position_idsbatch tensor: torch.Size([1, 1024])position_ids + batch tensor after cp:torch.Size([1, 1024]) + tokensbatch tensor after cp: tokenstorch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128]) +labels batch tensor after cp:torch.Size([1, 128]) +labelsbatch tensor after cp: torch.Size([1, 128])loss_mask + batch tensor after cp:torch.Size([1, 128]) +loss_mask batch tensor after cp: torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor: labelsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128])batch tensor after cp: + + batch tensor after cp:tokens batch tensor after cp: labels torch.Size([1, 128])labels +torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128])batch tensor after cp: +loss_masklabelsbatch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])loss_mask + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: tokenstokens batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +tokens batch tensor: batch tensor:labels labelstorch.Size([1, 1024]) torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: + batch tensor:loss_mask batch tensor:loss_masktorch.Size([1, 1024]) +labelstorch.Size([1, 1024])batch tensor: + torch.Size([1, 1024])attention_maskbatch tensor: + attention_maskbatch tensor:torch.Size([1, 1, 1024, 1024]) +loss_masktorch.Size([1, 1, 1024, 1024]) batch tensor: + torch.Size([1, 1024])batch tensor:position_ids + position_idsbatch tensor: torch.Size([1, 1024]) +torch.Size([1, 1024])attention_mask + torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])position_ids + batch tensor after cp:torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +batch tensor:batch tensor:batch tensor: position_idstokens tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) attention_mask +loss_mask batch tensor after cp:torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +attention_maskbatch tensor after cp:batch tensor after cp: position_ids torch.Size([1, 1, 128, 1024]) attention_mask + torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 1, 128, 1024]) +position_ids batch tensor after cp:torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor after cp: position_ids tokenstorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokensbatch tensor: tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + labels torch.Size([1, 1024])batch tensor: + batch tensor:labels loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_maskbatch tensor: torch.Size([1, 1024])attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) +attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])position_ids + torch.Size([1, 1024])batch tensor: + position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor:batch tensor: labelslabels torch.Size([1, 1024])tokenstorch.Size([1, 1024]) + + batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor:batch tensor: + attention_maskattention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])labels + batch tensor: torch.Size([1, 1024])batch tensor:position_ids + batch tensor:position_idstorch.Size([1, 1024]) +loss_masktorch.Size([1, 1024])batch tensor: +torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor:batch tensor after cp: tokenstokens tokenstorch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + labels torch.Size([1, 1024])torch.Size([1, 128])batch tensor: + + labelsbatch tensor after cp:batch tensor: torch.Size([1, 1024]) labels +loss_mask batch tensor:torch.Size([1, 1024])torch.Size([1, 128]) + +loss_maskbatch tensor:batch tensor after cp: torch.Size([1, 1024])loss_maskattention_mask +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask tokenstorch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:tokensbatch tensor after cp:labels torch.Size([1, 128])torch.Size([1, 128]) +tokensattention_maskbatch tensor after cp:batch tensor after cp: +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelstokens torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor:torch.Size([1, 1, 128, 1024])batch tensor: torch.Size([1, 1024]) + batch tensor after cp:attention_mask + tokens position_idsbatch tensor:torch.Size([1, 1, 1024, 1024]) torch.Size([1, 128]) +attention_mask +batch tensor: torch.Size([1, 1024]) batch tensor: +torch.Size([1, 1, 1024, 1024])position_ids +batch tensor after cp: tokens batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp:tokens batch tensor after cp:tokens torch.Size([1, 128])labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor after cp: torch.Size([1, 1, 128, 1024])loss_maskbatch tensor after cp:torch.Size([1, 128]) tokens +batch tensor after cp: +labelstokens batch tensor after cp: batch tensor after cp: torch.Size([1, 128])tokens torch.Size([1, 128])labelstorch.Size([1, 128]) + torch.Size([1, 128])position_ids +batch tensor:batch tensor: labelslabels torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:loss_maskbatch tensor after cp: torch.Size([1, 128])labelstokens + torch.Size([1, 128])batch tensor after cp:torch.Size([1, 128]) + +attention_maskbatch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([1, 1, 128, 1024]) torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: + batch tensor:torch.Size([1, 1024])tokensbatch tensor: +position_idslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor: +loss_mask torch.Size([1, 1024]) +batch tensor: batch tensor:labels attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: +torch.Size([1, 128]) +batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + +torch.Size([1, 128])batch tensor after cp:batch tensor after cp: + batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + + loss_maskbatch tensor after cp: + attention_masklabelslabels batch tensor after cp: batch tensor after cp: loss_masktorch.Size([1, 128]) +batch tensor: batch tensor:attention_mask attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: + position_idsbatch tensor: torch.Size([1, 1024])position_ids + torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp: position_ids attention_mask loss_mask torch.Size([1, 128]) torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: position_idsattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) + loss_maskbatch tensor: torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor after cp:batch tensor after cp:labels batch tensor after cp:labels loss_masktorch.Size([1, 128])torch.Size([1, 128]) tokens + +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +torch.Size([1, 128])torch.Size([1, 128])batch tensor after cp: +batch tensor after cp:labelsbatch tensor after cp: + batch tensor after cp:loss_maskbatch tensor after cp: attention_mask torch.Size([1, 128])position_ids +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024])batch tensor after cp: +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:loss_mask torch.Size([1, 128])loss_maskbatch tensor after cp: + torch.Size([1, 128])torch.Size([1, 128])batch tensor after cp:attention_mask + + batch tensor after cp:labelsbatch tensor after cp:torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])attention_mask batch tensor after cp:batch tensor after cp: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids batch tensor after cp:torch.Size([1, 128]) +tokens torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_masktorch.Size([1, 128]) batch tensor after cp:torch.Size([1, 1, 128, 1024])attention_mask +torch.Size([1, 128]) + +loss_mask torch.Size([1, 128])batch tensor after cp: +torch.Size([1, 1, 128, 1024])batch tensor after cp: batch tensor after cp: attention_mask +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: tokens torch.Size([1, 128])tokens + batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_masktorch.Size([1, 1024]) torch.Size([1, 128]) + +batch tensor:batch tensor after cp:batch tensor: attention_masklabels tokens torch.Size([1, 1, 128, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor:batch tensor after cp: torch.Size([1, 1024])position_ids loss_mask +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + + batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) batch tensor after cp:tokens position_ids +torch.Size([1, 1, 128, 1024]) torch.Size([1, 128])loss_masktokens batch tensor after cp: torch.Size([1, 128]) + +torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp:position_idsbatch tensor after cp:batch tensor after cp:position_ids labelsattention_masklabelstorch.Size([1, 128])tokenstorch.Size([1, 128]) + +batch tensor after cp: labels batch tensor after cp:torch.Size([1, 128]) +tokensbatch tensor after cp: loss_mask torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])labels +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: batch tensor after cp:loss_maskloss_mask tokenstorch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: torch.Size([1, 128]) attention_mask +attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) torch.Size([1, 1, 128, 1024]) +labels + batch tensor after cp:batch tensor after cp: torch.Size([1, 128]) position_ids +position_ids batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + +position_ids torch.Size([1, 128]) batch tensor after cp:torch.Size([1, 1, 128, 1024]) +attention_masktorch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +tokenstorch.Size([1, 128]) batch tensor: +batch tensor:torch.Size([1, 1024]) +labels torch.Size([1, 1024])batch tensor: +torch.Size([1, 1024])tokens batch tensor: attention_mask + loss_masktorch.Size([1, 1, 1024, 1024]) +batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + batch tensor: +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 128])torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + + +torch.Size([1, 128])batch tensor after cp:batch tensor after cp:batch tensor after cp: + loss_maskloss_maskposition_ids batch tensor after cp: torch.Size([1, 128])labelstorch.Size([1, 128]) torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 128]) +position_ids batch tensor after cp:torch.Size([1, 128]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128])batch tensor after cp: + tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_ids torch.Size([1, 1, 128, 1024]) position_idsattention_mask + torch.Size([1, 128])torch.Size([1, 128])batch tensor after cp:torch.Size([1, 1, 128, 1024]) + + +position_ids batch tensor after cp:torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +labelsbatch tensor: batch tensor: position_idstorch.Size([1, 1024]) attention_masklabelstorch.Size([1, 1024]) + + batch tensor:torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])loss_maskbatch tensor: + torch.Size([1, 1024])batch tensor: +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + position_idsbatch tensor after cp: position_idstorch.Size([1, 128]) +torch.Size([1, 128]) + +torch.Size([1, 128]) +batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +loss_mask batch tensor: torch.Size([1, 1024])position_idsattention_mask + batch tensor:torch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024]) +attention_mask +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp: attention_mask attention_maskloss_mask torch.Size([1, 1, 128, 1024]) torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) +batch tensor after cp: +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:torch.Size([1, 1, 1024, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + + batch tensor after cp:position_idsbatch tensor after cp: position_idstorch.Size([1, 128]) attention_mask + torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +tokensbatch tensor after cp:batch tensor after cp: position_idstorch.Size([1, 128])position_ids + torch.Size([1, 128])batch tensor after cp: +torch.Size([1, 128]) +labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Start exporting trace 2 +batch tensor after cp:batch tensor after cp: tokenstokensbatch tensor after cp: torch.Size([1, 128])tokenstorch.Size([1, 128]) + +batch tensor after cp:torch.Size([1, 128])batch tensor after cp: +labelslabels batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + +labelsbatch tensor after cp:batch tensor after cp: torch.Size([1, 128])loss_maskloss_mask + batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + +loss_maskbatch tensor after cp: batch tensor after cp:torch.Size([1, 128]) attention_mask +attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) torch.Size([1, 1, 128, 1024]) +attention_mask +batch tensor after cp: torch.Size([1, 1, 128, 1024])batch tensor after cp: + position_idsposition_ids batch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])position_ids + + torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Done exporting trace 2 + [2025-06-21 22:42:17] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 56.5 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 1073741824.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor:batch tensor: + batch tensor:labels tokens labels torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +torch.Size([1, 1024]) batch tensor: +loss_mask loss_masktorch.Size([1, 1024])batch tensor: +torch.Size([1, 1024])labels +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: batch tensor:torch.Size([1, 1024]) attention_mask +attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) +loss_mask + batch tensor:torch.Size([1, 1024]) batch tensor: +position_ids position_idsbatch tensor: torch.Size([1, 1024])torch.Size([1, 1024]) +attention_mask + torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokensbatch tensor: tokens torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024])batch tensor: + labelstokens batch tensor: torch.Size([1, 1024]) +labelsbatch tensor: torch.Size([1, 1024])torch.Size([1, 1024])loss_mask + +torch.Size([1, 1024])batch tensor:batch tensor: + labelsloss_mask batch tensor: torch.Size([1, 1024]) torch.Size([1, 1024])attention_mask + + batch tensor:batch tensor:torch.Size([1, 1, 1024, 1024]) +loss_maskattention_mask batch tensor:torch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024])position_ids + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokensbatch tensor: tokenstorch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024])batch tensor: +batch tensor: tokenslabels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:attention_mask labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: + position_idsbatch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) + batch tensor:torch.Size([1, 1024])batch tensor: + attention_maskposition_ids torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +labels torch.Size([1, 1024])batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])tokens + batch tensor after cp: torch.Size([1, 128])loss_mask + batch tensor after cp:torch.Size([1, 128]) +labels batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) +attention_mask tokensbatch tensor after cp:torch.Size([1, 1, 128, 1024]) +loss_maskbatch tensor after cp:torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + labelsbatch tensor: torch.Size([1, 1024])loss_mask + batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 1024]) +torch.Size([1, 128])position_ids +batch tensor after cp: batch tensor after cp:torch.Size([1, 128])labels + attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + loss_maskbatch tensor after cp: torch.Size([1, 128])position_ids + torch.Size([1, 128])batch tensor after cp: + attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128])batch tensor: +attention_mask batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp:batch tensor: tokenslabels torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) + batch tensor after cp: tokenslabels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 1024])torch.Size([1, 128]) + +torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor: labels loss_masktorch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + loss_mask batch tensor:torch.Size([1, 128]) +attention_mask batch tensor after cp: torch.Size([1, 1, 1024, 1024])attention_mask + torch.Size([1, 1, 128, 1024])batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: loss_mask torch.Size([1, 1024])tokens +batch tensor after cp: attention_maskbatch tensor: labelstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])batch tensor after cp: + position_idsbatch tensor: torch.Size([1, 128])loss_mask +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:position_ids position_ids torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + + batch tensor: attention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + + torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: labelstokenslabels torch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 128])batch tensor after cp:batch tensor after cp: + loss_maskloss_maskbatch tensor after cp: torch.Size([1, 128]) torch.Size([1, 128]) + +batch tensor:batch tensor: position_idslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) + tokensbatch tensor after cp: position_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024]) +batch tensor after cp:labels batch tensor after cp: attention_mask torch.Size([1, 128]) attention_mask +torch.Size([1, 1, 128, 1024]) batch tensor after cp: +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + torch.Size([1, 1, 128, 1024])batch tensor after cp:loss_mask + batch tensor after cp:position_idstorch.Size([1, 128]) +torch.Size([1, 128])position_ids +batch tensor after cp: torch.Size([1, 128])attention_mask +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labelsbatch tensor: torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor:batch tensor after cp: labels tokenstorch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_maskbatch tensor: torch.Size([1, 1, 128, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor:tokens loss_maskbatch tensor: torch.Size([1, 1024]) + torch.Size([1, 1024])tokensbatch tensor: +batch tensor after cp: batch tensor after cp:loss_mask loss_masktorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:attention_mask attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + batch tensor after cp:position_ids position_idstorch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + attention_mask batch tensor: torch.Size([1, 1, 128, 1024])labels + batch tensor after cp:torch.Size([1, 1024]) +batch tensor:position_idsbatch tensor: loss_masktorch.Size([1, 128]) +tokenstorch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor after cp:tokens position_ids torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1024]) +labels + batch tensor:torch.Size([1, 1024]) batch tensor: +position_ids batch tensor:labels loss_masktorch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024]) +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024])attention_mask +torch.Size([1, 128]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024])labels + torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp:batch tensor: tokensposition_ids torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_mask tokenstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + attention_maskbatch tensor after cp: labelstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor:torch.Size([1, 1, 1024, 1024]) attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + batch tensor after cp:position_ids loss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labelsbatch tensor after cp: torch.Size([1, 128])tokens +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024])batch tensor: +batch tensor after cp: tokensposition_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor after cp: torch.Size([1, 128])loss_mask + torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels batch tensor after cp:torch.Size([1, 128]) + batch tensor after cp:tokens loss_mask torch.Size([1, 128])torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels batch tensor after cp:torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + labelsbatch tensor after cp: torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +loss_maskbatch tensor after cp: torch.Size([1, 128])position_ids +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: batch tensor after cp:labels attention_masktorch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokensbatch tensor: loss_masktorch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])batch tensor after cp:tokens + batch tensor after cp:tokenstorch.Size([1, 128]) +loss_mask torch.Size([1, 128])batch tensor after cp:torch.Size([1, 128]) + + torch.Size([1, 128])batch tensor after cp: +attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + batch tensor after cp:loss_mask position_idstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor: labelsattention_mask torch.Size([1, 128])torch.Size([1, 1, 1024, 1024]) + + batch tensor after cp:batch tensor after cp: labels labels attention_mask torch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 1, 128, 1024])batch tensor after cp:batch tensor after cp: + batch tensor after cp:loss_mask loss_mask position_ids torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:batch tensor: loss_maskposition_ids torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + + tokens batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor:torch.Size([1, 1024]) loss_mask + torch.Size([1, 1024])batch tensor: + labelsbatch tensor: torch.Size([1, 1024])attention_mask +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + torch.Size([1, 1, 1024, 1024])batch tensor: + loss_maskbatch tensor: torch.Size([1, 1024])position_ids +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:torch.Size([1, 1024]) +attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor after cp:tokens loss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: labelsattention_mask torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + batch tensor after cp:loss_mask position_idstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +Start exporting trace 3 +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Done exporting trace 3 + [2025-06-21 22:42:17] 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 | +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor:batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) + position_idsbatch tensor: tokens tokenstorch.Size([1, 1024]) +position_ids torch.Size([1, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:labels labelstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + labels batch tensor: torch.Size([1, 1024])labels + batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 1024]) +attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) +attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])position_ids + torch.Size([1, 1024])batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024])batch tensor: + batch tensor:loss_mask loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:attention_mask attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024])position_ids + torch.Size([1, 1024]) + position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: labelslabels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokensloss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:batch tensor: loss_mask tokenstorch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: position_idsbatch tensor: batch tensor after cp:torch.Size([1, 1024])labels +tokenstorch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: + loss_maskbatch tensor after cp: labelstorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor:batch tensor after cp: attention_maskloss_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: + loss_maskbatch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024])batch tensor: + attention_maskbatch tensor: attention_masktorch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: batch tensor:tokens tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor:batch tensor: +batch tensor: attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:batch tensor: loss_mask tokenstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor:batch tensor after cp: position_idsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +torch.Size([1, 1, 1024, 1024])batch tensor: + position_idsbatch tensor: torch.Size([1, 1024])position_ids + torch.Size([1, 1024]) +labels labelsbatch tensor: torch.Size([1, 1024]) torch.Size([1, 1024]) +labels + batch tensor:batch tensor:torch.Size([1, 1024]) +loss_maskloss_mask batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:labels position_idstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: position_ids torch.Size([1, 1024]) +tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokensbatch tensor: batch tensor: position_idstorch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: batch tensor:position_ids labelsbatch tensor:torch.Size([1, 128]) +batch tensor after cp: position_idsbatch tensor after cp: torch.Size([1, 128]) +tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:loss_mask batch tensor: attention_mask torch.Size([1, 1024]) attention_mask +torch.Size([1, 1, 1024, 1024]) batch tensor: +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + tokenstorch.Size([1, 1024])batch tensor: +torch.Size([1, 1024]) +tokens batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +torch.Size([1, 1, 1024, 1024]) + batch tensor:attention_mask batch tensor: position_ids torch.Size([1, 1, 1024, 1024])position_idstorch.Size([1, 1024]) + +torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) + labelstorch.Size([1, 1024]) +torch.Size([1, 1024]) + attention_maskbatch tensor: labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:position_ids loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024])batch tensor: +batch tensor after cp: position_idstokens torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: batch tensor:tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:tokens tokens torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024])batch tensor: +batch tensor:batch tensor: loss_mask labelstorch.Size([1, 1024])batch tensor: +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor:batch tensor after cp: loss_mask loss_mask torch.Size([1, 1024])tokens +batch tensor after cp: tokens torch.Size([1, 128]) +loss_maskbatch tensor after cp: torch.Size([1, 128])loss_mask + batch tensor after cp:torch.Size([1, 128]) +batch tensor: loss_masktokens torch.Size([1, 1024]) +batch tensor: tokensbatch tensor: loss_maskattention_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels tokenstorch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labels labelstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +loss_mask batch tensor:torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor:torch.Size([1, 128])batch tensor: +batch tensor after cp: labels torch.Size([1, 128]) +attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024]) +attention_maskbatch tensor after cp: position_idstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:position_ids tokenstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: torch.Size([1, 1024])attention_mask +torch.Size([1, 1, 1024, 1024]) +batch tensor:torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1024])attention_mask +batch tensor:batch tensor: attention_masklabels torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +loss_maskbatch tensor: attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: position_ids attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokensbatch tensor after cp: torch.Size([1, 128])tokens + batch tensor after cp:torch.Size([1, 128]) +labelsbatch tensor after cp: torch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128]) +loss_maskbatch tensor after cp: torch.Size([1, 128])loss_mask + torch.Size([1, 128])batch tensor after cp: + attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +position_idsbatch tensor after cp: torch.Size([1, 128])position_ids + torch.Size([1, 128]) +attention_maskbatch tensor after cp:attention_mask labels torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024])torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: batch tensor:labels position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor:torch.Size([1, 1, 1024, 1024]) + + position_ids batch tensor:batch tensor:torch.Size([1, 1024]) +batch tensor:batch tensor: loss_maskposition_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: position_ids tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + +batch tensor: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_maskbatch tensor after cp: torch.Size([1, 1024])torch.Size([1, 1024])tokens + + batch tensor:batch tensor: attention_masktorch.Size([1, 128])attention_mask + torch.Size([1, 1, 1024, 1024])batch tensor after cp:torch.Size([1, 1, 1024, 1024]) + + batch tensor:batch tensor:labels position_idsposition_ids torch.Size([1, 128])torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) + batch tensor after cp:tokens position_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + position_idslabels torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor after cp: tokens batch tensor after cp:torch.Size([1, 128]) +tokensbatch tensor after cp: labelstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +torch.Size([1, 1024]) +batch tensor:batch tensor after cp: labels tokens torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:batch tensor after cp:position_ids loss_mask torch.Size([1, 1024])position_ids + torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor after cp:labels loss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor:batch tensor after cp: loss_masklabels torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor: tokensloss_maskbatch tensor after cp: torch.Size([1, 128])torch.Size([1, 1024]) +tokens +batch tensor after cp:batch tensor after cp: tokens tokens torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:labels labelstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labelsbatch tensor: torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: batch tensor after cp:loss_mask attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: +batch tensor after cp:batch tensor: loss_maskattention_mask torch.Size([1, 128])torch.Size([1, 1, 1024, 1024]) + +batch tensor after cp:batch tensor: attention_maskposition_ids torch.Size([1, 1, 128, 1024])torch.Size([1, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor: torch.Size([1, 128]) labelsattention_mask + torch.Size([1, 128])batch tensor after cp: +torch.Size([1, 1, 1024, 1024]) +labelsbatch tensor after cp:batch tensor after cp: batch tensor: torch.Size([1, 128])loss_mask + loss_maskbatch tensor after cp: torch.Size([1, 128]) +loss_mask batch tensor after cp: torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) + batch tensor after cp:tokens loss_mask torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 1024]) attention_mask + torch.Size([1, 1, 128, 1024])batch tensor: +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:attention_mask position_ids torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +tokensposition_ids batch tensor after cp: torch.Size([1, 128])torch.Size([1, 1024]) torch.Size([1, 128]) + +loss_mask +attention_mask batch tensor after cp: position_idstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp: + position_ids torch.Size([1, 128]) + batch tensor after cp:labels position_idstorch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokensbatch tensor after cp:tokens torch.Size([1, 128])tokens +torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128]) batch tensor after cp: +batch tensor after cp: batch tensor after cp: torch.Size([1, 128]) labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids + loss_mask torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +labels batch tensor after cp:labels torch.Size([1, 128])labelstorch.Size([1, 128]) + +attention_mask batch tensor after cp: torch.Size([1, 128]) +attention_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:torch.Size([1, 128])batch tensor after cp: + loss_maskbatch tensor after cp:loss_mask torch.Size([1, 128])torch.Size([1, 128]) +loss_mask +batch tensor after cp: batch tensor after cp:position_ids position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_idstokens torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) attention_mask +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024])attention_mask + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor after cp: tokens tokenstorch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:tokens labels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:loss_mask labelstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:batch tensor after cp: torch.Size([1, 1, 128, 1024]) +position_ids batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128])batch tensor: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +tokens torch.Size([1, 128]) +batch tensor after cp: labels batch tensor after cp:torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp:batch tensor after cp: attention_maskloss_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: batch tensor after cp:attention_mask torch.Size([1, 1, 128, 1024])position_ids +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +batch tensor after cp: tokensattention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels tokenstorch.Size([1, 128]) +batch tensor after cp:tokensbatch tensor: loss_masklabels torch.Size([1, 128]) torch.Size([1, 128]) + +torch.Size([1, 1024])batch tensor after cp: + batch tensor after cp:torch.Size([1, 128]) position_ids + torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: torch.Size([1, 1024])position_ids +torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:batch tensor:batch tensor:labels attention_maskloss_mask torch.Size([1, 128]) tokens +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:batch tensor after cp: tokensloss_mask torch.Size([1, 128])torch.Size([1, 128]) + + batch tensor after cp:loss_mask labelsbatch tensor:torch.Size([1, 128]) torch.Size([1, 128]) +tokens +torch.Size([1, 1, 128, 1024]) torch.Size([1, 1024]) +batch tensor after cp: +batch tensor after cp: batch tensor:torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsattention_maskattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: attention_masklabels torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + + batch tensor after cp:batch tensor after cp: torch.Size([1, 1024])loss_maskattention_mask +loss_mask position_idsattention_mask batch tensor: torch.Size([1, 128])torch.Size([1, 1, 1024, 1024])torch.Size([1, 128])labels + + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: position_idsloss_mask torch.Size([1, 128])torch.Size([1, 128]) + + torch.Size([1, 128]) +batch tensor:torch.Size([1, 1, 128, 1024]) batch tensor after cp: +batch tensor after cp:torch.Size([1, 1024])batch tensor: +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +labels batch tensor after cp:attention_mask torch.Size([1, 1024])position_idstorch.Size([1, 1, 128, 1024]) + + batch tensor:position_idsattention_mask loss_masktorch.Size([1, 1, 128, 1024]) torch.Size([1, 1024]) +torch.Size([1, 1024]) + +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128])batch tensor: position_idsloss_mask torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor: position_idsattention_mask torch.Size([1, 128])torch.Size([1, 1, 1024, 1024]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:loss_mask loss_masktorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:attention_mask attention_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + position_idsbatch tensor after cp: torch.Size([1, 128])position_ids +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor: batch tensor after cp:tokens attention_mask tokens torch.Size([1, 128]) torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: labelstorch.Size([1, 1024]) position_ids +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp:labels loss_masktorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor: batch tensor after cp:loss_mask attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor: +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + attention_maskbatch tensor after cp: position_idstorch.Size([1, 1, 1024, 1024]) + torch.Size([1, 128])batch tensor: + position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_maskbatch tensor after cp: torch.Size([1, 128])tokens +torch.Size([1, 128])batch tensor after cp: +attention_maskbatch tensor after cp: labelstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_idsloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +Start exporting trace 4 +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Done exporting trace 4 + [2025-06-21 22:42:17] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 47.1 | 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([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: batch tensor:loss_mask torch.Size([1, 1024]) +tokens batch tensor: attention_mask torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:labels position_idstorch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels tokenstorch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor:batch tensor: batch tensor: labelsattention_mask tokenstorch.Size([1, 1024]) tokens +batch tensor: batch tensor:tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor:batch tensor: +labels torch.Size([1, 1024])batch tensor:tokens + labelsbatch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + +loss_mask batch tensor:batch tensor:torch.Size([1, 1024]) +attention_masklabels batch tensor: torch.Size([1, 1, 1024, 1024])attention_masktorch.Size([1, 1024]) + +torch.Size([1, 1, 1024, 1024])batch tensor:batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor: position_idstokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels batch tensor:torch.Size([1, 1024]) +batch tensor: tokensloss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor:torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) +loss_mask +torch.Size([1, 1024])batch tensor: +position_idsbatch tensor:torch.Size([1, 1024]) batch tensor: +batch tensor:torch.Size([1, 1024]) + position_idsloss_maskbatch tensor: torch.Size([1, 1024])torch.Size([1, 1024])position_ids + + torch.Size([1, 1024])batch tensor: + attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor:tokens labels torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: labelsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: loss_maskposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])attention_mask + torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:labels position_idstorch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +labelslabelsbatch tensor: torch.Size([1, 1024])attention_mask +tokenstorch.Size([1, 1024]) batch tensor: torch.Size([1, 1, 1024, 1024]) + +batch tensor:loss_maskbatch tensor:torch.Size([1, 1024]) + position_idsloss_mask torch.Size([1, 1024])batch tensor: torch.Size([1, 1024]) +labels +torch.Size([1, 1024])batch tensor: + torch.Size([1, 1024])batch tensor:attention_mask + attention_maskbatch tensor:torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])loss_maskbatch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) + batch tensor:torch.Size([1, 1024]) position_idsposition_ids + torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + +attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor: attention_mask torch.Size([1, 1, 128, 1024])tokens +batch tensor: loss_mask batch tensor:torch.Size([1, 1024]) +batch tensor: attention_masktokens torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids tokenstorch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +tokens batch tensor: labels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask batch tensor:torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor after cp: batch tensor: loss_masktokens tokens torch.Size([1, 128]) +batch tensor after cp: torch.Size([1, 1024])attention_mask torch.Size([1, 1024])batch tensor: + +torch.Size([1, 1, 128, 1024]) +batch tensor:tokens batch tensor:labelsbatch tensor after cp: torch.Size([1, 1024])position_idslabels + batch tensor:torch.Size([1, 128])torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: position_ids torch.Size([1, 1024]) +tokens batch tensor after cp:torch.Size([1, 1024]) +tokens batch tensor:torch.Size([1, 128]) +labels batch tensor after cp:torch.Size([1, 1024]) +labelsbatch tensor: torch.Size([1, 128])loss_mask + batch tensor after cp:torch.Size([1, 1024]) +batch tensor: loss_maskbatch tensor: labelstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor:batch tensor: +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) + batch tensor:tokens attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: + position_ids torch.Size([1, 1024])batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_mask loss_maskbatch tensor: torch.Size([1, 1024])torch.Size([1, 1024])labels + + torch.Size([1, 1024])batch tensor:batch tensor: + batch tensor:attention_maskattention_mask loss_masktorch.Size([1, 1, 1024, 1024]) torch.Size([1, 1, 1024, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 128]) +attention_maskbatch tensor after cp: torch.Size([1, 1, 1024, 1024])attention_mask + batch tensor:torch.Size([1, 1, 128, 1024]) +position_idsbatch tensor after cp: torch.Size([1, 1024])position_ids + torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) + attention_maskbatch tensor: tokensloss_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: + position_idsbatch tensor: torch.Size([1, 1024]) torch.Size([1, 1024])attention_mask + + torch.Size([1, 1, 1024, 1024])batch tensor: + batch tensor:labels position_idstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) + labels torch.Size([1, 1024]) +batch tensor:batch tensor after cp: tokens tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 1024])torch.Size([1, 128]) + +torch.Size([1, 1024])batch tensor:batch tensor: + position_idsposition_idsbatch tensor: torch.Size([1, 1024])attention_mask +torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: loss_maskbatch tensor: torch.Size([1, 128])labels +batch tensor: tokens torch.Size([1, 1024])batch tensor: +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:torch.Size([1, 1024]) +attention_mask batch tensor: torch.Size([1, 1, 128, 1024])loss_mask + batch tensor after cp:torch.Size([1, 1024]) + batch tensor:tokens labels torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) loss_mask + torch.Size([1, 1024])batch tensor: +batch tensor after cp: batch tensor:attention_mask torch.Size([1, 1, 128, 1024])tokens + batch tensor after cp: position_ids torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:tokens tokenstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([1, 128])labels +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024])batch tensor after cp: +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: tokens torch.Size([1, 1024]) +position_ids batch tensor: torch.Size([1, 128])attention_mask + torch.Size([1, 1, 1024, 1024]) + labelsbatch tensor: torch.Size([1, 1024]) +attention_maskbatch tensor: loss_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])batch tensor after cp: + torch.Size([1, 128])batch tensor after cp: + loss_maskbatch tensor after cp: loss_masktorch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:tokens position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + position_idsbatch tensor: attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor:tokens attention_mask torch.Size([1, 128])torch.Size([1, 1, 1024, 1024]) + +torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:attention_mask attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor after cp:position_ids position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor: labelsbatch tensor after cp:position_ids torch.Size([1, 128])torch.Size([1, 1024])tokens + + batch tensor after cp: torch.Size([1, 128])loss_mask + torch.Size([1, 128])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([1, 128])attention_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelstokens torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labels tokenstorch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_maskbatch tensor after cp: torch.Size([1, 128])tokens +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +loss_mask batch tensor after cp:torch.Size([1, 128]) +position_ids batch tensor after cp:torch.Size([1, 128]) +attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:loss_mask torch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp:loss_maskbatch tensor after cp: tokenstorch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + +attention_maskbatch tensor after cp: batch tensor after cp:torch.Size([1, 1, 128, 1024])loss_mask + batch tensor after cp:torch.Size([1, 128]) +attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128])batch tensor: +batch tensor after cp: labelstokens torch.Size([1, 128]) +attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])loss_mask + torch.Size([1, 128])batch tensor after cp: + labelsbatch tensor after cp: torch.Size([1, 128]) torch.Size([1, 128]) +position_ids +batch tensor after cp: batch tensor after cp: torch.Size([1, 128]) attention_mask +loss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +labels batch tensor after cp: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 128]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens +batch tensor after cp:batch tensor after cp: batch tensor after cp: tokens tokens tokenstorch.Size([1, 128]) torch.Size([1, 128]) + +torch.Size([1, 128])batch tensor after cp:batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp: batch tensor after cp:tokensposition_ids attention_mask torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + + +batch tensor after cp:batch tensor after cp: labelsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_idsattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor:batch tensor: loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024])tokens + batch tensor:batch tensor: labelsattention_mask torch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) + +batch tensor: batch tensor:loss_maskbatch tensor: position_idslabelstorch.Size([1, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024]) + + labelslabelsbatch tensor after cp: labelstorch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:batch tensor after cp:loss_mask loss_mask loss_mask torch.Size([1, 128]) torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask batch tensor:torch.Size([1, 1, 128, 1024]) +labels batch tensor after cp: torch.Size([1, 1024])position_ids + torch.Size([1, 128])batch tensor: + loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor: attention_maskloss_mask torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:attention_mask position_ids torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:attention_maskattention_mask torch.Size([1, 1, 128, 1024])attention_masktorch.Size([1, 1, 128, 1024]) + +torch.Size([1, 1, 128, 1024])batch tensor after cp:batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor after cp: labelstokens torch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:labels torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokensposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) + position_idsbatch tensor after cp:position_ids torch.Size([1, 128])position_idstorch.Size([1, 128]) + +torch.Size([1, 128]) + batch tensor after cp:loss_mask labelstorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +tokensbatch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +attention_mask torch.Size([1, 1, 1024, 1024])batch tensor: + labelsbatch tensor: position_idstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp:batch tensor: loss_maskattention_mask torch.Size([1, 128]) +torch.Size([1, 1, 1024, 1024])batch tensor after cp: + attention_maskbatch tensor: torch.Size([1, 1, 128, 1024])position_ids +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:torch.Size([1, 1024]) +position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024])batch tensor after cp: +batch tensor after cp: tokensbatch tensor: torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 1, 1024, 1024]) +labels batch tensor:torch.Size([1, 128]) position_ids +torch.Size([1, 1024])batch tensor after cp: + loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + tokensbatch tensor: labelstorch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +tokensbatch tensor after cp: attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor after cp:labels position_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor: labelsloss_mask torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor after cp: attention_maskloss_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 128]) + +batch tensor:batch tensor after cp: position_idsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +tokensbatch tensor after cp:batch tensor after cp: torch.Size([1, 128])attention_masktokens + torch.Size([1, 1, 128, 1024])batch tensor after cp: +torch.Size([1, 128]) +labelsbatch tensor after cp: batch tensor after cp: torch.Size([1, 128]) +position_idslabels batch tensor after cp:torch.Size([1, 128]) torch.Size([1, 128]) +loss_mask +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 128]) +loss_mask batch tensor after cp:torch.Size([1, 128]) +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +position_ids batch tensor after cp:torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokensloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_masklabels torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +Start exporting trace 5 +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: position_idsloss_mask torch.Size([1, 128])torch.Size([1, 128]) + + [2025-06-21 22:42:17] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 49.1 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 134217728.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +Done exporting trace 5 +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:loss_mask loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:attention_mask attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:position_ids position_idstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens + batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])labels + batch tensor:torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024])batch tensor: labels + torch.Size([1, 1024])tokens +batch tensor: batch tensor: labelsloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024]) + +batch tensor: batch tensor:attention_maskbatch tensor: loss_masklabelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024])batch tensor: + + position_idsbatch tensor:batch tensor: torch.Size([1, 1024])loss_maskattention_mask + torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: + attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])position_ids + batch tensor:torch.Size([1, 1024]) +position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + labels batch tensor:torch.Size([1, 1024]) +labels batch tensor:torch.Size([1, 1024]) +loss_maskbatch tensor: loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: batch tensor:position_ids torch.Size([1, 1024]) +position_ids batch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: batch tensor:labels torch.Size([1, 1024]) +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024])batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +tokensbatch tensor: tokens torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +labels torch.Size([1, 1024])batch tensor: +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor:tokens loss_mask torch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])attention_mask +torch.Size([1, 1, 1024, 1024]) + batch tensor:tokens attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:torch.Size([1, 1024]) +position_ids torch.Size([1, 1024])batch tensor: + labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor:tokens loss_mask torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp:batch tensor: labelsattention_mask torch.Size([1, 128]) +torch.Size([1, 1, 1024, 1024])batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) + batch tensor:labels loss_masktorch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:labels batch tensor: position_idstorch.Size([1, 1024]) torch.Size([1, 1024])batch tensor: +tokens +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])batch tensor:batch tensor: + loss_maskbatch tensor: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 1024]) +attention_maskbatch tensor: torch.Size([1, 1, 128, 1024]) +batch tensor: loss_maskbatch tensor after cp:batch tensor after cp: torch.Size([1, 1024])tokenstokens +torch.Size([1, 128])torch.Size([1, 128])batch tensor: + +torch.Size([1, 1024])batch tensor:batch tensor: +batch tensor after cp: + loss_maskbatch tensor after cp: torch.Size([1, 128])loss_mask +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_masktokenstorch.Size([1, 1024]) batch tensor: + torch.Size([1, 1024])tokensbatch tensor: + tokensbatch tensor: tokensposition_ids torch.Size([1, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:labels labels torch.Size([1, 1024]) +torch.Size([1, 1024]) + batch tensor after cp: tokensposition_ids torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:attention_maskbatch tensor after cp: labelstorch.Size([1, 1, 1024, 1024])labels + batch tensor: loss_mask tokensattention_mask torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor:torch.Size([1, 1024]) + + torch.Size([1, 128])batch tensor after cp: + attention_maskbatch tensor after cp: attention_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: +batch tensor: tokens batch tensor:torch.Size([1, 1024]) + batch tensor:tokens labels batch tensor after cp:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + batch tensor:torch.Size([1, 1024])labels + attention_masktorch.Size([1, 1024])torch.Size([1, 1024]) batch tensor:torch.Size([1, 1, 1024, 1024]) + + +batch tensor:labelsbatch tensor: batch tensor:loss_mask position_ids torch.Size([1, 1024])labelstorch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + torch.Size([1, 128]) +batch tensor:torch.Size([1, 128])batch tensor after cp: + position_idsbatch tensor after cp: loss_maskbatch tensor: torch.Size([1, 1024]) +loss_mask torch.Size([1, 128]) +tokenstorch.Size([1, 128])batch tensor after cp: +batch tensor:attention_mask batch tensor:position_ids labelstorch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +torch.Size([1, 1024])batch tensor: + batch tensor:position_ids loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) + position_idsbatch tensor after cp: torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +torch.Size([1, 1024])tokensbatch tensor: + loss_maskbatch tensor:torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:torch.Size([1, 1024])batch tensor: + batch tensor:loss_maskattention_mask loss_masktorch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:attention_mask torch.Size([1, 1, 128, 1024])attention_masktorch.Size([1, 1024]) + +batch tensor after cp:torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +torch.Size([1, 1024])labelsbatch tensor after cp: torch.Size([1, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: batch tensor: attention_mask position_ids attention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: batch tensor: position_idsposition_ids tokens torch.Size([1, 1024]) torch.Size([1, 1024]) + +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor:position_idsbatch tensor after cp: labelstorch.Size([1, 128]) torch.Size([1, 1024]) position_ids + tokens batch tensor:batch tensor:torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor:batch tensor:labels loss_maskattention_masktorch.Size([1, 128]) +torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024])batch tensor after cp: + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 128]) + + tokenstokens batch tensor: labels torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor:loss_maskbatch tensor: position_idsattention_masktorch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + loss_mask + batch tensor:torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: + attention_maskbatch tensor: torch.Size([1, 1, 128, 1024])position_ids + batch tensor after cp:torch.Size([1, 1024]) +position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: batch tensor:loss_mask torch.Size([1, 128]) +tokens batch tensor after cp: attention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:labels labelsbatch tensor: torch.Size([1, 1024]) attention_mask +torch.Size([1, 1024]) batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +tokens batch tensor: labels torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_maskbatch tensor: torch.Size([1, 1024])labels + torch.Size([1, 1024])batch tensor: +batch tensor: batch tensor:attention_mask loss_mask tokens torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: torch.Size([1, 1024])attention_maskposition_ids +torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) +batch tensor: + labelsbatch tensor: position_idstorch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor after cp: position_ids torch.Size([1, 128])tokens +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor after cp: labels position_idstorch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: + loss_maskloss_maskbatch tensor: torch.Size([1, 1024]) torch.Size([1, 1024])position_ids + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor:tokens position_ids torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:batch tensor:torch.Size([1, 1024]) +batch tensor after cp: position_ids batch tensor after cp:torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: tokens tokens batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor after cp:labels tokens torch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +batch tensor after cp: batch tensor after cp: tokensloss_mask labels batch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 1024]) + + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +tokens batch tensor:batch tensor: labelslabels torch.Size([1, 1024]) torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor after cp:tokensbatch tensor after cp: batch tensor: loss_masktorch.Size([1, 128])attention_masklabels +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:batch tensor:batch tensor: labels loss_mask loss_masktorch.Size([1, 1024]) + torch.Size([1, 1, 1024, 1024])torch.Size([1, 128])torch.Size([1, 128])batch tensor after cp: + + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + +loss_mask batch tensor:batch tensor: torch.Size([1, 1024]) + batch tensor:labelsbatch tensor after cp:batch tensor after cp: attention_mask torch.Size([1, 128]) position_idsloss_mask torch.Size([1, 1024]) +batch tensor: tokens +batch tensor after cp:batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp:tokens tokenslabels torch.Size([1, 128])torch.Size([1, 128]) +torch.Size([1, 128]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_idsbatch tensor after cp: batch tensor after cp:torch.Size([1, 1024]) +tokenstokens batch tensor after cp:torch.Size([1, 128]) +torch.Size([1, 128])tokensbatch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:attention_mask torch.Size([1, 1, 128, 1024])tokens +torch.Size([1, 128])batch tensor after cp: + position_idsbatch tensor after cp: torch.Size([1, 128])labels + torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +attention_maskattention_mask batch tensor: torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1, 1024, 1024]) + torch.Size([1, 1, 128, 1024])torch.Size([1, 1024])batch tensor after cp:torch.Size([1, 128]) + + +batch tensor after cp:batch tensor after cp: batch tensor after cp: labels labels loss_masktorch.Size([1, 128])batch tensor: +torch.Size([1, 128]) batch tensor after cp: +torch.Size([1, 128]) batch tensor after cp:tokensloss_mask + loss_maskbatch tensor after cp:torch.Size([1, 128]) torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) + batch tensor after cp:batch tensor after cp:labelstorch.Size([1, 128]) +tokenslabelstorch.Size([1, 128])batch tensor after cp: + torch.Size([1, 128])batch tensor after cp:labelstorch.Size([1, 128]) +loss_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +attention_maskbatch tensor: + batch tensor:torch.Size([1, 1, 1024, 1024])position_ids +batch tensor after cp:loss_maskbatch tensor: batch tensor after cp:position_ids torch.Size([1, 128])attention_masklabels +torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +attention_masktorch.Size([1, 128]) +batch tensor after cp: +batch tensor: batch tensor after cp: torch.Size([1, 1, 128, 1024]) attention_masklabels +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1, 128, 1024])batch tensor after cp:batch tensor:position_ids + batch tensor after cp:torch.Size([1, 128])position_ids + loss_mask torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])batch tensor after cp: +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor:torch.Size([1, 1024])position_idsbatch tensor: + position_ids torch.Size([1, 1024])tokens + torch.Size([1, 1024]) +batch tensor after cp: torch.Size([1, 1024])batch tensor after cp:attention_mask + position_idsbatch tensor:torch.Size([1, 1, 128, 1024]) +position_idstorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + tokensbatch tensor: attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 1024, 1024])batch tensor after cp: + labelsbatch tensor: torch.Size([1, 128])position_ids +batch tensor after cp: position_ids torch.Size([1, 128])batch tensor after cp: loss_mask torch.Size([1, 128])torch.Size([1, 128]) +batch tensor after cp: + tokensbatch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor after cp:loss_mask position_ids torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 1024]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp: torch.Size([1, 128]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: batch tensor:position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + loss_maskbatch tensor after cp:batch tensor after cp:attention_mask labelsbatch tensor after cp: torch.Size([1, 128])batch tensor after cp:torch.Size([1, 1, 128, 1024]) +attention_maskbatch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 128])tokens + + torch.Size([1, 128])tokensbatch tensor after cp: batch tensor after cp: + torch.Size([1, 1, 128, 1024])loss_mask +batch tensor after cp:torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 128]) labelsbatch tensor after cp: + torch.Size([1, 1, 128, 1024])position_ids + batch tensor after cp:labels batch tensor after cp:torch.Size([1, 128]) torch.Size([1, 128]) + +attention_masktorch.Size([1, 128])position_ids +batch tensor after cp: torch.Size([1, 1, 128, 1024])batch tensor after cp: +loss_masktorch.Size([1, 128]) +batch tensor after cp:loss_masktorch.Size([1, 128]) +position_idstorch.Size([1, 128])batch tensor after cp: +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 128])attention_mask +attention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: batch tensor:position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + tokens torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +Start exporting trace 6 +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +Done exporting trace 6 +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelsloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: loss_maskattention_masktokens torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp:torch.Size([1, 128])batch tensor after cp: + attention_maskposition_idsbatch tensor after cp: torch.Size([1, 128])labelstorch.Size([1, 1, 128, 1024]) + +torch.Size([1, 128])batch tensor after cp: + position_idsbatch tensor after cp: torch.Size([1, 128])loss_mask +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:loss_mask loss_masktorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor after cp:batch tensor:attention_mask batch tensor after cp:attention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) +tokenstokens +batch tensor after cp: batch tensor after cp:position_ids torch.Size([1, 128])position_ids + torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) +batch tensor: +batch tensor after cp:labels labelstorch.Size([1, 128]) tokens +torch.Size([1, 1024]) batch tensor after cp: + batch tensor:torch.Size([1, 128])loss_mask +torch.Size([1, 128])loss_mask +batch tensor after cp: batch tensor after cp: torch.Size([1, 1024])labelsattention_mask + batch tensor:torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +attention_maskbatch tensor after cp: batch tensor after cp: torch.Size([1, 1, 1024, 1024])position_idsloss_mask + torch.Size([1, 128])batch tensor:torch.Size([1, 128]) + +position_idsbatch tensor after cp: torch.Size([1, 1024])attention_mask + torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + [2025-06-21 22:42:17] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 44.5 | 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([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens + batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens + batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) +labelsbatch tensor: torch.Size([1, 1024])position_ids + batch tensor:torch.Size([1, 1024]) +loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])tokens + batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024])batch tensor: + batch tensor:tokens labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])labels + batch tensor:torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokensbatch tensor: tokens torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) batch tensor:labels + torch.Size([1, 1024])batch tensor: +tokens batch tensor:labels batch tensor:loss_masktorch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor:tokensbatch tensor: + loss_masklabelsbatch tensor: torch.Size([1, 1024])torch.Size([1, 1024])attention_masktorch.Size([1, 1024]) + + +batch tensor: torch.Size([1, 1, 1024, 1024])batch tensor:loss_maskbatch tensor:batch tensor: + attention_mask torch.Size([1, 1024])labelsbatch tensor: + tokensbatch tensor:torch.Size([1, 1024]) +batch tensor: attention_maskbatch tensor: labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:position_ids loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + +batch tensor: batch tensor:attention_mask attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: batch tensor:labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])position_idsattention_maskbatch tensor: + torch.Size([1, 1024]) batch tensor: torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])loss_mask + + batch tensor:position_idsbatch tensor: torch.Size([1, 1024]) +labelsposition_idstorch.Size([1, 1024]) batch tensor: torch.Size([1, 1024]) + + torch.Size([1, 1024])attention_maskbatch tensor: + loss_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens + batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])labels + batch tensor:torch.Size([1, 1024]) +position_idsbatch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelstokens torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128]) +loss_mask batch tensor after cp:torch.Size([1, 128]) +labels batch tensor after cp: torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +loss_mask batch tensor after cp: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 128]) +attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:tokens loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: attention_maskbatch tensor: labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:position_ids loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: torch.Size([1, 1024]) + tokens torch.Size([1, 1024]) +batch tensor: batch tensor:position_ids attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor:batch tensor after cp: labels torch.Size([1, 128])tokens +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:batch tensor: loss_mask torch.Size([1, 1024]) +tokens batch tensor: batch tensor:attention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +tokensbatch tensor: batch tensor: labelsposition_ids torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024])labels +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128])batch tensor after cp: +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor after cp: loss_mask torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: attention_maskbatch tensor: torch.Size([1, 1, 128, 1024])labels + batch tensor after cp:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:labels labels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:torch.Size([1, 1024]) +attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])loss_mask + torch.Size([1, 1024])batch tensor: + position_idsbatch tensor: attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:tokens attention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: labelsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 128]) +loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens + batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + attention_maskbatch tensor: labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labelsbatch tensor: torch.Size([1, 1024])labels +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: batch tensor after cp:labels tokenstorch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: batch tensor:position_ids loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor after cp: batch tensor:labels tokenstorch.Size([1, 1024]) + batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor: torch.Size([1, 1024])batch tensor:loss_mask + loss_maskbatch tensor after cp: torch.Size([1, 1024])labels +batch tensor after cp: attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])tokens +batch tensor: loss_mask batch tensor:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenslabels torch.Size([1, 128]) +torch.Size([1, 128]) +tokensbatch tensor: torch.Size([1, 128]) loss_mask + batch tensor:torch.Size([1, 1024]) tokens + batch tensor:torch.Size([1, 128]) +attention_maskbatch tensor after cp: loss_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 128]) + batch tensor after cp: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 128]) +labels torch.Size([1, 128]) + batch tensor:tokens attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: + position_ids batch tensor:torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskloss_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: attention_maskposition_ids torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + torch.Size([1, 1024])batch tensor after cp: +labelstorch.Size([1, 1024]) batch tensor: + torch.Size([1, 128])attention_maskbatch tensor: +attention_mask batch tensor: attention_masktorch.Size([1, 1, 1024, 1024]) torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) + +batch tensor: batch tensor:position_idsbatch tensor: position_idstorch.Size([1, 1024]) labels +torch.Size([1, 1024]) +batch tensor:batch tensor after cp: position_idsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor:batch tensor after cp: loss_masktokens torch.Size([1, 128]) +labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: + batch tensor after cp:tokens position_ids torch.Size([1, 128])torch.Size([1, 128]) + + batch tensor after cp:labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor:batch tensor:loss_mask +torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: torch.Size([1, 1024])attention_mask +torch.Size([1, 1, 128, 1024])batch tensor: + batch tensor after cp:labels position_idstorch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])tokens + batch tensor after cp: loss_mask torch.Size([1, 128])torch.Size([1, 128]) + + tokensbatch tensor:position_idstorch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])labels + batch tensor after cp: position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +loss_masktorch.Size([1, 1024]) +batch tensor after cp:torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens batch tensor after cp: tokens torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokensbatch tensor after cp: tokens torch.Size([1, 128])torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_masktokens torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +attention_maskbatch tensor: batch tensor:attention_mask labels torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1, 128, 1024])batch tensor: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor after cp: labelslabels torch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + +batch tensor after cp: labelsbatch tensor: torch.Size([1, 128])labels +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labels position_idstorch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128]) loss_mask + torch.Size([1, 128]) +batch tensor: batch tensor after cp:loss_maskposition_ids torch.Size([1, 1024]) +position_idstorch.Size([1, 1024]) +batch tensor: torch.Size([1, 128])attention_mask + torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + loss_maskbatch tensor after cp: torch.Size([1, 1024])loss_mask + torch.Size([1, 128])batch tensor: + attention_mask batch tensor after cp:torch.Size([1, 1, 1024, 1024]) +attention_mask batch tensor: position_idstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 1024]) +loss_maskbatch tensor after cp:batch tensor: torch.Size([1, 128])loss_masktokens +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) + position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 1024]) +torch.Size([1, 128])attention_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokensbatch tensor: torch.Size([1, 128]) +tokens batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 1024]) loss_mask + torch.Size([1, 128])batch tensor: +batch tensor after cp:batch tensor: tokens torch.Size([1, 128])tokens + batch tensor after cp: labels torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: batch tensor after cp: torch.Size([1, 1, 128, 1024])attention_masklabels + torch.Size([1, 1, 1024, 1024])batch tensor after cp:torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor after cp:labels attention_maskbatch tensor:batch tensor after cp:torch.Size([1, 1024]) + torch.Size([1, 1, 128, 1024])batch tensor:tokens tokens + loss_mask batch tensor after cp:torch.Size([1, 128]) +loss_mask batch tensor:torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])tokens + batch tensor after cp:torch.Size([1, 128]) + batch tensor after cp:batch tensor:position_ids loss_maskposition_ids torch.Size([1, 128]) torch.Size([1, 128]) +torch.Size([1, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + position_idstorch.Size([1, 1024])batch tensor after cp: +torch.Size([1, 1024]) batch tensor: +torch.Size([1, 128]) labelsattention_mask +batch tensor: torch.Size([1, 128])labels + torch.Size([1, 1, 1024, 1024])batch tensor after cp:torch.Size([1, 1024]) + +batch tensor:loss_mask torch.Size([1, 128])position_ids +labelsbatch tensor after cp: attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor: +loss_mask batch tensor after cp:torch.Size([1, 128]) +labels batch tensor after cp:torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +Start exporting trace 7 +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor after cp:batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +attention_maskattention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 128, 1024]) + + batch tensor after cp:loss_mask position_idstorch.Size([1, 1024]) +torch.Size([1, 128]) +attention_maskbatch tensor after cp: loss_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128])batch tensor after cp: + tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:batch tensor after cp: labels tokenstorch.Size([1, 128])batch tensor after cp: +batch tensor:batch tensor after cp: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:position_ids attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Done exporting trace 7 +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp: loss_maskbatch tensor after cp:tokens torch.Size([1, 1024]) torch.Size([1, 128]) + +torch.Size([1, 128])tokensbatch tensor after cp:batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelstokens torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor after cp:attention_masktorch.Size([1, 128])labels + labelstorch.Size([1, 1, 128, 1024]) batch tensor after cp:torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + loss_maskbatch tensor after cp: torch.Size([1, 128])labels +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:attention_mask loss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 128]) batch tensor after cp:labelsbatch tensor after cp: + + position_idsbatch tensor after cp:tokensbatch tensor: batch tensor:torch.Size([1, 128]) + torch.Size([1, 128])loss_mask + torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_idsattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +tokensbatch tensor after cp:torch.Size([1, 128])batch tensor after cp:loss_mask + loss_masklabelstorch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor:torch.Size([1, 128])batch tensor: + + attention_masklabels batch tensor after cp:batch tensor after cp: torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + torch.Size([1, 1024]) +loss_maskbatch tensor: attention_maskbatch tensor: torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +position_idsloss_maskbatch tensor after cp:torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024])attention_mask +batch tensor after cp: + batch tensor: torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +position_idsattention_maskbatch tensor after cp: torch.Size([1, 1, 1024, 1024])torch.Size([1, 128])position_ids + +batch tensor:torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor after cp:position_ids tokenstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + [2025-06-21 22:42:17] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 45.2 | 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: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + batch tensor:loss_mask loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:attention_mask attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: + batch tensor:position_ids position_idstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: batch tensor:tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + labels batch tensor:torch.Size([1, 1024])batch tensor: +labelsbatch tensor: torch.Size([1, 1024])tokensloss_mask + batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: batch tensor:position_ids tokens torch.Size([1, 1024])tokens + torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + labelsbatch tensor:batch tensor: torch.Size([1, 1024])labels + batch tensor: tokensbatch tensor:torch.Size([1, 1024]) + loss_maskbatch tensor:tokens torch.Size([1, 1024])loss_masktorch.Size([1, 1024]) + +batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor:attention_mask +batch tensor: batch tensor:labelslabels torch.Size([1, 1, 1024, 1024])attention_mask + torch.Size([1, 1024])torch.Size([1, 1024])batch tensor:torch.Size([1, 1, 1024, 1024]) + + +batch tensor:position_idsbatch tensor:batch tensor: batch tensor: torch.Size([1, 1024]) +tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])position_ids loss_mask +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) 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1024])batch tensor: + + loss_maskposition_ids batch tensor: torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024])labels batch tensor: + torch.Size([1, 1024]) torch.Size([1, 1024]) +attention_mask + batch tensor:batch tensor:torch.Size([1, 1, 1024, 1024]) +loss_maskattention_maskbatch tensor: torch.Size([1, 1024])position_ids +torch.Size([1, 1, 1024, 1024]) +batch tensor:torch.Size([1, 1024])batch tensor: + attention_maskposition_ids torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor after cp: tokens tokenstorch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: +loss_mask torch.Size([1, 128])batch tensor: +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp:batch tensor: attention_maskattention_mask torch.Size([1, 1, 128, 1024])batch tensor:torch.Size([1, 1, 128, 1024]) +tokens + +labelsbatch tensor:batch tensor: torch.Size([1, 1024])attention_mask + batch tensor:tokenstorch.Size([1, 1, 1024, 1024]) +loss_mask batch tensor:torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) +attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:labels labelstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + batch tensor:loss_mask loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) +position_ids batch tensor: torch.Size([1, 1024])position_ids + torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024]) +tokensbatch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +attention_mask torch.Size([1, 1, 1024, 1024])batch tensor: + batch tensor:labels position_idstorch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor after cp:labels attention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: position_idsbatch tensor after cp:tokens torch.Size([1, 1024]) position_idstorch.Size([1, 128]) + +torch.Size([1, 128])torch.Size([1, 1024])batch tensor: + + labels batch tensor:torch.Size([1, 1024]) +labelsbatch tensor:batch tensor: torch.Size([1, 1024])loss_mask + batch tensor:torch.Size([1, 1024]) +tokensloss_mask batch tensor:torch.Size([1, 1024])batch tensor: +attention_masktorch.Size([1, 1024])batch tensor: + attention_masktokens torch.Size([1, 1, 1024, 1024])torch.Size([1, 1, 1024, 1024])batch tensor: + +labels batch tensor:torch.Size([1, 1024]) + position_idsbatch tensor: loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor after cp: batch tensor: tokensposition_idsloss_mask torch.Size([1, 128])torch.Size([1, 1024]) + + batch tensor:batch tensor: labels position_ids torch.Size([1, 1024])position_ids torch.Size([1, 1024]) + + torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor: + attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) +batch tensor:loss_mask labelstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor:batch tensor: attention_maskloss_mask batch tensor after cp:torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens + batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor: batch tensor:loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:loss_mask torch.Size([1, 128])tokens +labels batch tensor:torch.Size([1, 1024]) +tokensbatch tensor: batch tensor: torch.Size([1, 128])position_ids +attention_masktorch.Size([1, 1024]) batch tensor after cp: +torch.Size([1, 1, 1024, 1024]) labels +tokensbatch tensor: labels torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])labels +torch.Size([1, 1024]) +batch tensor:batch tensor: position_idsloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokensbatch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:torch.Size([1, 1024]) +position_ids torch.Size([1, 1024])batch tensor: + batch tensor after cp: attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: +position_ids batch tensor:torch.Size([1, 1024]) +loss_mask torch.Size([1, 1024])batch tensor: + batch tensor:torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) + labels torch.Size([1, 1024]) + batch tensor after cp:labels torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 128]) +loss_mask torch.Size([1, 128]) +batch tensor: batch tensor:tokens attention_mask torch.Size([1, 1, 1024, 1024])tokens +position_idsbatch tensor after cp: torch.Size([1, 1024])loss_mask +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: tokenstokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: labelslabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor:batch tensor: loss_maskloss_masktokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor:batch tensor:torch.Size([1, 1024]) attention_maskattention_mask +tokens batch tensor: torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1, 1024, 1024]) +labels +batch tensor: batch tensor:torch.Size([1, 1024])position_idstorch.Size([1, 1024]) + +position_idsbatch tensor:torch.Size([1, 1024]) + loss_maskbatch tensor: torch.Size([1, 1024])torch.Size([1, 1024])labels + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: +batch tensor: labelsbatch tensor: tokenslabels torch.Size([1, 1024])batch tensor: +torch.Size([1, 1024])batch tensor: torch.Size([1, 1024]) + tokensloss_mask +batch tensor: torch.Size([1, 1024])loss_maskbatch tensor: +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])batch tensor: + torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor:torch.Size([1, 1024]) +attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])loss_mask + batch tensor:torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + torch.Size([1, 1024])torch.Size([1, 1024])batch tensor:labels + + torch.Size([1, 1024])batch tensor:attention_mask + batch tensor: batch tensor:attention_mask torch.Size([1, 1, 1024, 1024])labelstorch.Size([1, 1, 1024, 1024])loss_mask + + batch tensor:batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) +position_ids +position_ids batch tensor:batch tensor:torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +position_ids batch tensor:torch.Size([1, 1024]) + torch.Size([1, 1024])labels +torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128])batch tensor after cp: +attention_maskloss_masktorch.Size([1, 1024]) + torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: attention_maskposition_ids torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:batch tensor: labelsloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:tokens tokenstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor:tokensbatch tensor: labels torch.Size([1, 1024])torch.Size([1, 1024]) +tokens + batch tensor: loss_maskbatch tensor: torch.Size([1, 1024])labels + batch tensor after cp:tokens labels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: loss_mask torch.Size([1, 128])tokens + batch tensor after cp:torch.Size([1, 128]) +batch tensor: batch tensor:loss_mask attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + labelsbatch tensor after cp: torch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor: + torch.Size([1, 1024])attention_mask + batch tensor:batch tensor:torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:batch tensor after cp: labelsloss_mask batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + + batch tensor after cp:batch tensor after cp: tokensattention_maskloss_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) +batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128])batch tensor after cp: +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])batch tensor:labels + batch tensor after cp:tokenstorch.Size([1, 128]) position_ids + attention_maskbatch tensor: position_idstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_maskbatch tensor after cp: torch.Size([1, 128])loss_mask +torch.Size([1, 128])batch tensor after cp: + attention_maskbatch tensor after cp: attention_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: +loss_masklabels batch tensor:torch.Size([1, 1024]) torch.Size([1, 1024]) +position_ids + batch tensor after cp:batch tensor after cp:position_ids attention_masklabelstorch.Size([1, 128]) batch tensor:torch.Size([1, 1, 128, 1024]) + +torch.Size([1, 128]) + tokensbatch tensor after cp: labelstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelsloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:loss_mask attention_masktorch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:torch.Size([1, 128]) +loss_masktorch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + + position_idsbatch tensor after cp: position_idstorch.Size([1, 128]) +torch.Size([1, 128]) + batch tensor:batch tensor:torch.Size([1, 1024]) attention_mask + loss_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +tokensbatch tensor after cp:batch tensor after cp: loss_maskposition_idstorch.Size([1, 1024]) +torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor:batch tensor after cp: labelsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +torch.Size([1, 1, 128, 1024])batch tensor after cp: + attention_maskbatch tensor after cp: position_idstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024])batch tensor after cp: + batch tensor:tokens loss_mask torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp:batch tensor: labelsattention_mask torch.Size([1, 128])torch.Size([1, 1, 1024, 1024]) + +batch tensor after cp:batch tensor: loss_mask position_idstorch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokensloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_masklabels torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor:batch tensor: position_ids attention_mask torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor after cp:loss_mask position_idstorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor:position_ids torch.Size([1, 128]) +tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor after cp: labelsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp:batch tensor after cp: tokensposition_idsposition_ids torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp:batch tensor after cp: loss_maskposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor: position_idsloss_mask torch.Size([1, 128])torch.Size([1, 1024]) +batch tensor: +batch tensor: attention_mask tokens torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +tokensbatch tensor after cp: torch.Size([1, 128])position_ids + torch.Size([1, 128])batch tensor after cp:batch tensor after cp: +batch tensor after cp:batch tensor after cp:batch tensor after cp: tokenslabelslabels torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: tokenstokenstokens torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp: batch tensor after cp:batch tensor after cp:labels labelslabelstorch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: batch tensor after cp: tokens loss_masktokens torch.Size([1, 128]) +torch.Size([1, 128])torch.Size([1, 128])batch tensor after cp: + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + labelstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:loss_masktokens labels torch.Size([1, 128]) torch.Size([1, 128])loss_mask + +torch.Size([1, 128])batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: torch.Size([1, 128]) batch tensor after cp: + torch.Size([1, 128])batch tensor after cp:tokensloss_mask + labelsbatch tensor after cp: batch tensor after cp: torch.Size([1, 128]) +attention_masklabelsbatch tensor after cp: loss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([1, 128])torch.Size([1, 128]) +batch tensor after cp:loss_mask batch tensor after cp: attention_masklabels attention_mask torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) torch.Size([1, 128])batch tensor after cp: + +torch.Size([1, 1, 128, 1024]) batch tensor after cp:batch tensor after cp: +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + loss_masktorch.Size([1, 128])torch.Size([1, 128])batch tensor after cp: + + loss_masktorch.Size([1, 128])batch tensor after cp:batch tensor after cp: labels +attention_mask torch.Size([1, 128]) batch tensor after cp:torch.Size([1, 128]) + +torch.Size([1, 1, 128, 1024])attention_maskbatch tensor after cp: +batch tensor after cp:batch tensor after cp: batch tensor after cp: loss_mask attention_mask position_ids torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp:batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens batch tensor after cp:torch.Size([1, 128]) +tokens batch tensor after cp: batch tensor after cp:torch.Size([1, 128])labels + tokensbatch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskloss_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])torch.Size([1, 128])tokens + + batch tensor after cp:batch tensor after cp: position_idstorch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +attention_mask batch tensor after cp:loss_mask position_ids torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) position_ids + torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp: batch tensor after cp:torch.Size([1, 1, 128, 1024])attention_mask + loss_maskbatch tensor after cp:torch.Size([1, 1, 128, 1024]) batch tensor after cp: +torch.Size([1, 128])position_idsbatch tensor after cp: + position_idsattention_mask torch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +torch.Size([1, 128])labelsbatch tensor after cp: +labelsbatch tensor: batch tensor after cp:torch.Size([1, 128]) + attention_maskposition_ids torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + position_idstorch.Size([1, 128]) tokenstorch.Size([1, 128])batch tensor after cp:position_ids + + attention_masktorch.Size([1, 128]) +torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: labelsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:loss_mask torch.Size([1, 128])labelstorch.Size([1, 128]) + +batch tensor after cp:torch.Size([1, 128]) batch tensor after cp: +loss_mask attention_maskbatch tensor after cp:torch.Size([1, 128]) + position_idsbatch tensor after cp:tokens torch.Size([1, 128])loss_mask +torch.Size([1, 1024]) torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_mask torch.Size([1, 1, 128, 1024])batch tensor after cp:torch.Size([1, 128]) + +batch tensor after cp:attention_maskbatch tensor after cp: position_idsattention_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +batch tensor after cp: + position_idsbatch tensor after cp: position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp:batch tensor: labelsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: loss_masktokens batch tensor after cp:torch.Size([1, 128]) + torch.Size([1, 128])batch tensor after cp:tokens +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor:batch tensor after cp: loss_maskposition_ids torch.Size([1, 1024])torch.Size([1, 128]) + + batch tensor after cp:attention_mask labelstorch.Size([1, 128]) + torch.Size([1, 1, 128, 1024])batch tensor after cp:torch.Size([1, 128]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: attention_mask batch tensor after cp:torch.Size([1, 1, 1024, 1024]) +tokens batch tensor: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 1024]) +labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +labelsbatch tensor after cp: batch tensor after cp: position_idstorch.Size([1, 128]) loss_mask +torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor:position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +loss_mask batch tensor after cp:torch.Size([1, 128]) +attention_mask batch tensor after cp: torch.Size([1, 1, 128, 1024])attention_mask +batch tensor: tokens torch.Size([1, 1024]) +tokens torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor after cp: torch.Size([1, 1, 128, 1024])position_ids +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +Start exporting trace 8 +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +Done exporting trace 8 +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + [2025-06-21 22:42:17] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 44.3 | 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([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +tokens batch tensor: labels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024])labels + torch.Size([1, 1024])batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids batch tensor:torch.Size([1, 1024]) + tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor:attention_mask loss_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:position_ids attention_mask torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024])batch tensor: +batch tensor: labelstokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:labels torch.Size([1, 1024]) +tokensbatch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens batch tensor:torch.Size([1, 1024]) + batch tensor:tokens labels torch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])loss_mask +torch.Size([1, 1024]) +batch tensor:batch tensor: labelsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])tokens + batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +batch tensor:batch tensor:batch tensor: batch tensor:labelsattention_masktokens tokenstorch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: +batch tensor: position_ids batch tensor:torch.Size([1, 1024]) + batch tensor:tokens tokens torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +labels batch tensor:torch.Size([1, 1024])batch tensor: +batch tensor:torch.Size([1, 1024])batch tensor: +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: loss_maskposition_ids torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: batch tensor: tokenstokenslabelsbatch tensor after cp: torch.Size([1, 128])torch.Size([1, 1024]) +torch.Size([1, 1024])tokens +batch tensor after cp: + labelsbatch tensor:batch tensor: torch.Size([1, 128]) loss_masktorch.Size([1, 128]) + +labelsbatch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +torch.Size([1, 1024]) batch tensor:loss_mask batch tensor:torch.Size([1, 1024]) + batch tensor:labels loss_mask tokens batch tensor:torch.Size([1, 1024]) torch.Size([1, 1024]) + +batch tensor: batch tensor:tokensloss_masktorch.Size([1, 1024]) +attention_masktorch.Size([1, 1024]) +batch tensor:torch.Size([1, 1, 1024, 1024]) batch tensor: +labelstorch.Size([1, 1024]) +attention_maskbatch tensor: torch.Size([1, 1024]) batch tensor:torch.Size([1, 1, 1024, 1024]) position_ids + + labelsbatch tensor:torch.Size([1, 1024]) +batch tensor:position_idstorch.Size([1, 1024]) +loss_masktorch.Size([1, 1024])batch tensor: +attention_mask batch tensor:tokenstorch.Size([1, 1, 1024, 1024]) +labelsbatch tensor: torch.Size([1, 1024])position_idstorch.Size([1, 1024]) + batch tensor: +batch tensor:torch.Size([1, 1024])batch tensor: +batch tensor: tokens torch.Size([1, 1024])batch tensor: + batch tensor:tokens labels batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor:batch tensor after cp:torch.Size([1, 1024]) loss_mask torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp: attention_mask + attention_maskattention_maskbatch tensor: torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1, 128, 1024]) + +torch.Size([1, 1, 128, 1024])loss_maskbatch tensor: + batch tensor:tokens +batch tensor: labelsbatch tensor:batch tensor: tokensposition_ids torch.Size([1, 1024]) +loss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:attention_mask attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:position_ids position_idstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor:batch tensor:tokensloss_mask labelstokens batch tensor: tokens tokens torch.Size([1, 1024]) +batch tensor:batch tensor:tokens labelsloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + tokensbatch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: +position_ids torch.Size([1, 1024])batch tensor: + labels torch.Size([1, 1024]) +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024]) + batch tensor after cp: batch tensor after cp:position_ids torch.Size([1, 1024])position_ids +position_idstorch.Size([1, 1024])batch tensor:torch.Size([1, 128]) + +torch.Size([1, 128])attention_mask + attention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])labels +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: torch.Size([1, 1024])labelstorch.Size([1, 1024])torch.Size([1, 1024]) + torch.Size([1, 1024]) + + +torch.Size([1, 1024])batch tensor:batch tensor:torch.Size([1, 1024])batch tensor:batch tensor: + +batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: batch tensor:tokens tokensattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +torch.Size([1, 1024])batch tensor:batch tensor: + torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: batch tensor:torch.Size([1, 1024]) position_idsbatch tensor:torch.Size([1, 1024]) +labels +torch.Size([1, 1024])batch tensor:torch.Size([1, 1024])loss_mask + batch tensor: +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +labelsbatch tensor: attention_maskbatch tensor:labels loss_mask torch.Size([1, 1024])loss_mask torch.Size([1, 1, 1024, 1024])labelstorch.Size([1, 1024]) +torch.Size([1, 1024]) batch tensor:torch.Size([1, 1024]) + + +batch tensor: batch tensor: loss_mask attention_mask labelstorch.Size([1, 1, 1024, 1024]) + torch.Size([1, 1024])batch tensor:torch.Size([1, 1024])batch tensor: + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + labelsposition_idsbatch tensor: torch.Size([1, 1024])torch.Size([1, 1024]) +labels +batch tensor: torch.Size([1, 1024])loss_mask +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024]) labels batch tensor: +loss_masktorch.Size([1, 1024]) batch tensor:torch.Size([1, 1024]) + loss_maskbatch tensor:attention_mask +batch tensor after cp: position_ids torch.Size([1, 128]) + +torch.Size([1, 1024])batch tensor:batch tensor: batch tensor:batch tensor: loss_maskattention_mask + batch tensor:batch tensor:position_ids torch.Size([1, 1024]) +attention_masktokensloss_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor:torch.Size([1, 1024]) +loss_mask batch tensor:torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + torch.Size([1, 1024])batch tensor:loss_masktorch.Size([1, 1, 1024, 1024]) + +batch tensor:attention_masktorch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + position_idsloss_mask torch.Size([1, 1024])loss_masktorch.Size([1, 1, 1024, 1024]) +batch tensor: position_idsbatch tensor: attention_mask + torch.Size([1, 1024]) torch.Size([1, 1024])batch tensor: + +batch tensor:torch.Size([1, 1024]) batch tensor: +position_ids batch tensor:attention_masktorch.Size([1, 1024]) +labelstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor:batch tensor: + batch tensor:position_ids loss_mask torch.Size([1, 1024]) tokens +torch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) +attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024])position_ids + batch tensor:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor:attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])position_ids + batch tensor:torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024])attention_mask + +batch tensor after cp: tokens batch tensor after cp:torch.Size([1, 128]) + torch.Size([1, 1024])attention_maskbatch tensor:torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) + + +attention_maskbatch tensor: batch tensor:torch.Size([1, 1, 1024, 1024]) position_idstorch.Size([1, 1, 1024, 1024]) + +labels batch tensor:torch.Size([1, 1024]) +position_idsbatch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + position_idsbatch tensor: torch.Size([1, 1, 1024, 1024])position_ids +torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: + position_ids torch.Size([1, 1024]) +tokensbatch tensor after cp: labelstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:labels loss_masktorch.Size([1, 128])batch tensor after cp: +attention_maskbatch tensor: batch tensor:torch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024]) +position_idsposition_ids + torch.Size([1, 1024])torch.Size([1, 1024])batch tensor: + + position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokensbatch tensor: batch tensor:tokens torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: torch.Size([1, 128]) tokens +loss_mask batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 1024]) attention_mask + torch.Size([1, 1, 128, 1024])batch tensor: +batch tensor: labels torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: batch tensor:labels torch.Size([1, 128]) + batch tensor after cp:batch tensor after cp:tokens tokensloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 1024])batch tensor after cp:batch tensor after cp: +batch tensor:batch tensor: labelslabels batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + + batch tensor:batch tensor: tokensloss_maskloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + batch tensor after cp:tokens position_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +attention_mask batch tensor after cp:batch tensor after cp: torch.Size([1, 1, 128, 1024])labelsattention_mask +batch tensor after cp: loss_mask torch.Size([1, 128]) + batch tensor after cp:attention_mask position_idstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 128])batch tensor: + position_ids torch.Size([1, 1024]) +tokens batch tensor:loss_mask labelstorch.Size([1, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: + attention_masklabelsbatch tensor: torch.Size([1, 1, 128, 1024])torch.Size([1, 128])labels + + batch tensor after cp:batch tensor after cp:batch tensor after cp:torch.Size([1, 1024]) + loss_maskposition_idstokensbatch tensor: torch.Size([1, 128]) + torch.Size([1, 128])loss_mask + +torch.Size([1, 1024])batch tensor:batch tensor: +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp:torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + + batch tensor after cp:position_idsbatch tensor after cp: loss_masktorch.Size([1, 128])position_ids +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: batch tensor: attention_mask loss_mask labels batch tensor:torch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024])batch tensor:torch.Size([1, 1024]) + +torch.Size([1, 128])batch tensor after cp: + torch.Size([1, 1024])attention_maskbatch tensor after cp: + batch tensor:torch.Size([1, 1, 128, 1024])labels + attention_maskattention_mask batch tensor: torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])labels + batch tensor:batch tensor:torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024])tokens + tokensbatch tensor after cp:torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: batch tensor after cp: position_idstorch.Size([1, 128])tokens tokens +labels torch.Size([1, 128])batch tensor after cp:torch.Size([1, 128]) +torch.Size([1, 128]) +torch.Size([1, 128]) +labels +batch tensor after cp:batch tensor after cp: batch tensor after cp: torch.Size([1, 128])labels labels + torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: + attention_masktorch.Size([1, 128])batch tensor after cp: +position_idsposition_ids batch tensor: torch.Size([1, 1024]) +torch.Size([1, 1024])loss_mask +batch tensor: labels torch.Size([1, 1024]) + loss_mask batch tensor after cp: torch.Size([1, 128])torch.Size([1, 128]) +torch.Size([1, 128]) +loss_maskbatch tensor after cp: + batch tensor after cp: torch.Size([1, 128])batch tensor after cp: loss_mask + loss_maskattention_maskbatch tensor after cp:torch.Size([1, 128]) torch.Size([1, 128]) +attention_masktorch.Size([1, 1, 128, 1024]) + +batch tensor after cp:torch.Size([1, 1, 128, 1024])batch tensor after cp:batch tensor after cp: attention_mask +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: tokensposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_masktokens torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_idslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor:tokens batch tensor: position_ids attention_mask tokensloss_masktorch.Size([1, 1024]) +torch.Size([1, 1024]) torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) +batch tensor: +torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])position_idsbatch tensor after cp: + batch tensor:torch.Size([1, 128])loss_mask + torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + attention_mask batch tensor after cp:position_ids position_idstorch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) torch.Size([1, 128]) + +torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: position_ids position_idstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: batch tensor after cp: batch tensor after cp:labelslabels batch tensor after cp: batch tensor after cp:tokens tokens torch.Size([1, 128])torch.Size([1, 128]) tokens +torch.Size([1, 128])batch tensor after cp:torch.Size([1, 128]) + + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])tokens + batch tensor after cp: torch.Size([1, 128])position_ids +batch tensor:labelsbatch tensor: + attention_maskposition_idstorch.Size([1, 1024]) batch tensor: + torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024])labels +batch tensor: + batch tensor:torch.Size([1, 1024])loss_mask + position_idstorch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:tokens loss_maskbatch tensor after cp: batch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])loss_masklabelslabels +torch.Size([1, 128]) + torch.Size([1, 128])batch tensor after cp: +batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + batch tensor after cp:batch tensor after cp: + batch tensor after cp: + torch.Size([1, 128])batch tensor after cp: + labels torch.Size([1, 128]) + position_idsbatch tensor:torch.Size([1, 1024]) + torch.Size([1, 1024])loss_mask + batch tensor:torch.Size([1, 1024]) +attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) + attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: batch tensor after cp:loss_mask tokenstorch.Size([1, 1024]) +torch.Size([1, 128])batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +labelsattention_mask labels loss_masktorch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:loss_mask torch.Size([1, 128])tokens + batch tensor after cp: torch.Size([1, 128])attention_mask + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +labelsbatch tensor after cp: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 128]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])position_ids + torch.Size([1, 1024])batch tensor: + position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) + attention_maskbatch tensor after cp: torch.Size([1, 1, 1024, 1024])labels + batch tensor:torch.Size([1, 128]) +position_ids batch tensor after cp:torch.Size([1, 1024]) +loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:torch.Size([1, 1, 128, 1024]) batch tensor after cp: attention_masktorch.Size([1, 128])torch.Size([1, 128]) +loss_maskloss_mask +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: batch tensor after cp: tokenstokens position_ids tokenstorch.Size([1, 128])torch.Size([1, 128]) torch.Size([1, 128]) + + +torch.Size([1, 128])batch tensor after cp: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) + batch tensor after cp: +batch tensor after cp:torch.Size([1, 128])batch tensor after cp:torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp: tokenstokensattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels batch tensor after cp: labelstorch.Size([1, 128]) labels +torch.Size([1, 128]) batch tensor after cp: +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +loss_masktorch.Size([1, 128])position_ids +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor after cp: position_ids tokenslabels labelsbatch tensor after cp: torch.Size([1, 128]) tokens +torch.Size([1, 128]) torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) + torch.Size([1, 128])batch tensor after cp:loss_mask + loss_maskbatch tensor after cp:torch.Size([1, 128]) +torch.Size([1, 128])loss_maskbatch tensor after cp: + batch tensor after cp:attention_masktorch.Size([1, 128]) +attention_masktorch.Size([1, 1, 128, 1024])batch tensor after cp: + torch.Size([1, 1, 128, 1024])batch tensor after cp:attention_mask +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +attention_maskbatch tensor after cp: batch tensor after cp: torch.Size([1, 128])batch tensor after cp: torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) +loss_mask +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + position_idsbatch tensor after cp: torch.Size([1, 128]) torch.Size([1, 1, 128, 1024]) +position_ids + batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +attention_mask torch.Size([1, 128])attention_mask + + batch tensor after cp:position_idsbatch tensor after cp: torch.Size([1, 1, 128, 1024]) torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +position_idsattention_mask + batch tensor after cp: batch tensor after cp:torch.Size([1, 1, 128, 1024]) torch.Size([1, 128])position_idsposition_ids +torch.Size([1, 128]) + +batch tensor after cp:torch.Size([1, 128]) +position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: loss_maskbatch tensor after cp: torch.Size([1, 128]) +labelstorch.Size([1, 128])labelsbatch tensor after cp: +torch.Size([1, 128]) batch tensor after cp: +batch tensor: position_ids torch.Size([1, 1024]) +position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 128]) attention_mask +attention_maskbatch tensor after cp: batch tensor after cp: torch.Size([1, 1, 128, 1024]) loss_masktorch.Size([1, 1, 128, 1024]) + loss_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + +tokensbatch tensor after cp: batch tensor after cp: labelstorch.Size([1, 128])labels + torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: torch.Size([1, 128]) batch tensor after cp: +torch.Size([1, 128])position_ids +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 128]) batch tensor after cp: +labels batch tensor after cp: loss_mask torch.Size([1, 128]) loss_mask +torch.Size([1, 128]) batch tensor after cp: +torch.Size([1, 128]) batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) + batch tensor after cp:position_idsbatch tensor after cp: torch.Size([1, 128]) attention_masktorch.Size([1, 128]) +attention_mask +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_mask batch tensor after cp:attention_mask attention_mask torch.Size([1, 128]) torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: +batch tensor after cp: batch tensor after cp: attention_mask position_ids position_ids torch.Size([1, 1, 128, 1024])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) + [2025-06-21 22:42:17] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 45.7 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 8388608.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Start exporting trace 9 +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +Done exporting trace 9 +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +[after training is done] datetime: 2025-06-21 22:42:17 +saving checkpoint at iteration 10 to /mnt/sharefs/users/hao.zhang/junda/gpt-checkpoint/ctx-tp-cp-bs in torch_dist format +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.06843876838684082 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 41, takes 0.06915116310119629 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 49, takes 0.06876564025878906 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 52, takes 0.06878876686096191 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 55, takes 0.06880021095275879 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 51, takes 0.06883811950683594 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 53, takes 0.06890153884887695 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 50, takes 0.0688788890838623 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 34, takes 0.06945037841796875 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 38, takes 0.06946301460266113 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 39, takes 0.06949043273925781 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 60, takes 0.06894874572753906 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 57, takes 0.06896638870239258 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 58, takes 0.06896495819091797 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.06853151321411133 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 46, takes 0.06918740272521973 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 54, takes 0.06890392303466797 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 33, takes 0.0694892406463623 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 61, takes 0.06904220581054688 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 62, takes 0.06903076171875 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 63, takes 0.06905174255371094 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.06856727600097656 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 43, takes 0.0692446231842041 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 47, takes 0.06925654411315918 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 48, takes 0.06941938400268555 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 37, takes 0.0695333480834961 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 36, takes 0.06954693794250488 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 59, takes 0.0691976547241211 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.06883096694946289 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 42, takes 0.06931447982788086 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 44, takes 0.06928539276123047 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 35, takes 0.06952309608459473 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 56, takes 0.06982874870300293 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.0688631534576416 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 40, takes 0.06953907012939453 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 32, takes 0.07016968727111816 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.06933140754699707 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 45, takes 0.07300710678100586 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.0695652961730957 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.07056736946105957 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 20, takes 0.08046770095825195 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 22, takes 0.08048415184020996 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.08057999610900879 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.08060002326965332 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.08063650131225586 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.08067488670349121 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 19, takes 0.08051490783691406 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.08075070381164551 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 30, takes 0.08156991004943848 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 21, takes 0.08053874969482422 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.08075690269470215 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 26, takes 0.08160519599914551 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 18, takes 0.08058595657348633 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 17, takes 0.08057880401611328 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 23, takes 0.08061003684997559 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.08081221580505371 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 31, takes 0.08166933059692383 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 16, takes 0.08096694946289062 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.08115816116333008 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 27, takes 0.0816957950592041 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 29, takes 0.08172202110290527 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 28, takes 0.0817117691040039 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 25, takes 0.08178091049194336 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 24, takes 0.0823373794555664 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.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.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.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.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(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(46137344), 2), (np.int64(46137344), 3), (np.int64(41959936), 4), (np.int64(41959936), 5), (np.int64(44040192), 6), (np.int64(44040192), 7)] +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(52428800), 1), (np.int64(50331648), 2), (np.int64(50331648), 3), (np.int64(46326272), 4), (np.int64(48234496), 5), (np.int64(48234496), 6), (np.int64(46326272), 7)] 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consumed: 13864960, before: 1687425024, after: 1701289984 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 26120192, before: 1678561280, after: 1704681472 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 21983232, before: 1697333248, after: 1719316480 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 27652096, before: 1671274496, after: 1698926592 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 13672448, before: 1672392704, after: 1686065152 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 13844480, before: 1705984000, after: 1719828480 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 28413952, before: 1668861952, after: 1697275904 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 26030080, before: 1669124096, after: 1695154176 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+DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.25s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 38703104, before: 1672392704, after: 1711095808 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.2768202, rank: 38, write(sync,parallel): 0.2133467197418213 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.280726, rank: 18, write(sync,parallel): 0.22541546821594238 +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:0 consumed: 38805504, before: 1705992192, after: 1744797696 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.2813668, rank: 27, write(sync,parallel): 0.22785544395446777 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.2814534, rank: 30, write(sync,parallel): 0.22466778755187988 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.25s from forking +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:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.2894816, rank: 19, write(sync,parallel): 0.2294471263885498 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+DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3036554, rank: 54, write(sync,parallel): 0.2228984832763672 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.29s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3043063, rank: 63, write(sync,parallel): 0.20842289924621582 +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 0.27s from forking +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:1750545742.3039649, rank: 24, write(sync,parallel): 0.24366235733032227 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57769984, before: 1669586944, after: 1727356928 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3061535, rank: 40, write(sync,parallel): 0.22828984260559082 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.307953, rank: 20, write(sync,parallel): 0.2588534355163574 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57753600, before: 1674797056, after: 1732550656 +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: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:1750545742.3083823, rank: 37, write(sync,parallel): 0.24285078048706055 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3078914, rank: 59, write(sync,parallel): 0.20859169960021973 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3106883, rank: 23, write(sync,parallel): 0.2565031051635742 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3118806, rank: 44, write(sync,parallel): 0.23559069633483887 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.28s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3085845, rank: 34, write(sync,parallel): 0.24015402793884277 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 26464256, before: 1722757120, after: 1749221376 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3084133, rank: 28, write(sync,parallel): 0.2572035789489746 +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:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57614336, before: 1678094336, after: 1735708672 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.314798, rank: 16, write(sync,parallel): 0.23857522010803223 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.28s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3126876, rank: 36, write(sync,parallel): 0.2473909854888916 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57774080, before: 1671163904, after: 1728937984 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.312852, rank: 53, write(sync,parallel): 0.22870349884033203 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.29s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3153536, rank: 29, write(sync,parallel): 0.26926302909851074 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3165278, rank: 22, write(sync,parallel): 0.2571871280670166 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57430016, before: 1715585024, after: 1773015040 +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 0.30s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3160243, rank: 35, write(sync,parallel): 0.243027925491333 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.30s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57479168, before: 1666854912, after: 1724334080 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3189116, rank: 62, write(sync,parallel): 0.2246701717376709 +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 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3205488, rank: 50, write(sync,parallel): 0.23974370956420898 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3210523, rank: 48, write(sync,parallel): 0.2267599105834961 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57577472, before: 1670512640, after: 1728090112 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.322776, rank: 45, write(sync,parallel): 0.24311590194702148 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3238199, rank: 21, write(sync,parallel): 0.26525306701660156 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 57765888, before: 1671331840, after: 1729097728 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3272467, rank: 47, write(sync,parallel): 0.25450634956359863 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.29s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.28s from forking +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:1750545742.3304062, rank: 46, write(sync,parallel): 0.25336170196533203 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3299315, rank: 32, write(sync,parallel): 0.2515072822570801 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.30s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.29s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.32s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.32s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.34s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.30s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3495255, rank: 11, write(sync,parallel): 0.283231258392334 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.35s 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 0.35s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3553064, rank: 9, write(sync,parallel): 0.2873878479003906 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3567286, rank: 12, write(sync,parallel): 0.2964463233947754 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s 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 0.33s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.35s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.34s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3608675, rank: 56, write(sync,parallel): 0.25363826751708984 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.35s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.34s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.36s 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 0.31s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3656101, rank: 15, write(sync,parallel): 0.28791308403015137 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s 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 0.35s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.34s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.368613, rank: 14, write(sync,parallel): 0.30625295639038086 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.33s from forking +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.async_utils:TemporalAsyncCaller: Async process join finished after 0.34s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3717003, rank: 8, write(sync,parallel): 0.29273486137390137 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.372077, rank: 13, write(sync,parallel): 0.30896687507629395 +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 0.36s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.3762383, rank: 10, write(sync,parallel): 0.3048384189605713 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.37s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.37s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.39s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.35s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.38s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.40s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.39s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.40s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.40s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109518848, before: 1662148608, after: 1771667456 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109473792, before: 1663877120, after: 1773350912 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109580288, before: 1662492672, after: 1772072960 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109686784, before: 1658646528, after: 1768333312 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109723648, before: 1661640704, after: 1771364352 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109297664, before: 1661755392, after: 1771053056 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109445120, before: 1707040768, after: 1816485888 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.490038, rank: 7, write(sync,parallel): 0.4202244281768799 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.4970522, rank: 2, write(sync,parallel): 0.4224557876586914 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.5057077, rank: 6, write(sync,parallel): 0.42322564125061035 +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:1750545742.509061, rank: 5, write(sync,parallel): 0.4279670715332031 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.5095935, rank: 3, write(sync,parallel): 0.4355340003967285 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.5133145, rank: 1, write(sync,parallel): 0.43785905838012695 +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 0.49s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.5213816, rank: 4, write(sync,parallel): 0.44298362731933594 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109715456, before: 1990168576, after: 2099884032 +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 0.50s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.51s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.51s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.51s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.51s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750545742.577248, rank: 0, write(sync,parallel): 0.4389660358428955 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.54s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6308258, 4, gather: 0.07555389404296875 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6308265, 7, gather: 0.10828971862792969 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6309123, 3, gather: 0.09042906761169434 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6309268, 5, gather: 0.08230042457580566 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.631014, 2, gather: 0.09936356544494629 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.631063, 6, gather: 0.08180117607116699 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6311064, 12, gather: 0.2234492301940918 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6311047, 10, gather: 0.20504426956176758 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6311166, 1, gather: 0.08154916763305664 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6311128, 13, gather: 0.20914006233215332 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6311526, 9, gather: 0.23648858070373535 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6311758, 11, gather: 0.24017620086669922 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6329691, 16, gather: 0.2702000141143799 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6330216, 21, gather: 0.2614161968231201 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6330273, 20, gather: 0.27631330490112305 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6330326, 22, gather: 0.27029943466186523 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.631213, 14, gather: 0.21324491500854492 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6313384, 25, gather: 0.2925229072570801 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6314948, 27, gather: 0.31329822540283203 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6332273, 19, gather: 0.304307222366333 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6314776, 8, gather: 0.21098065376281738 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.631481, 15, gather: 0.2189016342163086 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6315665, 31, gather: 0.2878286838531494 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.633273, 23, gather: 0.2758975028991699 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750545742.6315546, 26, gather: 0.32744574546813965 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+DEBUG:megatron.training.checkpointing:rank: 10, takes 0.006124734878540039 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.006136655807495117 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.006111860275268555 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.006095409393310547 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.006124019622802734 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.0060384273529052734 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 58, takes 0.006135463714599609 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 60, takes 0.006136894226074219 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 57, takes 0.006124019622802734 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 61, takes 0.006140470504760742 to finalize ckpt save 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+DEBUG:megatron.training.checkpointing:rank: 0, takes 0.006195545196533203 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.006200313568115234 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 22, takes 0.006184816360473633 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 55, takes 0.006196022033691406 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 53, takes 0.006166696548461914 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 54, takes 0.006170034408569336 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 34, takes 0.0062139034271240234 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.00600886344909668 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 62, takes 0.006104469299316406 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 59, takes 0.006123542785644531 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.006212949752807617 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.006254911422729492 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.006255149841308594 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.00623011589050293 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.0062482357025146484 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.006255388259887695 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 21, takes 0.0062139034271240234 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 35, takes 0.006266355514526367 to finalize ckpt save +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +DEBUG:megatron.training.checkpointing:rank: 23, takes 0.00616455078125 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 36, takes 0.006436824798583984 to finalize ckpt save + +batch tensor: labelsbatch tensor: torch.Size([1, 1024])labels +Evaluating on 1 samples +Evaluating iter 1/1 +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +DEBUG:megatron.training.checkpointing:rank: 38, takes 0.0064661502838134766 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 37, takes 0.006474733352661133 to finalize ckpt save +batch tensor: tokensbatch tensor: tokens torch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])labels +torch.Size([1, 1024]) + batch tensor:torch.Size([1, 1024]) +loss_maskbatch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024]) +batch tensor: attention_maskbatch tensor: attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor:batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])tokens + batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: batch tensor:tokens tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor:batch tensor: + labels batch tensor:tokens torch.Size([1, 1024])labels +torch.Size([1, 1024])batch tensor: + loss_maskbatch tensor: torch.Size([1, 1024]) torch.Size([1, 1024])loss_mask + +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])batch tensor: +DEBUG:megatron.training.checkpointing:rank: 39, takes 0.006390094757080078 to finalize ckpt save +batch tensor:batch tensor: labelsloss_mask torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + + batch tensor:position_ids position_idstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labelsbatch tensor: torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor: position_idstokens torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: batch tensor: torch.Size([1, 1024])labelsattention_mask + torch.Size([1, 1024])batch tensor:torch.Size([1, 1, 1024, 1024]) + +batch tensor: attention_masktokens torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: batch tensor: tokens loss_maskattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: torch.Size([1, 1024])attention_maskposition_idsbatch tensor: + torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: tokens torch.Size([1, 1024]) +tokensbatch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:attention_mask batch tensor:loss_mask torch.Size([1, 1, 1024, 1024])position_idstorch.Size([1, 1024]) + + torch.Size([1, 1024])batch tensor:batch tensor: + position_idsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokensbatch tensor:batch tensor: labelsposition_ids torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024])labels + torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: loss_maskattention_mask torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: attention_maskposition_ids torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +labelsbatch tensor: batch tensor:torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labelsbatch tensor: torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + tokensposition_ids batch tensor:tokens loss_masktorch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: + batch tensor:tokensbatch tensor: loss_mask torch.Size([1, 1024])tokens + batch tensor:torch.Size([1, 1024]) attention_mask + torch.Size([1, 1, 1024, 1024])batch tensor: +torch.Size([1, 1024]) labelsbatch tensor: +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask batch tensor:torch.Size([1, 1, 1024, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) + tokensattention_maskbatch tensor:batch tensor: torch.Size([1, 1, 1024, 1024])labelslabels + torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + position_idstorch.Size([1, 1024]) + batch tensor:batch tensor: torch.Size([1, 1024])labelsloss_mask + torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:loss_mask attention_masktorch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokensposition_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +torch.Size([1, 1024]) batch tensor: + +position_ids loss_maskbatch tensor:batch tensor:torch.Size([1, 1024]) labels + attention_maskbatch tensor: position_idstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + loss_masktorch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor:batch tensor: batch tensor: +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +attention_maskbatch tensor: loss_masktokensattention_masktorch.Size([1, 1, 1024, 1024]) + torch.Size([1, 1024])batch tensor:torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024])tokens +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: batch tensor:attention_mask torch.Size([1, 1, 128, 1024]) +tokensbatch tensor after cp: position_ids torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +batch tensor:batch tensor: tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024])batch tensor after cp: + +torch.Size([1, 1024]) batch tensor:batch tensor:position_ids + batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + batch tensor:tokens labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: batch tensor:tokenstokens batch tensor: labelstorch.Size([1, 128])torch.Size([1, 128]) torch.Size([1, 1024]) + +tokens + attention_maskposition_idstorch.Size([1, 1024])batch tensor: +torch.Size([1, 1024])batch tensor: + attention_mask torch.Size([1, 1, 1024, 1024])batch tensor: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: attention_maskbatch tensor: torch.Size([1, 1, 1024, 1024]) +tokens batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor:attention_mask labels torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: position_idsloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor:batch tensor:batch tensor: labels labelstorch.Size([1, 1024])tokenstokens + torch.Size([1, 1024])batch tensor: + batch tensor:loss_mask loss_masktorch.Size([1, 1024]) torch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024]) + + +batch tensor after cp: batch tensor after cp:tokens batch tensor after cp:tokenstorch.Size([1, 128]) + tokens batch tensor after cp: torch.Size([1, 128])labels + torch.Size([1, 128])torch.Size([1, 128]) +batch tensor after cp: +batch tensor after cp:batch tensor after cp: labelsbatch tensor: labelstorch.Size([1, 1024]) torch.Size([1, 128]) + +torch.Size([1, 128])loss_maskbatch tensor after cp: + labelsbatch tensor:torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024]) +tokens + batch tensor:labelsbatch tensor: position_idstorch.Size([1, 1024]) tokens +batch tensor after cp:batch tensor after cp:batch tensor: loss_masktokens torch.Size([1, 128])torch.Size([1, 128])tokens + + batch tensor after cp:batch tensor after cp: attention_masklabels torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: batch tensor:batch tensor:attention_maskbatch tensor: labelslabelsbatch tensor: attention_masktorch.Size([1, 1, 1024, 1024]) torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024])tokens + +batch tensor: + batch tensor after cp:batch tensor after cp:labels labelsloss_mask batch tensor after cp: torch.Size([1, 128])torch.Size([1, 128]) torch.Size([1, 128]) + +tokens + batch tensor after cp:batch tensor after cp: batch tensor after cp:torch.Size([1, 128])loss_mask + loss_maskbatch tensor after cp:attention_mask torch.Size([1, 128]) + batch tensor:batch tensor after cp: loss_masktorch.Size([1, 1024])labelsloss_mask + torch.Size([1, 128])batch tensor: + torch.Size([1, 1024])torch.Size([1, 128])batch tensor after cp: +attention_mask + batch tensor:batch tensor after cp:torch.Size([1, 1, 1024, 1024]) attention_mask +batch tensor: batch tensor:position_ids loss_masktorch.Size([1, 1024])torch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor: + batch tensor:torch.Size([1, 1024]) +loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +torch.Size([1, 1024])batch tensor after cp:batch tensor after cp: +position_idsloss_mask torch.Size([1, 128])batch tensor:torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) + position_idsbatch tensor:batch tensor:batch tensor: torch.Size([1, 1024]) + position_idsloss_masktorch.Size([1, 1024])loss_mask + torch.Size([1, 1024])torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + + torch.Size([1, 128])labelsbatch tensor after cp: torch.Size([1, 1, 128, 1024]) + torch.Size([1, 128]) +attention_maskbatch tensor after cp: +batch tensor after cp: batch tensor after cp:position_idsattention_masktorch.Size([1, 1, 128, 1024]) torch.Size([1, 128]) + +loss_masktorch.Size([1, 1, 128, 1024])batch tensor after cp: + torch.Size([1, 128])batch tensor after cp:position_ids +loss_mask attention_maskbatch tensor: torch.Size([1, 1, 128, 1024])torch.Size([1, 1024]) torch.Size([1, 1, 128, 1024]) + + + labelsbatch tensor: torch.Size([1, 1024])attention_mask + batch tensor:torch.Size([1, 1, 1024, 1024]) + attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])labels +labels torch.Size([1, 1024])batch tensor after cp:batch tensor after cp: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) + batch tensor: + batch tensor after cp:position_idstorch.Size([1, 128]) +attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:position_idsbatch tensor after cp:batch tensor: torch.Size([1, 1024]) position_idsposition_ids +loss_maskbatch tensor: torch.Size([1, 1024])position_ids + batch tensor:torch.Size([1, 1024]) + batch tensor:torch.Size([1, 1024]) + attention_masktokens batch tensor: torch.Size([1, 1, 128, 1024])loss_masktorch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:labels attention_maskattention_mask torch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: +batch tensor: batch tensor: loss_mask position_idsposition_ids torch.Size([1, 1024]) torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +attention_mask batch tensor:torch.Size([1, 128]) batch tensor after cp: + torch.Size([1, 128]) torch.Size([1, 1, 1024, 1024])tokens +tokens +attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) + batch tensor after cp:batch tensor after cp:torch.Size([1, 1024]) labels +position_ids batch tensor:torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: labels batch tensor after cp:torch.Size([1, 128]) +tokens batch tensor after cp: loss_masktorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels attention_masktorch.Size([1, 128]) +batch tensor after cp:batch tensor: tokens tokenstorch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 1024])torch.Size([1, 128]) + +batch tensor after cp: attention_maskbatch tensor: labelstorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1024])batch tensor after cp: +torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor:torch.Size([1, 128])torch.Size([1, 1024]) +position_ids +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +loss_maskbatch tensor: torch.Size([1, 1024]) +attention_maskbatch tensor after cp: loss_maskbatch tensor after cp: torch.Size([1, 1, 1024, 1024])torch.Size([1, 128]) + +torch.Size([1, 1, 128, 1024])batch tensor after cp: + loss_maskbatch tensor after cp: torch.Size([1, 128])position_ids + torch.Size([1, 128])batch tensor after cp: + batch tensor:position_ids loss_mask torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: torch.Size([1, 1024])batch tensor:labels +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128])batch tensor after cp: + tokensbatch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: +tokens batch tensor after cp:batch tensor: attention_masktorch.Size([1, 128])position_ids +attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +labelstorch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: + tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) + tokensbatch tensor after cp: position_idstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + position_ids batch tensor:torch.Size([1, 1024]) +labels torch.Size([1, 1024]) + batch tensor after cp:torch.Size([1, 1, 128, 1024])torch.Size([1, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor:loss_mask loss_masktorch.Size([1, 128]) + labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +labelsbatch tensor after cp: position_idstorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128])batch tensor after cp: + tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: position_idstokens torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor after cp: +batch tensor after cp:batch tensor after cp: batch tensor after cp:tokens tokens torch.Size([1, 128])tokenstorch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: batch tensor after cp:attention_mask torch.Size([1, 1, 1024, 1024])tokens + attention_maskbatch tensor: torch.Size([1, 1, 128, 1024])attention_mask +torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:torch.Size([1, 128])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +tokensbatch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor: torch.Size([1, 128])position_ids + batch tensor after cp:torch.Size([1, 1024]) +labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor: position_idsposition_ids torch.Size([1, 1024])torch.Size([1, 128]) + +labels batch tensor after cp: labels torch.Size([1, 128])labels batch tensor after cp: torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp:tokensbatch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor after cp:loss_maskloss_mask torch.Size([1, 128]) + torch.Size([1, 128])loss_maskbatch tensor after cp: +torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: batch tensor after cp: attention_masktokens torch.Size([1, 1, 128, 1024])tokens + torch.Size([1, 128])batch tensor after cp: + position_idsbatch tensor after cp: torch.Size([1, 128])torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:labelsbatch tensor after cp:torch.Size([1, 128]) attention_mask + torch.Size([1, 128])attention_maskbatch tensor after cp: +torch.Size([1, 1, 128, 1024]) batch tensor after cp: + torch.Size([1, 1, 128, 1024])attention_maskbatch tensor after cp: loss_mask + torch.Size([1, 1, 128, 1024])batch tensor after cp: position_ids +labels + torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:tokens attention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + torch.Size([1, 128]) +batch tensor after cp:position_idstorch.Size([1, 128])batch tensor after cp: + position_idstorch.Size([1, 128])attention_mask + torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +batch tensor after cp: +batch tensor after cp:batch tensor after cp: labelsloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:position_ids labelstorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: tokensposition_ids torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_mask loss_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 128])batch tensor after cp: +position_ids batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp:batch tensor: tokensposition_ids torch.Size([1, 128])torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: loss_masklabels torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:attention_mask loss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: position_idsattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +attention_mask torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: tokens tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 128]) +labels batch tensor after cp:torch.Size([1, 128]) +labelsbatch tensor after cp: torch.Size([1, 128])loss_mask +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) + batch tensor after cp:batch tensor after cp: torch.Size([1, 128])loss_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokensbatch tensor after cp: batch tensor after cp: attention_mask torch.Size([1, 128])attention_masktorch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor: position_ids torch.Size([1, 1024]) + +torch.Size([1, 1, 128, 1024])batch tensor after cp:batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor after cp:labelsposition_ids position_idstorch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:attention_mask attention_masktorch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + position_idsbatch tensor after cp: torch.Size([1, 128])position_ids + torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +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 .......................................: (2978.95, 2980.55) +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +---------------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on validation set | lm loss value: 1.178553E+01 | lm loss PPL: 1.313386E+05 | +---------------------------------------------------------------------------------------------------------------- +Evaluating on 1 samples +Evaluating iter 1/1 +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_maskbatch tensor: batch tensor:torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) + tokensbatch tensor: tokensattention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: torch.Size([1, 1024])position_idstorch.Size([1, 1024]) + +torch.Size([1, 1024]) +batch tensor: batch tensor:labels labels torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor:batch tensor: tokenstokens tokens torch.Size([1, 1024])torch.Size([1, 1024])torch.Size([1, 1024]) + + +batch tensor:batch tensor:batch tensor:batch tensor: labels tokenslabels labelstorch.Size([1, 1024]) +torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor: + torch.Size([1, 1024])batch tensor:loss_maskbatch tensor: batch tensor: +loss_masktorch.Size([1, 1024])loss_mask +batch tensor: tokenstorch.Size([1, 1024])batch tensor: torch.Size([1, 1024]) labels + + attention_maskbatch tensor:torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024])attention_mask +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: batch tensor:batch tensor: loss_masktorch.Size([1, 1, 1024, 1024])loss_maskposition_ids + torch.Size([1, 1024])torch.Size([1, 1024])batch tensor:torch.Size([1, 1024]) + + +batch tensor:batch tensor:batch tensor: tokens tokens torch.Size([1, 1024]) +tokenstorch.Size([1, 1024]) +batch tensor: labels batch tensor:torch.Size([1, 1024])torch.Size([1, 1024]) + +labels torch.Size([1, 1024])batch tensor: + batch tensor:loss_maskbatch tensor: labelstorch.Size([1, 1024])loss_mask + torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: batch tensor:attention_maskbatch tensor: loss_mask attention_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) +torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor:batch tensor: attention_maskposition_idsposition_ids torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokensbatch tensor: tokens torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor: +labels torch.Size([1, 1024]) +batch tensor: labels batch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024])batch tensor: + loss_maskbatch tensor: torch.Size([1, 1024])attention_mask + torch.Size([1, 1, 1024, 1024])batch tensor: + attention_maskbatch tensor: position_idstorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])batch tensor: + position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens batch tensor:torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor:batch tensor: tokens tokens tokens torch.Size([1, 1024])torch.Size([1, 1024]) +torch.Size([1, 1024])batch tensor:batch tensor: + + labelstokensbatch tensor: batch tensor: torch.Size([1, 1024]) labels +torch.Size([1, 1024])labels batch tensor: + torch.Size([1, 1024])batch tensor:torch.Size([1, 1024])loss_mask + + batch tensor:labelsbatch tensor:batch tensor: loss_masktokensloss_mask torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: torch.Size([1, 1024]) +batch tensor: torch.Size([1, 1024])batch tensor: + attention_mask batch tensor: position_idstorch.Size([1, 1024]) loss_masktorch.Size([1, 1, 1024, 1024]) +attention_mask +torch.Size([1, 1024]) batch tensor: +torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1, 1024, 1024]) batch tensor:position_ids +labels batch tensor:attention_masktorch.Size([1, 1024]) + position_idstorch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: loss_maskbatch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024]) +batch tensor: attention_maskbatch tensor: attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024]) +batch tensor: batch tensor:position_ids position_idstorch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +position_idsbatch tensor:batch tensor: attention_masktorch.Size([1, 1024]) +attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: + position_idsbatch tensor: torch.Size([1, 1024])position_ids + torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: batch tensor:labels torch.Size([1, 1024]) +tokens batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:torch.Size([1, 1024]) attention_mask + torch.Size([1, 1, 1024, 1024])batch tensor: + labelsbatch tensor: torch.Size([1, 1024])position_ids + torch.Size([1, 1024])batch tensor: + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens batch tensor: torch.Size([1, 1024])tokens +batch tensor: labels torch.Size([1, 1024])torch.Size([1, 1024]) + + batch tensor: tokenslabels torch.Size([1, 1024]) +batch tensor: loss_masktorch.Size([1, 1024]) torch.Size([1, 1024]) + +batch tensor:batch tensor: batch tensor: attention_masklabels torch.Size([1, 1, 1024, 1024]) torch.Size([1, 1024]) +tokensbatch tensor: +batch tensor: batch tensor:position_ids tokenstorch.Size([1, 1024]) torch.Size([1, 1024])loss_mask + + batch tensor:batch tensor:torch.Size([1, 1024]) torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: batch tensor: position_idsloss_mask torch.Size([1, 1024])tokens +torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: +batch tensor: tokensbatch tensor: labels tokenstorch.Size([1, 1024])position_ids + torch.Size([1, 1024])batch tensor: +loss_mask torch.Size([1, 1024])torch.Size([1, 1024])batch tensor: + +torch.Size([1, 1024])labelsbatch tensor: torch.Size([1, 1024])attention_mask + + torch.Size([1, 1, 1024, 1024])batch tensor:batch tensor: + loss_maskbatch tensor:labels torch.Size([1, 1024])position_idstorch.Size([1, 1024]) + +torch.Size([1, 1024])batch tensor: + batch tensor:attention_mask loss_mask torch.Size([1, 1, 1024, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: position_idsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +tokens batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor:batch tensor: attention_mask torch.Size([1, 1, 1024, 1024])tokens + batch tensor: position_ids torch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask batch tensor:torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: loss_masklabels torch.Size([1, 1024])torch.Size([1, 1024]) + +batch tensor:batch tensor: loss_maskattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor:batch tensor: position_idsattention_mask torch.Size([1, 1024])torch.Size([1, 1, 1024, 1024]) + +batch tensor: position_ids torch.Size([1, 1024]) +labels + batch tensor:tokensbatch tensor:torch.Size([1, 1024]) labels +attention_mask batch tensor: torch.Size([1, 1024]) torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024])loss_mask +batch tensor: + batch tensor: batch tensor:torch.Size([1, 1024])batch tensor: +loss_maskposition_ids batch tensor:labels torch.Size([1, 1024])torch.Size([1, 1024]) + torch.Size([1, 1024])tokens +attention_mask +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_masktokens torch.Size([1, 1, 1024, 1024]) +batch tensor: torch.Size([1, 1024])position_ids +torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) + batch tensor: torch.Size([1, 1024])batch tensor:attention_mask torch.Size([1, 1, 1024, 1024]) +loss_masktorch.Size([1, 1, 1024, 1024]) +batch tensor:torch.Size([1, 1024]) + +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: tokenstokenstokens torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp:batch tensor after cp:batch tensor after cp: labelslabelslabels torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: tokenslabels tokenstorch.Size([1, 128])torch.Size([1, 128]) + +batch tensor:batch tensor:labels position_idsbatch tensor: position_idstorch.Size([1, 1024])torch.Size([1, 1024]) +attention_mask + torch.Size([1, 1024])batch tensor:torch.Size([1, 1, 1024, 1024]) + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor:batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: loss_maskloss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp:batch tensor after cp: batch tensor after cp:batch tensor after cp: attention_masktokensattention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +torch.Size([1, 1, 128, 1024]) +batch tensor after cp: + +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128])batch tensor after cp: +batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) +loss_masklabelsbatch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])labels + + batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) +loss_mask batch tensor:torch.Size([1, 1024]) +position_ids batch tensor:torch.Size([1, 1024]) +tokens torch.Size([1, 1024]) + batch tensor after cp:batch tensor after cp:batch tensor after cp:position_ids position_idstorch.Size([1, 128]) labelsbatch tensor after cp:position_idsbatch tensor after cp: +torch.Size([1, 128]) batch tensor after cp: +torch.Size([1, 128])tokensbatch tensor after cp:tokens torch.Size([1, 128])tokens +torch.Size([1, 128]) + +batch tensor after cp:torch.Size([1, 128])tokens +torch.Size([1, 128])batch tensor after cp: labelsbatch tensor after cp: +loss_mask torch.Size([1, 128]) batch tensor after cp:torch.Size([1, 128]) +torch.Size([1, 128])labels + +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask batch tensor after cp:torch.Size([1, 128]) + batch tensor after cp:tokens labels torch.Size([1, 128])torch.Size([1, 128]) + + attention_maskloss_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])torch.Size([1, 128])loss_mask +batch tensor after cp: + batch tensor after cp:torch.Size([1, 128])batch tensor after cp:tokens +attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: labels torch.Size([1, 1024]) +tokens batch tensor: loss_mask torch.Size([1, 1024]) +labelsbatch tensor after cp:batch tensor after cp: batch tensor after cp: torch.Size([1, 128]) torch.Size([1, 128])labelsattention_mask + +loss_maskbatch tensor after cp: batch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 1, 128, 1024])loss_maskloss_mask + + +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor after cp:tokens batch tensor after cp:attention_masktorch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: labelsloss_maskbatch tensor after cp: torch.Size([1, 128])torch.Size([1, 128])tokens + + attention_mask batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) attention_maskposition_ids + torch.Size([1, 1, 128, 1024])batch tensor after cp:torch.Size([1, 1, 128, 1024]) + tokens +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor:torch.Size([1, 1024]) attention_mask +torch.Size([1, 1, 1024, 1024]) + batch tensor after cp:torch.Size([1, 128])batch tensor after cp:batch tensor after cp:torch.Size([1, 128]) +attention_mask loss_mask +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +tokenstorch.Size([1, 1, 128, 1024])batch tensor after cp: + batch tensor:torch.Size([1, 128])batch tensor after cp:labels + batch tensor after cp:batch tensor after cp: torch.Size([1, 128])attention_mask +torch.Size([1, 1, 128, 1024])loss_maskbatch tensor after cp: +batch tensor after cp:torch.Size([1, 128])labels batch tensor after cp:torch.Size([1, 128])torch.Size([1, 128]) + + +position_idsposition_idsbatch tensor after cp:batch tensor after cp: torch.Size([1, 128])torch.Size([1, 128]) labels + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: batch tensor:labels position_idstokenstorch.Size([1, 1024]) +torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024])torch.Size([1, 1024]) +batch tensor after cp: position_idstorch.Size([1, 1, 128, 1024]) batch tensor after cp:attention_masktorch.Size([1, 128]) + + torch.Size([1, 128])batch tensor after cp:batch tensor after cp:attention_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +tokens torch.Size([1, 128])position_idsbatch tensor after cp: +torch.Size([1, 128]) batch tensor after cp: +labels loss_mask torch.Size([1, 128])torch.Size([1, 1024]) + batch tensor after cp:labels torch.Size([1, 128]) position_idstorch.Size([1, 128]) + +torch.Size([1, 128])batch tensor after cp:batch tensor after cp: +loss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp:batch tensor after cp: loss_masktokens attention_masktorch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + +torch.Size([1, 1, 128, 1024]) torch.Size([1, 1, 128, 1024])position_ids +attention_mask +batch tensor after cp: batch tensor after cp:position_idstorch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])tokens + batch tensor after cp: loss_masktorch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: + loss_maskattention_mask torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) + + torch.Size([1, 128])torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor after cp: labels tokenstorch.Size([1, 128]) +batch tensor after cp: batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: +position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + loss_mask torch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: + attention_mask batch tensor after cp:torch.Size([1, 1, 128, 1024]) +labelsbatch tensor after cp: torch.Size([1, 128])position_ids +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask batch tensor: torch.Size([1, 1, 1024, 1024])labels + torch.Size([1, 1024])batch tensor: + position_ids batch tensor:torch.Size([1, 1024]) + +position_idstorch.Size([1, 128])batch tensor after cp: + torch.Size([1, 128])position_ids + torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: tokensloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +torch.Size([1, 128])batch tensor after cp: + labelsbatch tensor after cp: attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + loss_maskbatch tensor after cp: torch.Size([1, 128])position_ids + torch.Size([1, 128])batch tensor after cp: + batch tensor after cp:batch tensor: loss_masklabels batch tensor after cp:attention_masktorch.Size([1, 1024])torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_maskposition_ids torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + + torch.Size([1, 128])batch tensor after cp: + loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp: batch tensor after cp:attention_mask labels torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: position_idsloss_mask torch.Size([1, 128])torch.Size([1, 128]) + attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) tokens + +torch.Size([1, 1, 128, 1024])batch tensor after cp: +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: position_ids torch.Size([1, 1024]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024])batch tensor after cp: +batch tensor after cp: tokens torch.Size([1, 128])batch tensor after cp: + batch tensor:torch.Size([1, 128])position_idsbatch tensor after cp: + torch.Size([1, 128])batch tensor after cp:position_idsloss_mask +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:tokens tokens torch.Size([1, 128]) +torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + batch tensor after cp:tokens position_ids torch.Size([1, 128])torch.Size([1, 128]) + + tokensbatch tensor after cp: labels torch.Size([1, 128])torch.Size([1, 128]) + + labelstorch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])labels + batch tensor after cp:torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels loss_masktorch.Size([1, 128]) +torch.Size([1, 128])batch tensor after cp: +torch.Size([1, 128])batch tensor: + attention_maskbatch tensor after cp: torch.Size([1, 1, 1024, 1024])loss_mask +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +loss_mask batch tensor after cp: torch.Size([1, 128])loss_mask + batch tensor after cp:torch.Size([1, 128]) +attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])attention_mask +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) + batch tensor after cp:loss_mask attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024])batch tensor after cp: + attention_maskbatch tensor after cp: torch.Size([1, 1, 128, 1024])position_ids + batch tensor after cp:torch.Size([1, 128]) +position_ids torch.Size([1, 128]) + torch.Size([1, 128])batch tensor: + position_idsbatch tensor after cp: torch.Size([1, 1024])attention_mask + torch.Size([1, 1, 128, 1024]) +batch tensor after cp:batch tensor: tokens torch.Size([1, 128])tokens + batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:torch.Size([1, 1024]) loss_mask + torch.Size([1, 128])batch tensor: + labelsbatch tensor after cp: torch.Size([1, 1024])attention_mask + torch.Size([1, 1, 128, 1024])batch tensor: + batch tensor after cp:loss_mask position_idstorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) + batch tensor after cp:torch.Size([1, 1, 128, 1024]) +position_idsbatch tensor after cp: torch.Size([1, 128])position_ids + torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor:batch tensor after cp: tokens tokenstorch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +torch.Size([1, 1024])batch tensor after cp: +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])tokens +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +loss_maskbatch tensor: torch.Size([1, 128])labels +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) + batch tensor after cp: batch tensor after cp:torch.Size([1, 128])loss_mask + tokenstorch.Size([1, 128]) +batch tensor after cp: batch tensor after cp:labels torch.Size([1, 128])attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) + +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024])batch tensor after cp: +batch tensor after cp: batch tensor after cp: tokens tokensposition_ids torch.Size([1, 128])torch.Size([1, 128])batch tensor:torch.Size([1, 128]) + + + batch tensor after cp:torch.Size([1, 1024]) +attention_mask batch tensor:torch.Size([1, 1, 128, 1024]) +loss_maskbatch tensor after cp: position_idstorch.Size([1, 1024]) +torch.Size([1, 128]) +batch tensor: batch tensor:tokens tokens torch.Size([1, 1024]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp:batch tensor after cp: labelsloss_maskposition_ids torch.Size([1, 128])torch.Size([1, 128])torch.Size([1, 128]) + + +batch tensor after cp: batch tensor after cp:tokenslabels labels torch.Size([1, 128])torch.Size([1, 128]) +torch.Size([1, 1024]) +batch tensor after cp: +batch tensor after cp:loss_mask batch tensor:loss_masktorch.Size([1, 128]) +labelstorch.Size([1, 128])batch tensor after cp: + torch.Size([1, 1024])batch tensor after cp:attention_mask + attention_maskbatch tensor: batch tensor:torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + + batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp:batch tensor after cp: attention_maskloss_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 128]) + +batch tensor after cp: batch tensor after cp:position_ids attention_masktorch.Size([1, 128]) +torch.Size([1, 1, 128, 1024]) + loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor:batch tensor: position_ids tokenstorch.Size([1, 1024]) +batch tensor: labels batch tensor: torch.Size([1, 1024])labels + torch.Size([1, 1024])batch tensor: + loss_maskbatch tensor: torch.Size([1, 1024])loss_mask + torch.Size([1, 1024])batch tensor: +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: tokens torch.Size([1, 1024]) +torch.Size([1, 1024]) + attention_mask batch tensor: attention_masktorch.Size([1, 1, 1024, 1024]) +torch.Size([1, 1, 1024, 1024])batch tensor: + position_ids batch tensor: torch.Size([1, 1024])position_ids +batch tensor: tokens torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor: position_ids torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor: loss_mask torch.Size([1, 1024]) + torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor: attention_mask batch tensor after cp:torch.Size([1, 1, 1024, 1024]) +tokens batch tensor:torch.Size([1, 128]) +batch tensor: attention_mask torch.Size([1, 1, 1024, 1024]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +position_idsbatch tensor after cp: labelstorch.Size([1, 1024]) torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor:batch tensor: position_ids torch.Size([1, 1024])tokens +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) + torch.Size([1, 1024]) +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([1, 1, 128, 1024])torch.Size([1, 1, 128, 1024]) + +batch tensor after cp:batch tensor after cp: position_idsposition_ids torch.Size([1, 128])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor: labels torch.Size([1, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor: loss_mask torch.Size([1, 1024]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp:batch tensor: attention_masktokens torch.Size([1, 1, 1024, 1024])torch.Size([1, 128]) + +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor:batch tensor after cp: position_idslabels torch.Size([1, 128])torch.Size([1, 1024]) + +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +(min, max) time across ranks (ms): + evaluate .......................................: (33.47, 34.54) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +---------------------------------------------------------------------------------------------------------- +batch tensor after cp: position_ids torch.Size([1, 128]) + validation loss at iteration 10 on test set | lm loss value: 1.178553E+01 | lm loss PPL: 1.313386E+05 | +---------------------------------------------------------------------------------------------------------- +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +batch tensor after cp: tokens torch.Size([1, 128]) +batch tensor after cp: labels torch.Size([1, 128]) +batch tensor after cp: loss_mask torch.Size([1, 128]) +batch tensor after cp: attention_mask torch.Size([1, 1, 128, 1024]) +batch tensor after cp: position_ids torch.Size([1, 128]) +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