diff --git a/libero_10_ckpt/checkpoint-100000/config.json b/libero_10_ckpt/checkpoint-100000/config.json new file mode 100644 index 0000000000000000000000000000000000000000..06e4fb31e6ac9b7412391f49c0b655cb5488f035 --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/config.json @@ -0,0 +1,70 @@ +{ + "action_horizon": 50, + "add_pos_embed": true, + "apply_sincos_state_encoding": true, + "architectures": [ + "Gr00tN1d6" + ], + "attn_dropout": 0.2, + "attn_implementation": null, + "backbone_embedding_dim": 2048, + "backbone_model_type": "eagle", + "backbone_trainable_params_fp32": true, + "collator_overwrite_image_inputs": false, + "color_jitter_params": { + "brightness": 0.1, + "contrast": 0.1, + "hue": 0.1, + "saturation": 0.1 + }, + "crop_fraction": 0.95, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "eagle_collator": true, + "formalize_language": true, + "gemma_collator": false, + "hidden_size": 1024, + "image_crop_size": null, + "image_target_size": null, + "input_embedding_dim": 1536, + "load_bf16": true, + "max_action_dim": 128, + "max_num_embodiments": 32, + "max_seq_len": 1024, + "max_state_dim": 128, + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "model_type": "Gr00tN1d6", + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_inference_timesteps": 4, + "num_timestep_buckets": 1000, + "random_rotation_angle": null, + "reproject_vision": false, + "select_layer": 16, + "shortest_image_edge": 256, + "state_dropout_prob": 0.8, + "torch_dtype": "bfloat16", + "transformers_version": "4.51.3", + "tune_diffusion_model": true, + "tune_llm": false, + "tune_projector": true, + "tune_top_llm_layers": 4, + "tune_visual": false, + "tune_vlln": true, + "use_albumentations_transforms": true, + "use_alternate_vl_dit": true, + "use_flash_attention": true, + "use_relative_action": true, + "use_vlln": true +} diff --git a/libero_10_ckpt/checkpoint-100000/embodiment_id.json b/libero_10_ckpt/checkpoint-100000/embodiment_id.json new file mode 100644 index 0000000000000000000000000000000000000000..9db6cf1b1236298d5de030192d4d9c3f1d143e4c --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/embodiment_id.json @@ -0,0 +1,11 @@ +{ + "robocasa_panda_omron": 13, + "gr1": 20, + "behavior_r1_pro": 24, + "unitree_g1": 8, + "oxe_google": 0, + "oxe_widowx": 1, + "libero_panda": 2, + "oxe_droid": 16, + "new_embodiment": 10 +} \ No newline at end of file diff --git a/libero_10_ckpt/checkpoint-100000/experiment_cfg/conf.yaml b/libero_10_ckpt/checkpoint-100000/experiment_cfg/conf.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6f8d5e87ce636ac2559aaf04eba53dead6c5baa1 --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/experiment_cfg/conf.yaml @@ -0,0 +1,244 @@ +load_config_path: null +model: + model_type: Gr00tN1d6 + model_dtype: bfloat16 + model_name: nvidia/Eagle-Block2A-2B-v2 + backbone_model_type: eagle + model_revision: null + tune_top_llm_layers: 4 + backbone_embedding_dim: 2048 + tune_llm: false + tune_visual: false + select_layer: 16 + reproject_vision: false + use_flash_attention: true + load_bf16: false + collator_overwrite_image_inputs: false + eagle_collator: true + backbone_trainable_params_fp32: true + image_crop_size: null + image_target_size: null + shortest_image_edge: 256 + crop_fraction: 0.95 + random_rotation_angle: null + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + use_albumentations_transforms: true + formalize_language: true + apply_sincos_state_encoding: false + use_relative_action: true + max_state_dim: 29 + max_action_dim: 29 + action_horizon: 16 + hidden_size: 1024 + input_embedding_dim: 1536 + add_pos_embed: true + attn_dropout: 0.2 + use_vlln: true + max_seq_len: 1024 + use_alternate_vl_dit: true + attend_text_every_n_blocks: 2 + diffusion_model_cfg: + positional_embeddings: null + num_layers: 32 + num_attention_heads: 32 + attention_head_dim: 48 + norm_type: ada_norm + dropout: 0.2 + final_dropout: true + output_dim: 1024 + interleave_self_attention: true + num_inference_timesteps: 4 + noise_beta_alpha: 1.5 + noise_beta_beta: 1.0 + noise_s: 0.999 + num_timestep_buckets: 1000 + tune_projector: true + tune_diffusion_model: true + tune_vlln: true + state_dropout_prob: 0.8 + state_additive_noise_scale: 0.0 + use_lie_group_rotation: false + lie_rot_start: 0 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_num_langevin_steps: 100 + max_num_embodiments: 32 +data: + datasets: + - dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + embodiment_tag: libero_panda + mix_ratio: 1.0 + dataset_type: physical_embodiment + val_dataset_path: null + modality_configs: + libero_panda: + video: + delta_indices: + - 0 + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + state: + delta_indices: + - 0 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + action: + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + language: + delta_indices: + - 0 + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + download_cache: false + shard_size: 1024 + episode_sampling_rate: 0.1 + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + mode: single_turn + random_chop: 0.0 + mock_dataset_mode: false + shuffle: true + seed: 42 + multiprocessing_context: fork + allow_padding: false + subsample_ratio: 1.0 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + video_backend: torchcodec +training: + output_dir: /tmp/libero_10 + experiment_name: null + max_steps: 100000 + global_batch_size: 640 + batch_size: null + gradient_accumulation_steps: 1 + learning_rate: 0.0001 + lr_scheduler_type: cosine + weight_decay: 1.0e-05 + warmup_ratio: 0.05 + warmup_steps: 0 + max_grad_norm: 1.0 + optim: adamw_torch + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + fp16: false + bf16: true + eval_bf16: true + logging_steps: 10 + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + max_concurrent_uploads: 2 + eval_strategy: 'no' + eval_steps: 500 + eval_set_split_ratio: 0.1 + eval_batch_size: 2 + save_best_eval_metric_name: '' + save_best_eval_metric_greater_is_better: true + deepspeed_stage: 2 + gradient_checkpointing: false + transformers_trust_remote_code: true + transformers_local_files_only: false + transformers_cache_dir: null + transformers_access_token: null + use_ddp: false + ddp_bucket_cap_mb: 100 + num_gpus: 8 + dataloader_num_workers: 4 + remove_unused_columns: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + enable_profiling: false + max_retries: 3 + assert_loss_less_than: null + add_rl_callback: false + enable_open_loop_eval: false + open_loop_eval_traj_ids: + - 0 + open_loop_eval_steps_per_traj: 100 + open_loop_eval_plot_indices: null +max_steps: 100000 +save_steps: 1000 diff --git a/libero_10_ckpt/checkpoint-100000/experiment_cfg/config.yaml b/libero_10_ckpt/checkpoint-100000/experiment_cfg/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..34955a257e2dfce0b63b552d7566f8db2ad47239 --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/experiment_cfg/config.yaml @@ -0,0 +1,258 @@ +!!python/object:gr00t.configs.base_config.Config +data: !!python/object:gr00t.configs.data.data_config.DataConfig + allow_padding: false + datasets: + - !!python/object:gr00t.configs.data.data_config.SingleDatasetConfig + dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + dataset_type: physical_embodiment + embodiment_tag: libero_panda + mix_ratio: 1.0 + val_dataset_path: null + download_cache: false + episode_sampling_rate: 0.1 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + mock_dataset_mode: false + modality_configs: + libero_panda: + action: !!python/object:gr00t.data.types.ModalityConfig + action_configs: + - &id001 !!python/object:gr00t.data.types.ActionConfig + format: !!python/object/apply:gr00t.data.types.ActionFormat + - default + rep: !!python/object/apply:gr00t.data.types.ActionRepresentation + - absolute + state_key: null + type: !!python/object/apply:gr00t.data.types.ActionType + - non_eef + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + language: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + state: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + video: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mode: single_turn + multiprocessing_context: fork + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + random_chop: 0.0 + seed: 42 + shard_size: 1024 + shuffle: true + subsample_ratio: 1.0 + video_backend: torchcodec +load_config_path: null +model: !!python/object:gr00t.configs.model.gr00t_n1d6.Gr00tN1d6Config + _attn_implementation_autoset: false + _attn_implementation_internal: null + _commit_hash: null + _name_or_path: '' + add_cross_attention: false + architectures: null + backbone_model_type: eagle + backbone_trainable_params_fp32: true + bad_words_ids: null + begin_suppress_tokens: null + bos_token_id: null + chunk_size_feed_forward: 0 + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + hue: 0.08 + saturation: 0.5 + cross_attention_hidden_size: null + decoder_start_token_id: null + diffusion_model_cfg: + attention_head_dim: 48 + dropout: 0.2 + final_dropout: true + interleave_self_attention: true + norm_type: ada_norm + num_attention_heads: 32 + num_layers: 32 + output_dim: 1024 + positional_embeddings: null + diversity_penalty: 0.0 + do_sample: false + eagle_collator: true + early_stopping: false + encoder_no_repeat_ngram_size: 0 + eos_token_id: null + exponential_decay_length_penalty: null + finetuning_task: null + forced_bos_token_id: null + forced_eos_token_id: null + id2label: + 0: LABEL_0 + 1: LABEL_1 + is_decoder: false + is_encoder_decoder: false + label2id: + LABEL_0: 0 + LABEL_1: 1 + length_penalty: 1.0 + lie_num_langevin_steps: 100 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_rot_start: 0 + load_bf16: false + max_length: 20 + min_length: 0 + model_name: nvidia/Eagle-Block2A-2B-v2 + no_repeat_ngram_size: 0 + num_beam_groups: 1 + num_beams: 1 + num_return_sequences: 1 + output_attentions: false + output_hidden_states: false + output_scores: false + pad_token_id: null + prefix: null + problem_type: null + pruned_heads: {} + random_rotation_angle: null + remove_invalid_values: false + repetition_penalty: 1.0 + reproject_vision: false + return_dict: true + return_dict_in_generate: false + sep_token_id: null + state_dropout_prob: 0.8 + suppress_tokens: null + task_specific_params: null + temperature: 1.0 + tf_legacy_loss: false + tie_encoder_decoder: false + tie_word_embeddings: true + tokenizer_class: null + top_k: 50 + top_p: 1.0 + torch_dtype: null + torchscript: false + transformers_version: null + tune_diffusion_model: true + tune_llm: false + tune_projector: true + tune_visual: false + typical_p: 1.0 + use_bfloat16: false + use_lie_group_rotation: false + use_relative_action: true +training: !!python/object:gr00t.configs.training.training_config.TrainingConfig + add_rl_callback: false + assert_loss_less_than: null + batch_size: null + bf16: true + dataloader_num_workers: 4 + ddp_bucket_cap_mb: 100 + deepspeed_stage: 2 + enable_open_loop_eval: false + enable_profiling: false + eval_batch_size: 2 + eval_bf16: true + eval_set_split_ratio: 0.1 + eval_steps: 500 + eval_strategy: 'no' + experiment_name: null + fp16: false + global_batch_size: 640 + gradient_accumulation_steps: 1 + gradient_checkpointing: false + learning_rate: 0.0001 + logging_steps: 10 + lr_scheduler_type: cosine + max_concurrent_uploads: 2 + max_grad_norm: 1.0 + max_retries: 3 + max_steps: 100000 + num_gpus: 8 + open_loop_eval_plot_indices: null + open_loop_eval_steps_per_traj: 100 + open_loop_eval_traj_ids: + - 0 + optim: adamw_torch + output_dir: /tmp/libero_10 + remove_unused_columns: false + save_best_eval_metric_greater_is_better: true + save_best_eval_metric_name: '' + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + transformers_access_token: null + transformers_cache_dir: null + transformers_local_files_only: false + transformers_trust_remote_code: true + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + use_ddp: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + warmup_ratio: 0.05 + warmup_steps: 0 + weight_decay: 1.0e-05 diff --git a/libero_10_ckpt/checkpoint-100000/experiment_cfg/dataset_statistics.json b/libero_10_ckpt/checkpoint-100000/experiment_cfg/dataset_statistics.json new file mode 100644 index 0000000000000000000000000000000000000000..a86ac2888898322487ce2f2d1c64b97d2c00fbf0 --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/experiment_cfg/dataset_statistics.json @@ -0,0 +1,295 @@ +{ + "libero_panda": { + "state": { + "x": { + "min": [ + -0.4828203022480011 + ], + "max": [ + 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"backbone_embedding_dim": 2048, + "tune_llm": false, + "tune_visual": false, + "select_layer": 16, + "reproject_vision": false, + "use_flash_attention": true, + "load_bf16": true, + "collator_overwrite_image_inputs": false, + "eagle_collator": true, + "backbone_trainable_params_fp32": true, + "apply_sincos_state_encoding": true, + "use_relative_action": true, + "max_state_dim": 128, + "max_action_dim": 128, + "action_horizon": 50, + "hidden_size": 1024, + "input_embedding_dim": 1536, + "add_pos_embed": true, + "attn_dropout": 0.2, + "use_vlln": true, + "max_seq_len": 1024, + "use_alternate_vl_dit": true, + "attend_text_every_n_blocks": 2, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "num_inference_timesteps": 4, + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_timestep_buckets": 1000, + "tune_projector": true, + "tune_diffusion_model": true, + "tune_vlln": true, + "state_dropout_prob": 0.8, + "state_additive_noise_scale": 0.0, + "use_lie_group_rotation": false, + "lie_rot_start": 3, + "lie_rot_end": 6, + "lie_rot_loss_weight": 1.0, + "lie_num_langevin_steps": 100, + "max_num_embodiments": 32 +} \ No newline at end of file diff --git a/libero_10_ckpt/checkpoint-100000/experiment_cfg/final_processor_config.json b/libero_10_ckpt/checkpoint-100000/experiment_cfg/final_processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6e73810a6eb1d340cdb71c5179d73207b5892f59 --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/experiment_cfg/final_processor_config.json @@ -0,0 +1,27 @@ +{ + "modality_configs": "{'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}", + "state_action_processor": "StateActionProcessor(modality_configs={'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}, statistics={'behavior_r1_pro': {'state': {'robot_pos': {'min': [-15.878335952758789, -4.874791622161865, -0.0030556470155715942], 'max': [28.783069610595703, 25.5261173248291, 0.03920263051986694], 'mean': [8.377509353981011, 3.5519228390967257, 0.005352396491811134], 'std': [9.333495189323855, 6.293349382128611, 0.002169489814694542], 'q01': [-15.627003288269043, -3.923245668411255, 0.00464746356010437], 'q99': [28.161231212615963, 25.433036193847656, 0.019137874245643616]}, 'robot_ori_cos': {'min': [0.9964054226875305, 0.9807400703430176, -1.0], 'max': [1.0, 1.0, 1.0], 'mean': [0.9999999103013197, 0.9999999012025106, -0.04432371966161217], 'std': [1.0955394108707479e-05, 1.5141053684175453e-05, 0.7159057916231842], 'q01': [0.9996431469917297, 0.9996242523193359, -0.9999993443489075], 'q99': [0.9999995827674866, 0.9999999403953552, 0.9999995231628418]}, 'robot_ori_sin': {'min': [-0.08471290022134781, -0.0467030331492424, -1.0], 'max': [0.03247667849063873, 0.1953173577785492, 1.0], 'mean': [-0.0014819139726979956, 0.0011138110099060865, -0.1747246084485246], 'std': [0.0010692286968725616, 0.0011352230347134887, 0.6740228681643635], 'q01': [-0.026712577790021896, -0.027393728494644165, -0.9999995827674866], 'q99': [0.023761406727135177, 0.015501312464475616, 0.9999998211860657]}, 'robot_2d_ori': {'min': [-3.1415927410125732], 'max': [3.1415927410125732], 'mean': [-0.34116709549670976], 'std': [1.8744272700649074], 'q01': [-3.139659881591797], 'q99': [3.1393938064575195]}, 'robot_2d_ori_cos': {'min': [-1.0], 'max': [1.0], 'mean': [-0.04432376387887985], 'std': [0.7159057729093367], 'q01': [-0.9999993443489075], 'q99': [0.9999995231628418]}, 'robot_2d_ori_sin': {'min': [-1.0], 'max': [1.0], 'mean': [-0.1747246006771579], 'std': [0.6740228728055352], 'q01': [-0.9999995827674866], 'q99': [0.9999998211860657]}, 'robot_lin_vel': {'min': [-0.32111313939094543, -0.3024478554725647, -0.338993102312088], 'max': [0.2888447046279907, 0.29485562443733215, 0.27627527713775635], 'mean': [0.0008032824331051274, -0.000851410695872969, -0.014532478391340347], 'std': [0.04011179386851422, 0.03532797622061294, 0.010790340283590575], 'q01': [-0.1382001194357872, -0.13443224787712096, -0.05219040531665087], 'q99': [0.13666878938674926, 0.13471105083823204, 0.005502225868403539]}, 'robot_ang_vel': {'min': [-1.5866682529449463, -1.7150360345840454, -0.04071682691574097], 'max': [1.6171817779541016, 1.5271120071411133, 0.024320760741829872], 'mean': [0.004609276467004075, 0.004284130662207927, -5.683130788171653e-05], 'std': [0.18832387264077133, 0.2137817785121968, 0.0008538363202659394], 'q01': [-0.7182695555686951, -0.736412662267685, -0.008470569178462029], 'q99': [0.7163371068239212, 0.7284575819969177, 0.008367371931672096]}, 'arm_left_qpos': {'min': [-3.3108322620391846, -0.17451578378677368, -2.3561959266662598, -1.7452956438064575, -2.3561980724334717, -1.0472002029418945, -1.5708036422729492], 'max': [0.5505963563919067, 3.0216875076293945, 2.356196165084839, 0.16950564086437225, 2.3564183712005615, 1.0471999645233154, 1.57080078125], 'mean': [-0.44064831753394845, 0.044810599293520874, -0.09667299499241816, -0.6179559573275901, 0.37324754543867494, 0.16138558929363642, -0.04477622658352863], 'std': [0.4374789217045343, 0.16097317375158413, 0.4566687142938444, 0.5212846522479491, 0.5669997492408592, 0.5223829217451378, 0.5210728198228], 'q01': [-2.61892716884613, -0.17450003325939178, -2.296152515411377, -1.6367655992507935, -1.6587732124328614, -1.04719078540802, -1.5708000659942627], 'q99': [0.5170108753442764, 0.8608633208274836, 1.9160030841827416, 0.09510957151651382, 2.3561954498291016, 1.0471981763839722, 1.570236325263977]}, 'arm_left_qpos_sin': {'min': [-1.0, -0.17363129556179047, -1.0, -1.0, -1.0, -0.8660266995429993, -1.0], 'max': [0.5231955647468567, 1.0, 1.0, 0.1686950922012329, 1.0, 0.8660265803337097, 1.0], 'mean': [-0.3788414888506152, 0.043208541825812886, -0.07497595954692522, -0.49673881006309, 0.2950975242538935, 0.13821974557859906, -0.023564394694352786], 'std': [0.33500504759538763, 0.1550813208035862, 0.35555302155891993, 0.3650771441344372, 0.394534724671373, 0.4671246774437793, 0.4220550333326586], 'q01': [-0.9998769760131836, -0.17361578345298767, -0.9997559851408004, -0.999994695186615, -0.9991854596138, -0.866021990776062, -1.0], 'q99': [0.49428389251232147, 0.7477953410148599, 0.9990536546707154, 0.0949662446975708, 0.9998820424079895, 0.8660256862640381, 0.9999998211860657]}, 'arm_left_qpos_cos': {'min': [-1.0, -0.9928199648857117, -0.7071079611778259, -0.17361508309841156, -0.7072650790214539, 0.49999770522117615, 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"use_percentiles": "False", + "clip_outliers": "True", + "apply_sincos_state_encoding": "True", + "use_relative_action": "True", + "formalize_language": "True", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "model_type": "eagle", + "max_state_dim": "128", + "max_action_dim": "128", + "max_action_horizon": "50", + "image_crop_size": "None", + "image_target_size": "None", + "random_rotation_angle": "None", + "color_jitter_params": "{'brightness': 0.3, 'contrast': 0.4, 'saturation': 0.5, 'hue': 0.08}", + "processor": "Eagle3_VLProcessor:\n- image_processor: Eagle3_VLImageProcessorFast {\n \"auto_map\": {\n \"AutoImageProcessor\": \"image_processing_eagle3_vl_fast.Eagle3_VLImageProcessorFast\",\n \"AutoProcessor\": \"processing_eagle3_vl.Eagle3_VLProcessor\"\n },\n \"crop_size\": null,\n \"data_format\": \"channels_first\",\n \"default_to_square\": false,\n \"device\": null,\n \"do_center_crop\": null,\n \"do_convert_rgb\": true,\n \"do_normalize\": true,\n \"do_pad\": false,\n \"do_rescale\": true,\n \"do_resize\": false,\n \"image_mean\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"image_processor_type\": \"Eagle3_VLImageProcessorFast\",\n \"image_std\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"input_data_format\": null,\n \"processor_class\": \"Eagle3_VLProcessor\",\n \"resample\": 3,\n \"rescale_factor\": 0.00392156862745098,\n \"return_tensors\": null,\n \"size\": {\n \"height\": 448,\n \"width\": 448\n }\n}\n\n- tokenizer: Qwen2TokenizerFast(name_or_path='/mnt/nfs/tars111060008/GR00T-lie/gr00t/model/gr00t_n1d6/../modules/nvidia/Eagle-Block2A-2B-v2', vocab_size=151643, model_max_length=32768, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>', '', '', '', '', '', '', '', '', '', '', '']}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151657: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151658: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151665: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151666: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151667: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151668: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151669: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151670: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151671: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151672: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151673: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151674: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151675: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151676: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151677: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151678: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151679: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n}\n)\n\n{\n \"auto_map\": {\n \"AutoProcessor\": \"processing_eagle3_vl.Eagle3_VLProcessor\"\n },\n \"image_end_token\": \"\",\n \"image_placeholder\": \"image\",\n \"image_start_token\": \"\",\n \"image_token\": \"\",\n \"pixels_per_token\": 784,\n \"processor_class\": \"Eagle3_VLProcessor\",\n \"video_placeholder\": \"video\",\n \"video_token\": \"\",\n \"vision_feature_select_strategy\": null\n}\n", + "embodiment_id_mapping": "{'robocasa_panda_omron': 13, 'gr1': 20, 'behavior_r1_pro': 24, 'unitree_g1': 8, 'oxe_google': 0, 'oxe_widowx': 1, 'libero_panda': 2, 'oxe_droid': 16, 'new_embodiment': 10}", + "shortest_image_edge": "256", + "crop_fraction": "0.95", + "use_albumentations": "True", + "train_image_transform": "ReplayCompose([\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n FractionalRandomCrop(p=1.0, crop_fraction=0.95),\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n ColorJitter(p=1.0, brightness=(0.7, 1.3), contrast=(0.6, 1.4), saturation=(0.5, 1.5), hue=(-0.08, 0.08)),\n], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True, save_key='replay')", + "eval_image_transform": "Compose([\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n FractionalCenterCrop(p=1.0, crop_fraction=0.95),\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True)", + "_collator": "Gr00tN1d6DataCollator(model_name=nvidia/Eagle-Block2A-2B-v2, model_type=eagle)", + "training": "True" +} \ No 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https://git-lfs.github.com/spec/v1 +oid sha256:0e54108ef49d43695577cb5a9c0a2408b556d604df64d075decd8b39ef34bedd +size 7633 diff --git a/libero_10_ckpt/checkpoint-100000/wandb_config.json b/libero_10_ckpt/checkpoint-100000/wandb_config.json new file mode 100644 index 0000000000000000000000000000000000000000..c02db295493d133d24d349d48070a702759dcb71 --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/wandb_config.json @@ -0,0 +1 @@ +{"project": "finetune-gr00t-n1d6", "run_id": "libero_10"} \ No newline at end of file diff --git a/libero_10_ckpt/checkpoint-100000/zero_to_fp32.py b/libero_10_ckpt/checkpoint-100000/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/libero_10_ckpt/checkpoint-100000/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters) diff --git a/libero_10_ckpt/config.json b/libero_10_ckpt/config.json new file mode 100644 index 0000000000000000000000000000000000000000..06e4fb31e6ac9b7412391f49c0b655cb5488f035 --- /dev/null +++ b/libero_10_ckpt/config.json @@ -0,0 +1,70 @@ +{ + "action_horizon": 50, + "add_pos_embed": true, + "apply_sincos_state_encoding": true, + "architectures": [ + "Gr00tN1d6" + ], + "attn_dropout": 0.2, + "attn_implementation": null, + "backbone_embedding_dim": 2048, + "backbone_model_type": "eagle", + "backbone_trainable_params_fp32": true, + "collator_overwrite_image_inputs": false, + "color_jitter_params": { + "brightness": 0.1, + "contrast": 0.1, + "hue": 0.1, + "saturation": 0.1 + }, + "crop_fraction": 0.95, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "eagle_collator": true, + "formalize_language": true, + "gemma_collator": false, + "hidden_size": 1024, + "image_crop_size": null, + "image_target_size": null, + "input_embedding_dim": 1536, + "load_bf16": true, + "max_action_dim": 128, + "max_num_embodiments": 32, + "max_seq_len": 1024, + "max_state_dim": 128, + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "model_type": "Gr00tN1d6", + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_inference_timesteps": 4, + "num_timestep_buckets": 1000, + "random_rotation_angle": null, + "reproject_vision": false, + "select_layer": 16, + "shortest_image_edge": 256, + "state_dropout_prob": 0.8, + "torch_dtype": "bfloat16", + "transformers_version": "4.51.3", + "tune_diffusion_model": true, + "tune_llm": false, + "tune_projector": true, + "tune_top_llm_layers": 4, + "tune_visual": false, + "tune_vlln": true, + "use_albumentations_transforms": true, + "use_alternate_vl_dit": true, + "use_flash_attention": true, + "use_relative_action": true, + "use_vlln": true +} diff --git a/libero_10_ckpt/experiment_cfg/conf.yaml b/libero_10_ckpt/experiment_cfg/conf.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6f8d5e87ce636ac2559aaf04eba53dead6c5baa1 --- /dev/null +++ b/libero_10_ckpt/experiment_cfg/conf.yaml @@ -0,0 +1,244 @@ +load_config_path: null +model: + model_type: Gr00tN1d6 + model_dtype: bfloat16 + model_name: nvidia/Eagle-Block2A-2B-v2 + backbone_model_type: eagle + model_revision: null + tune_top_llm_layers: 4 + backbone_embedding_dim: 2048 + tune_llm: false + tune_visual: false + select_layer: 16 + reproject_vision: false + use_flash_attention: true + load_bf16: false + collator_overwrite_image_inputs: false + eagle_collator: true + backbone_trainable_params_fp32: true + image_crop_size: null + image_target_size: null + shortest_image_edge: 256 + crop_fraction: 0.95 + random_rotation_angle: null + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + use_albumentations_transforms: true + formalize_language: true + apply_sincos_state_encoding: false + use_relative_action: true + max_state_dim: 29 + max_action_dim: 29 + action_horizon: 16 + hidden_size: 1024 + input_embedding_dim: 1536 + add_pos_embed: true + attn_dropout: 0.2 + use_vlln: true + max_seq_len: 1024 + use_alternate_vl_dit: true + attend_text_every_n_blocks: 2 + diffusion_model_cfg: + positional_embeddings: null + num_layers: 32 + num_attention_heads: 32 + attention_head_dim: 48 + norm_type: ada_norm + dropout: 0.2 + final_dropout: true + output_dim: 1024 + interleave_self_attention: true + num_inference_timesteps: 4 + noise_beta_alpha: 1.5 + noise_beta_beta: 1.0 + noise_s: 0.999 + num_timestep_buckets: 1000 + tune_projector: true + tune_diffusion_model: true + tune_vlln: true + state_dropout_prob: 0.8 + state_additive_noise_scale: 0.0 + use_lie_group_rotation: false + lie_rot_start: 0 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_num_langevin_steps: 100 + max_num_embodiments: 32 +data: + datasets: + - dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + embodiment_tag: libero_panda + mix_ratio: 1.0 + dataset_type: physical_embodiment + val_dataset_path: null + modality_configs: + libero_panda: + video: + delta_indices: + - 0 + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + state: + delta_indices: + - 0 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + action: + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + language: + delta_indices: + - 0 + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + download_cache: false + shard_size: 1024 + episode_sampling_rate: 0.1 + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + mode: single_turn + random_chop: 0.0 + mock_dataset_mode: false + shuffle: true + seed: 42 + multiprocessing_context: fork + allow_padding: false + subsample_ratio: 1.0 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + video_backend: torchcodec +training: + output_dir: /tmp/libero_10 + experiment_name: null + max_steps: 100000 + global_batch_size: 640 + batch_size: null + gradient_accumulation_steps: 1 + learning_rate: 0.0001 + lr_scheduler_type: cosine + weight_decay: 1.0e-05 + warmup_ratio: 0.05 + warmup_steps: 0 + max_grad_norm: 1.0 + optim: adamw_torch + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + fp16: false + bf16: true + eval_bf16: true + logging_steps: 10 + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + max_concurrent_uploads: 2 + eval_strategy: 'no' + eval_steps: 500 + eval_set_split_ratio: 0.1 + eval_batch_size: 2 + save_best_eval_metric_name: '' + save_best_eval_metric_greater_is_better: true + deepspeed_stage: 2 + gradient_checkpointing: false + transformers_trust_remote_code: true + transformers_local_files_only: false + transformers_cache_dir: null + transformers_access_token: null + use_ddp: false + ddp_bucket_cap_mb: 100 + num_gpus: 8 + dataloader_num_workers: 4 + remove_unused_columns: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + enable_profiling: false + max_retries: 3 + assert_loss_less_than: null + add_rl_callback: false + enable_open_loop_eval: false + open_loop_eval_traj_ids: + - 0 + open_loop_eval_steps_per_traj: 100 + open_loop_eval_plot_indices: null +max_steps: 100000 +save_steps: 1000 diff --git a/libero_10_ckpt/experiment_cfg/config.yaml b/libero_10_ckpt/experiment_cfg/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..34955a257e2dfce0b63b552d7566f8db2ad47239 --- /dev/null +++ b/libero_10_ckpt/experiment_cfg/config.yaml @@ -0,0 +1,258 @@ +!!python/object:gr00t.configs.base_config.Config +data: !!python/object:gr00t.configs.data.data_config.DataConfig + allow_padding: false + datasets: + - !!python/object:gr00t.configs.data.data_config.SingleDatasetConfig + dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + dataset_type: physical_embodiment + embodiment_tag: libero_panda + mix_ratio: 1.0 + val_dataset_path: null + download_cache: false + episode_sampling_rate: 0.1 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + mock_dataset_mode: false + modality_configs: + libero_panda: + action: !!python/object:gr00t.data.types.ModalityConfig + action_configs: + - &id001 !!python/object:gr00t.data.types.ActionConfig + format: !!python/object/apply:gr00t.data.types.ActionFormat + - default + rep: !!python/object/apply:gr00t.data.types.ActionRepresentation + - absolute + state_key: null + type: !!python/object/apply:gr00t.data.types.ActionType + - non_eef + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + language: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + state: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + video: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mode: single_turn + multiprocessing_context: fork + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + random_chop: 0.0 + seed: 42 + shard_size: 1024 + shuffle: true + subsample_ratio: 1.0 + video_backend: torchcodec +load_config_path: null +model: !!python/object:gr00t.configs.model.gr00t_n1d6.Gr00tN1d6Config + _attn_implementation_autoset: false + _attn_implementation_internal: null + _commit_hash: null + _name_or_path: '' + add_cross_attention: false + architectures: null + backbone_model_type: eagle + backbone_trainable_params_fp32: true + bad_words_ids: null + begin_suppress_tokens: null + bos_token_id: null + chunk_size_feed_forward: 0 + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + hue: 0.08 + saturation: 0.5 + cross_attention_hidden_size: null + decoder_start_token_id: null + diffusion_model_cfg: + attention_head_dim: 48 + dropout: 0.2 + final_dropout: true + interleave_self_attention: true + norm_type: ada_norm + num_attention_heads: 32 + num_layers: 32 + output_dim: 1024 + positional_embeddings: null + diversity_penalty: 0.0 + do_sample: false + eagle_collator: true + early_stopping: false + encoder_no_repeat_ngram_size: 0 + eos_token_id: null + exponential_decay_length_penalty: null + finetuning_task: null + forced_bos_token_id: null + forced_eos_token_id: null + id2label: + 0: LABEL_0 + 1: LABEL_1 + is_decoder: false + is_encoder_decoder: false + label2id: + LABEL_0: 0 + LABEL_1: 1 + length_penalty: 1.0 + lie_num_langevin_steps: 100 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_rot_start: 0 + load_bf16: false + max_length: 20 + min_length: 0 + model_name: nvidia/Eagle-Block2A-2B-v2 + no_repeat_ngram_size: 0 + num_beam_groups: 1 + num_beams: 1 + num_return_sequences: 1 + output_attentions: false + output_hidden_states: false + output_scores: false + pad_token_id: null + prefix: null + problem_type: null + pruned_heads: {} + random_rotation_angle: null + remove_invalid_values: false + repetition_penalty: 1.0 + reproject_vision: false + return_dict: true + return_dict_in_generate: false + sep_token_id: null + state_dropout_prob: 0.8 + suppress_tokens: null + task_specific_params: null + temperature: 1.0 + tf_legacy_loss: false + tie_encoder_decoder: false + tie_word_embeddings: true + tokenizer_class: null + top_k: 50 + top_p: 1.0 + torch_dtype: null + torchscript: false + transformers_version: null + tune_diffusion_model: true + tune_llm: false + tune_projector: true + tune_visual: false + typical_p: 1.0 + use_bfloat16: false + use_lie_group_rotation: false + use_relative_action: true +training: !!python/object:gr00t.configs.training.training_config.TrainingConfig + add_rl_callback: false + assert_loss_less_than: null + batch_size: null + bf16: true + dataloader_num_workers: 4 + ddp_bucket_cap_mb: 100 + deepspeed_stage: 2 + enable_open_loop_eval: false + enable_profiling: false + eval_batch_size: 2 + eval_bf16: true + eval_set_split_ratio: 0.1 + eval_steps: 500 + eval_strategy: 'no' + experiment_name: null + fp16: false + global_batch_size: 640 + gradient_accumulation_steps: 1 + gradient_checkpointing: false + learning_rate: 0.0001 + logging_steps: 10 + lr_scheduler_type: cosine + max_concurrent_uploads: 2 + max_grad_norm: 1.0 + max_retries: 3 + max_steps: 100000 + num_gpus: 8 + open_loop_eval_plot_indices: null + open_loop_eval_steps_per_traj: 100 + open_loop_eval_traj_ids: + - 0 + optim: adamw_torch + output_dir: /tmp/libero_10 + remove_unused_columns: false + save_best_eval_metric_greater_is_better: true + save_best_eval_metric_name: '' + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + transformers_access_token: null + transformers_cache_dir: null + transformers_local_files_only: false + transformers_trust_remote_code: true + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + use_ddp: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + warmup_ratio: 0.05 + warmup_steps: 0 + weight_decay: 1.0e-05 diff --git a/libero_10_ckpt/experiment_cfg/dataset_statistics.json b/libero_10_ckpt/experiment_cfg/dataset_statistics.json new file mode 100644 index 0000000000000000000000000000000000000000..a86ac2888898322487ce2f2d1c64b97d2c00fbf0 --- /dev/null +++ b/libero_10_ckpt/experiment_cfg/dataset_statistics.json @@ -0,0 +1,295 @@ +{ + "libero_panda": { + "state": { + "x": { + "min": [ + -0.4828203022480011 + ], + "max": [ + 0.21031762659549713 + ], + "mean": [ + -0.04190658777952194 + ], + "std": [ + 0.10743364691734314 + ], + "q01": [ + -0.3899900782108307 + ], + "q99": [ + 0.1530261474847791 + ] + }, + "y": { + "min": [ + -0.3255046010017395 + ], + "max": [ + 0.39128610491752625 + ], + "mean": [ + 0.03539430722594261 + ], + "std": [ + 0.14424669742584229 + ], + "q01": [ + -0.2838300323486328 + ], + "q99": [ + 0.32915401458740223 + ] + }, + "z": { + "min": [ + 0.445506751537323 + ], + "max": [ + 1.3332009315490723 + ], + "mean": [ + 0.8257141709327698 + ], + "std": [ + 0.2572328448295593 + ], + "q01": [ + 0.44795057058334353 + ], + "q99": [ + 1.2546923208236693 + ] + }, + "roll": { + "min": [ + 1.1321442127227783 + ], + "max": [ + 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0.040048558115959164, + -0.0017598449345678235 + ] + } + }, + "action": { + "x": { + "min": [ + -0.9375 + ], + "max": [ + 0.9375 + ], + "mean": [ + 0.01820324920117855 + ], + "std": [ + 0.2825464606285095 + ], + "q01": [ + -0.6348214149475098 + ], + "q99": [ + 0.7714285850524902 + ] + }, + "y": { + "min": [ + -0.9375 + ], + "max": [ + 0.9375 + ], + "mean": [ + 0.05858374014496803 + ], + "std": [ + 0.35904666781425476 + ], + "q01": [ + -0.7741071581840515 + ], + "q99": [ + 0.8464285731315613 + ] + }, + "z": { + "min": [ + -0.9375 + ], + "max": [ + 0.9375 + ], + "mean": [ + -0.05592384561896324 + ], + "std": [ + 0.3673802614212036 + ], + "q01": [ + -0.7633928656578064 + ], + "q99": [ + 0.9375 + ] + }, + "roll": { + "min": [ + -0.23642857372760773 + ], + "max": [ + 0.30000001192092896 + ], + "mean": [ + 0.004626928828656673 + ], + "std": [ + 0.03770702704787254 + ], + "q01": [ + -0.09749999642372131 + ], + "q99": [ + 0.13928571343421936 + ] + }, + "pitch": { + "min": [ + -0.3053571283817291 + ], + "max": [ + 0.29357144236564636 + ], + "mean": [ + 0.00289608770981431 + ], + "std": [ + 0.05429719388484955 + ], + "q01": [ + -0.14819999992847435 + ], + "q99": [ + 0.15964286029338837 + ] + }, + "yaw": { + "min": [ + -0.3675000071525574 + ], + "max": [ + 0.375 + ], + "mean": [ + -0.007673131301999092 + ], + "std": [ + 0.08725254982709885 + ], + "q01": [ + -0.2742857038974762 + ], + "q99": [ + 0.3246428668498993 + ] + }, + "gripper": { + "min": [ + 0.0 + ], + "max": [ + 1.0 + ], + "mean": [ + 0.5457824468612671 + ], + "std": [ + 0.49815231561660767 + ], + "q01": [ + 0.0 + ], + "q99": [ + 1.0 + ] + } + }, + "relative_action": {} + } +} \ No newline at end of file diff --git a/libero_10_ckpt/experiment_cfg/final_model_config.json b/libero_10_ckpt/experiment_cfg/final_model_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6fff17a7e7d6cffd11cb6b2124041a76aa0c9a39 --- /dev/null +++ b/libero_10_ckpt/experiment_cfg/final_model_config.json @@ -0,0 +1,58 @@ +{ + "model_type": "Gr00tN1d6", + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "backbone_model_type": "eagle", + "model_revision": null, + "tune_top_llm_layers": 4, + "backbone_embedding_dim": 2048, + "tune_llm": false, + "tune_visual": false, + "select_layer": 16, + "reproject_vision": false, + "use_flash_attention": true, + "load_bf16": true, + "collator_overwrite_image_inputs": false, + "eagle_collator": true, + "backbone_trainable_params_fp32": true, + "apply_sincos_state_encoding": true, + "use_relative_action": true, + "max_state_dim": 128, + "max_action_dim": 128, + "action_horizon": 50, + "hidden_size": 1024, + "input_embedding_dim": 1536, + "add_pos_embed": true, + "attn_dropout": 0.2, + "use_vlln": true, + "max_seq_len": 1024, + "use_alternate_vl_dit": true, + "attend_text_every_n_blocks": 2, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "num_inference_timesteps": 4, + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_timestep_buckets": 1000, + "tune_projector": true, + "tune_diffusion_model": true, + "tune_vlln": true, + "state_dropout_prob": 0.8, + "state_additive_noise_scale": 0.0, + "use_lie_group_rotation": false, + "lie_rot_start": 3, + "lie_rot_end": 6, + "lie_rot_loss_weight": 1.0, + "lie_num_langevin_steps": 100, + "max_num_embodiments": 32 +} \ No newline at end of file diff --git a/libero_10_ckpt/experiment_cfg/final_processor_config.json b/libero_10_ckpt/experiment_cfg/final_processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6e73810a6eb1d340cdb71c5179d73207b5892f59 --- /dev/null +++ b/libero_10_ckpt/experiment_cfg/final_processor_config.json @@ -0,0 +1,27 @@ +{ + "modality_configs": "{'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}", + "state_action_processor": "StateActionProcessor(modality_configs={'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}, statistics={'behavior_r1_pro': {'state': {'robot_pos': {'min': [-15.878335952758789, -4.874791622161865, -0.0030556470155715942], 'max': [28.783069610595703, 25.5261173248291, 0.03920263051986694], 'mean': [8.377509353981011, 3.5519228390967257, 0.005352396491811134], 'std': [9.333495189323855, 6.293349382128611, 0.002169489814694542], 'q01': [-15.627003288269043, -3.923245668411255, 0.00464746356010437], 'q99': [28.161231212615963, 25.433036193847656, 0.019137874245643616]}, 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b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/embodiment_id.json @@ -0,0 +1,11 @@ +{ + "robocasa_panda_omron": 13, + "gr1": 20, + "behavior_r1_pro": 24, + "unitree_g1": 8, + "oxe_google": 0, + "oxe_widowx": 1, + "libero_panda": 2, + "oxe_droid": 16, + "new_embodiment": 10 +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/conf.yaml b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/conf.yaml new file mode 100644 index 0000000000000000000000000000000000000000..90cf7847073fe8feb05e7dd1dd0b80e425db2c5e --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/conf.yaml @@ -0,0 +1,244 @@ +load_config_path: null +model: + model_type: Gr00tN1d6 + model_dtype: bfloat16 + model_name: nvidia/Eagle-Block2A-2B-v2 + backbone_model_type: eagle + model_revision: null + tune_top_llm_layers: 4 + backbone_embedding_dim: 2048 + tune_llm: false + tune_visual: false + select_layer: 16 + reproject_vision: false + use_flash_attention: true + load_bf16: false + collator_overwrite_image_inputs: false + eagle_collator: true + backbone_trainable_params_fp32: true + image_crop_size: null + image_target_size: null + shortest_image_edge: 256 + crop_fraction: 0.95 + random_rotation_angle: null + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + use_albumentations_transforms: true + formalize_language: true + apply_sincos_state_encoding: false + use_relative_action: true + max_state_dim: 29 + max_action_dim: 29 + action_horizon: 16 + hidden_size: 1024 + input_embedding_dim: 1536 + add_pos_embed: true + attn_dropout: 0.2 + use_vlln: true + max_seq_len: 1024 + use_alternate_vl_dit: true + attend_text_every_n_blocks: 2 + diffusion_model_cfg: + positional_embeddings: null + num_layers: 32 + num_attention_heads: 32 + attention_head_dim: 48 + norm_type: ada_norm + dropout: 0.2 + final_dropout: true + output_dim: 1024 + interleave_self_attention: true + num_inference_timesteps: 4 + noise_beta_alpha: 1.5 + noise_beta_beta: 1.0 + noise_s: 0.999 + num_timestep_buckets: 1000 + tune_projector: true + tune_diffusion_model: true + tune_vlln: true + state_dropout_prob: 0.8 + state_additive_noise_scale: 0.0 + use_lie_group_rotation: true + lie_rot_start: 0 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_num_langevin_steps: 100 + max_num_embodiments: 32 +data: + datasets: + - dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + embodiment_tag: libero_panda + mix_ratio: 1.0 + dataset_type: physical_embodiment + val_dataset_path: null + modality_configs: + libero_panda: + video: + delta_indices: + - 0 + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + state: + delta_indices: + - 0 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + action: + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + language: + delta_indices: + - 0 + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + download_cache: false + shard_size: 1024 + episode_sampling_rate: 0.1 + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + mode: single_turn + random_chop: 0.0 + mock_dataset_mode: false + shuffle: true + seed: 42 + multiprocessing_context: fork + allow_padding: false + subsample_ratio: 1.0 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + video_backend: torchcodec +training: + output_dir: libero_10_lie_gripper_noise_ckpt + experiment_name: null + max_steps: 100000 + global_batch_size: 640 + batch_size: null + gradient_accumulation_steps: 1 + learning_rate: 0.0001 + lr_scheduler_type: cosine + weight_decay: 1.0e-05 + warmup_ratio: 0.05 + warmup_steps: 0 + max_grad_norm: 1.0 + optim: adamw_torch + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + fp16: false + bf16: true + eval_bf16: true + logging_steps: 10 + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + max_concurrent_uploads: 2 + eval_strategy: 'no' + eval_steps: 500 + eval_set_split_ratio: 0.1 + eval_batch_size: 2 + save_best_eval_metric_name: '' + save_best_eval_metric_greater_is_better: true + deepspeed_stage: 2 + gradient_checkpointing: false + transformers_trust_remote_code: true + transformers_local_files_only: false + transformers_cache_dir: null + transformers_access_token: null + use_ddp: false + ddp_bucket_cap_mb: 100 + num_gpus: 8 + dataloader_num_workers: 4 + remove_unused_columns: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + enable_profiling: false + max_retries: 3 + assert_loss_less_than: null + add_rl_callback: false + enable_open_loop_eval: false + open_loop_eval_traj_ids: + - 0 + open_loop_eval_steps_per_traj: 100 + open_loop_eval_plot_indices: null +max_steps: 100000 +save_steps: 1000 diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/config.yaml b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..528af8f882b268a85c3ab4a6f4f3433ce8d30fa0 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/config.yaml @@ -0,0 +1,258 @@ +!!python/object:gr00t.configs.base_config.Config +data: !!python/object:gr00t.configs.data.data_config.DataConfig + allow_padding: false + datasets: + - !!python/object:gr00t.configs.data.data_config.SingleDatasetConfig + dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + dataset_type: physical_embodiment + embodiment_tag: libero_panda + mix_ratio: 1.0 + val_dataset_path: null + download_cache: false + episode_sampling_rate: 0.1 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + mock_dataset_mode: false + modality_configs: + libero_panda: + action: !!python/object:gr00t.data.types.ModalityConfig + action_configs: + - &id001 !!python/object:gr00t.data.types.ActionConfig + format: !!python/object/apply:gr00t.data.types.ActionFormat + - default + rep: !!python/object/apply:gr00t.data.types.ActionRepresentation + - absolute + state_key: null + type: !!python/object/apply:gr00t.data.types.ActionType + - non_eef + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + language: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + state: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + video: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mode: single_turn + multiprocessing_context: fork + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + random_chop: 0.0 + seed: 42 + shard_size: 1024 + shuffle: true + subsample_ratio: 1.0 + video_backend: torchcodec +load_config_path: null +model: !!python/object:gr00t.configs.model.gr00t_n1d6.Gr00tN1d6Config + _attn_implementation_autoset: false + _attn_implementation_internal: null + _commit_hash: null + _name_or_path: '' + add_cross_attention: false + architectures: null + backbone_model_type: eagle + backbone_trainable_params_fp32: true + bad_words_ids: null + begin_suppress_tokens: null + bos_token_id: null + chunk_size_feed_forward: 0 + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + hue: 0.08 + saturation: 0.5 + cross_attention_hidden_size: null + decoder_start_token_id: null + diffusion_model_cfg: + attention_head_dim: 48 + dropout: 0.2 + final_dropout: true + interleave_self_attention: true + norm_type: ada_norm + num_attention_heads: 32 + num_layers: 32 + output_dim: 1024 + positional_embeddings: null + diversity_penalty: 0.0 + do_sample: false + eagle_collator: true + early_stopping: false + encoder_no_repeat_ngram_size: 0 + eos_token_id: null + exponential_decay_length_penalty: null + finetuning_task: null + forced_bos_token_id: null + forced_eos_token_id: null + id2label: + 0: LABEL_0 + 1: LABEL_1 + is_decoder: false + is_encoder_decoder: false + label2id: + LABEL_0: 0 + LABEL_1: 1 + length_penalty: 1.0 + lie_num_langevin_steps: 100 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_rot_start: 0 + load_bf16: false + max_length: 20 + min_length: 0 + model_name: nvidia/Eagle-Block2A-2B-v2 + no_repeat_ngram_size: 0 + num_beam_groups: 1 + num_beams: 1 + num_return_sequences: 1 + output_attentions: false + output_hidden_states: false + output_scores: false + pad_token_id: null + prefix: null + problem_type: null + pruned_heads: {} + random_rotation_angle: null + remove_invalid_values: false + repetition_penalty: 1.0 + reproject_vision: false + return_dict: true + return_dict_in_generate: false + sep_token_id: null + state_dropout_prob: 0.8 + suppress_tokens: null + task_specific_params: null + temperature: 1.0 + tf_legacy_loss: false + tie_encoder_decoder: false + tie_word_embeddings: true + tokenizer_class: null + top_k: 50 + top_p: 1.0 + torch_dtype: null + torchscript: false + transformers_version: null + tune_diffusion_model: true + tune_llm: false + tune_projector: true + tune_visual: false + typical_p: 1.0 + use_bfloat16: false + use_lie_group_rotation: true + use_relative_action: true +training: !!python/object:gr00t.configs.training.training_config.TrainingConfig + add_rl_callback: false + assert_loss_less_than: null + batch_size: null + bf16: true + dataloader_num_workers: 4 + ddp_bucket_cap_mb: 100 + deepspeed_stage: 2 + enable_open_loop_eval: false + enable_profiling: false + eval_batch_size: 2 + eval_bf16: true + eval_set_split_ratio: 0.1 + eval_steps: 500 + eval_strategy: 'no' + experiment_name: null + fp16: false + global_batch_size: 640 + gradient_accumulation_steps: 1 + gradient_checkpointing: false + learning_rate: 0.0001 + logging_steps: 10 + lr_scheduler_type: cosine + max_concurrent_uploads: 2 + max_grad_norm: 1.0 + max_retries: 3 + max_steps: 100000 + num_gpus: 8 + open_loop_eval_plot_indices: null + open_loop_eval_steps_per_traj: 100 + open_loop_eval_traj_ids: + - 0 + optim: adamw_torch + output_dir: libero_10_lie_gripper_noise_ckpt + remove_unused_columns: false + save_best_eval_metric_greater_is_better: true + save_best_eval_metric_name: '' + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + transformers_access_token: null + transformers_cache_dir: null + transformers_local_files_only: false + transformers_trust_remote_code: true + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + use_ddp: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + warmup_ratio: 0.05 + warmup_steps: 0 + weight_decay: 1.0e-05 diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/dataset_statistics.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/dataset_statistics.json new file mode 100644 index 0000000000000000000000000000000000000000..a86ac2888898322487ce2f2d1c64b97d2c00fbf0 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/dataset_statistics.json @@ -0,0 +1,295 @@ +{ + "libero_panda": { + "state": { + "x": { + "min": [ + -0.4828203022480011 + ], + "max": [ + 0.21031762659549713 + ], + "mean": [ + -0.04190658777952194 + ], + "std": [ + 0.10743364691734314 + ], + "q01": [ + 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-0.2742857038974762 + ], + "q99": [ + 0.3246428668498993 + ] + }, + "gripper": { + "min": [ + 0.0 + ], + "max": [ + 1.0 + ], + "mean": [ + 0.5457824468612671 + ], + "std": [ + 0.49815231561660767 + ], + "q01": [ + 0.0 + ], + "q99": [ + 1.0 + ] + } + }, + "relative_action": {} + } +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/final_model_config.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/final_model_config.json new file mode 100644 index 0000000000000000000000000000000000000000..b9b19727e33cfe1437532403e4efd91b4125a276 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/final_model_config.json @@ -0,0 +1,58 @@ +{ + "model_type": "Gr00tN1d6", + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "backbone_model_type": "eagle", + "model_revision": null, + "tune_top_llm_layers": 4, + "backbone_embedding_dim": 2048, + "tune_llm": false, + "tune_visual": false, + "select_layer": 16, + "reproject_vision": false, + "use_flash_attention": true, + "load_bf16": true, + "collator_overwrite_image_inputs": false, + "eagle_collator": true, + "backbone_trainable_params_fp32": true, + "apply_sincos_state_encoding": true, + "use_relative_action": true, + "max_state_dim": 128, + "max_action_dim": 128, + "action_horizon": 50, + "hidden_size": 1024, + "input_embedding_dim": 1536, + "add_pos_embed": true, + "attn_dropout": 0.2, + "use_vlln": true, + "max_seq_len": 1024, + "use_alternate_vl_dit": true, + "attend_text_every_n_blocks": 2, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "num_inference_timesteps": 4, + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_timestep_buckets": 1000, + "tune_projector": true, + "tune_diffusion_model": true, + "tune_vlln": true, + "state_dropout_prob": 0.8, + "state_additive_noise_scale": 0.0, + "use_lie_group_rotation": true, + "lie_rot_start": 0, + "lie_rot_end": 6, + "lie_rot_loss_weight": 1.0, + "lie_num_langevin_steps": 100, + "max_num_embodiments": 32 +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/final_processor_config.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/final_processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6e73810a6eb1d340cdb71c5179d73207b5892f59 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/experiment_cfg/final_processor_config.json @@ -0,0 +1,27 @@ +{ + "modality_configs": "{'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}", + "state_action_processor": "StateActionProcessor(modality_configs={'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), 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"use_percentiles": "False", + "clip_outliers": "True", + "apply_sincos_state_encoding": "True", + "use_relative_action": "True", + "formalize_language": "True", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "model_type": "eagle", + "max_state_dim": "128", + "max_action_dim": "128", + "max_action_horizon": "50", + "image_crop_size": "None", + "image_target_size": "None", + "random_rotation_angle": "None", + "color_jitter_params": "{'brightness': 0.3, 'contrast': 0.4, 'saturation': 0.5, 'hue': 0.08}", + "processor": "Eagle3_VLProcessor:\n- image_processor: Eagle3_VLImageProcessorFast {\n \"auto_map\": {\n \"AutoImageProcessor\": \"image_processing_eagle3_vl_fast.Eagle3_VLImageProcessorFast\",\n \"AutoProcessor\": \"processing_eagle3_vl.Eagle3_VLProcessor\"\n },\n \"crop_size\": null,\n \"data_format\": \"channels_first\",\n \"default_to_square\": false,\n \"device\": null,\n \"do_center_crop\": null,\n \"do_convert_rgb\": true,\n \"do_normalize\": true,\n \"do_pad\": false,\n 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sha256:1ed87ac9fdec53121945d3ea7a5cbd1f63db2243e55bbe29c46e275c7e5d8eff +size 7633 diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/wandb_config.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/wandb_config.json new file mode 100644 index 0000000000000000000000000000000000000000..df7b475940b7d322567596d4d9c01dd2f37d3e38 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/wandb_config.json @@ -0,0 +1 @@ +{"project": "finetune-gr00t-n1d6", "run_id": "libero_10_lie_gripper_noise_ckpt"} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/zero_to_fp32.py b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-100000/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters) diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/config.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/config.json new file mode 100644 index 0000000000000000000000000000000000000000..6fbc3429a62d8737dabbdc6c56c94080fd18128b --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/config.json @@ -0,0 +1,75 @@ +{ + "action_horizon": 50, + "add_pos_embed": true, + "apply_sincos_state_encoding": true, + "architectures": [ + "Gr00tN1d6" + ], + "attn_dropout": 0.2, + "attn_implementation": null, + "backbone_embedding_dim": 2048, + "backbone_model_type": "eagle", + "backbone_trainable_params_fp32": true, + "collator_overwrite_image_inputs": false, + "color_jitter_params": { + "brightness": 0.1, + "contrast": 0.1, + "hue": 0.1, + "saturation": 0.1 + }, + "crop_fraction": 0.95, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "eagle_collator": true, + "formalize_language": true, + "gemma_collator": false, + "hidden_size": 1024, + "image_crop_size": null, + "image_target_size": null, + "input_embedding_dim": 1536, + "lie_num_langevin_steps": 100, + "lie_rot_end": 6, + "lie_rot_loss_weight": 1.0, + "lie_rot_start": 0, + "load_bf16": true, + "max_action_dim": 128, + "max_num_embodiments": 32, + "max_seq_len": 1024, + "max_state_dim": 128, + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "model_type": "Gr00tN1d6", + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_inference_timesteps": 4, + "num_timestep_buckets": 1000, + "random_rotation_angle": null, + "reproject_vision": false, + "select_layer": 16, + "shortest_image_edge": 256, + "state_dropout_prob": 0.8, + "torch_dtype": "bfloat16", + "transformers_version": "4.51.3", + "tune_diffusion_model": true, + "tune_llm": false, + "tune_projector": true, + "tune_top_llm_layers": 4, + "tune_visual": false, + "tune_vlln": true, + "use_albumentations_transforms": true, + "use_alternate_vl_dit": true, + "use_flash_attention": true, + "use_lie_group_rotation": true, + "use_relative_action": true, + "use_vlln": true +} diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/embodiment_id.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/embodiment_id.json new file mode 100644 index 0000000000000000000000000000000000000000..9db6cf1b1236298d5de030192d4d9c3f1d143e4c --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/embodiment_id.json @@ -0,0 +1,11 @@ +{ + "robocasa_panda_omron": 13, + "gr1": 20, + "behavior_r1_pro": 24, + "unitree_g1": 8, + "oxe_google": 0, + "oxe_widowx": 1, + "libero_panda": 2, + "oxe_droid": 16, + "new_embodiment": 10 +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/conf.yaml b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/conf.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a943c8711196aa9cd431e57c663bfd864e890663 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/conf.yaml @@ -0,0 +1,244 @@ +load_config_path: null +model: + model_type: Gr00tN1d6 + model_dtype: bfloat16 + model_name: nvidia/Eagle-Block2A-2B-v2 + backbone_model_type: eagle + model_revision: null + tune_top_llm_layers: 4 + backbone_embedding_dim: 2048 + tune_llm: false + tune_visual: false + select_layer: 16 + reproject_vision: false + use_flash_attention: true + load_bf16: false + collator_overwrite_image_inputs: false + eagle_collator: true + backbone_trainable_params_fp32: true + image_crop_size: null + image_target_size: null + shortest_image_edge: 256 + crop_fraction: 0.95 + random_rotation_angle: null + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + use_albumentations_transforms: true + formalize_language: true + apply_sincos_state_encoding: false + use_relative_action: true + max_state_dim: 29 + max_action_dim: 29 + action_horizon: 16 + hidden_size: 1024 + input_embedding_dim: 1536 + add_pos_embed: true + attn_dropout: 0.2 + use_vlln: true + max_seq_len: 1024 + use_alternate_vl_dit: true + attend_text_every_n_blocks: 2 + diffusion_model_cfg: + positional_embeddings: null + num_layers: 32 + num_attention_heads: 32 + attention_head_dim: 48 + norm_type: ada_norm + dropout: 0.2 + final_dropout: true + output_dim: 1024 + interleave_self_attention: true + num_inference_timesteps: 4 + noise_beta_alpha: 1.5 + noise_beta_beta: 1.0 + noise_s: 0.999 + num_timestep_buckets: 1000 + tune_projector: true + tune_diffusion_model: true + tune_vlln: true + state_dropout_prob: 0.8 + state_additive_noise_scale: 0.0 + use_lie_group_rotation: true + lie_rot_start: 0 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_num_langevin_steps: 100 + max_num_embodiments: 32 +data: + datasets: + - dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + embodiment_tag: libero_panda + mix_ratio: 1.0 + dataset_type: physical_embodiment + val_dataset_path: null + modality_configs: + libero_panda: + video: + delta_indices: + - 0 + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + state: + delta_indices: + - 0 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + action: + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + language: + delta_indices: + - 0 + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + download_cache: false + shard_size: 1024 + episode_sampling_rate: 0.1 + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + mode: single_turn + random_chop: 0.0 + mock_dataset_mode: false + shuffle: true + seed: 42 + multiprocessing_context: fork + allow_padding: false + subsample_ratio: 1.0 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + video_backend: torchcodec +training: + output_dir: libero_10_lie_gripper_noise_ckpt + experiment_name: null + max_steps: 50000 + global_batch_size: 640 + batch_size: null + gradient_accumulation_steps: 1 + learning_rate: 0.0001 + lr_scheduler_type: cosine + weight_decay: 1.0e-05 + warmup_ratio: 0.05 + warmup_steps: 0 + max_grad_norm: 1.0 + optim: adamw_torch + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + fp16: false + bf16: true + eval_bf16: true + logging_steps: 10 + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + max_concurrent_uploads: 2 + eval_strategy: 'no' + eval_steps: 500 + eval_set_split_ratio: 0.1 + eval_batch_size: 2 + save_best_eval_metric_name: '' + save_best_eval_metric_greater_is_better: true + deepspeed_stage: 2 + gradient_checkpointing: false + transformers_trust_remote_code: true + transformers_local_files_only: false + transformers_cache_dir: null + transformers_access_token: null + use_ddp: false + ddp_bucket_cap_mb: 100 + num_gpus: 8 + dataloader_num_workers: 4 + remove_unused_columns: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + enable_profiling: false + max_retries: 3 + assert_loss_less_than: null + add_rl_callback: false + enable_open_loop_eval: false + open_loop_eval_traj_ids: + - 0 + open_loop_eval_steps_per_traj: 100 + open_loop_eval_plot_indices: null +max_steps: 50000 +save_steps: 1000 diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/config.yaml b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c9f37fb4dd7c4e097e0e342b773cb738cb29588f --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/config.yaml @@ -0,0 +1,258 @@ +!!python/object:gr00t.configs.base_config.Config +data: !!python/object:gr00t.configs.data.data_config.DataConfig + allow_padding: false + datasets: + - !!python/object:gr00t.configs.data.data_config.SingleDatasetConfig + dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + dataset_type: physical_embodiment + embodiment_tag: libero_panda + mix_ratio: 1.0 + val_dataset_path: null + download_cache: false + episode_sampling_rate: 0.1 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + mock_dataset_mode: false + modality_configs: + libero_panda: + action: !!python/object:gr00t.data.types.ModalityConfig + action_configs: + - &id001 !!python/object:gr00t.data.types.ActionConfig + format: !!python/object/apply:gr00t.data.types.ActionFormat + - default + rep: !!python/object/apply:gr00t.data.types.ActionRepresentation + - absolute + state_key: null + type: !!python/object/apply:gr00t.data.types.ActionType + - non_eef + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + language: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + state: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + video: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mode: single_turn + multiprocessing_context: fork + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + random_chop: 0.0 + seed: 42 + shard_size: 1024 + shuffle: true + subsample_ratio: 1.0 + video_backend: torchcodec +load_config_path: null +model: !!python/object:gr00t.configs.model.gr00t_n1d6.Gr00tN1d6Config + _attn_implementation_autoset: false + _attn_implementation_internal: null + _commit_hash: null + _name_or_path: '' + add_cross_attention: false + architectures: null + backbone_model_type: eagle + backbone_trainable_params_fp32: true + bad_words_ids: null + begin_suppress_tokens: null + bos_token_id: null + chunk_size_feed_forward: 0 + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + hue: 0.08 + saturation: 0.5 + cross_attention_hidden_size: null + decoder_start_token_id: null + diffusion_model_cfg: + attention_head_dim: 48 + dropout: 0.2 + final_dropout: true + interleave_self_attention: true + norm_type: ada_norm + num_attention_heads: 32 + num_layers: 32 + output_dim: 1024 + positional_embeddings: null + diversity_penalty: 0.0 + do_sample: false + eagle_collator: true + early_stopping: false + encoder_no_repeat_ngram_size: 0 + eos_token_id: null + exponential_decay_length_penalty: null + finetuning_task: null + forced_bos_token_id: null + forced_eos_token_id: null + id2label: + 0: LABEL_0 + 1: LABEL_1 + is_decoder: false + is_encoder_decoder: false + label2id: + LABEL_0: 0 + LABEL_1: 1 + length_penalty: 1.0 + lie_num_langevin_steps: 100 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_rot_start: 0 + load_bf16: false + max_length: 20 + min_length: 0 + model_name: nvidia/Eagle-Block2A-2B-v2 + no_repeat_ngram_size: 0 + num_beam_groups: 1 + num_beams: 1 + num_return_sequences: 1 + output_attentions: false + output_hidden_states: false + output_scores: false + pad_token_id: null + prefix: null + problem_type: null + pruned_heads: {} + random_rotation_angle: null + remove_invalid_values: false + repetition_penalty: 1.0 + reproject_vision: false + return_dict: true + return_dict_in_generate: false + sep_token_id: null + state_dropout_prob: 0.8 + suppress_tokens: null + task_specific_params: null + temperature: 1.0 + tf_legacy_loss: false + tie_encoder_decoder: false + tie_word_embeddings: true + tokenizer_class: null + top_k: 50 + top_p: 1.0 + torch_dtype: null + torchscript: false + transformers_version: null + tune_diffusion_model: true + tune_llm: false + tune_projector: true + tune_visual: false + typical_p: 1.0 + use_bfloat16: false + use_lie_group_rotation: true + use_relative_action: true +training: !!python/object:gr00t.configs.training.training_config.TrainingConfig + add_rl_callback: false + assert_loss_less_than: null + batch_size: null + bf16: true + dataloader_num_workers: 4 + ddp_bucket_cap_mb: 100 + deepspeed_stage: 2 + enable_open_loop_eval: false + enable_profiling: false + eval_batch_size: 2 + eval_bf16: true + eval_set_split_ratio: 0.1 + eval_steps: 500 + eval_strategy: 'no' + experiment_name: null + fp16: false + global_batch_size: 640 + gradient_accumulation_steps: 1 + gradient_checkpointing: false + learning_rate: 0.0001 + logging_steps: 10 + lr_scheduler_type: cosine + max_concurrent_uploads: 2 + max_grad_norm: 1.0 + max_retries: 3 + max_steps: 50000 + num_gpus: 8 + open_loop_eval_plot_indices: null + open_loop_eval_steps_per_traj: 100 + open_loop_eval_traj_ids: + - 0 + optim: adamw_torch + output_dir: libero_10_lie_gripper_noise_ckpt + remove_unused_columns: false + save_best_eval_metric_greater_is_better: true + save_best_eval_metric_name: '' + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + transformers_access_token: null + transformers_cache_dir: null + transformers_local_files_only: false + transformers_trust_remote_code: true + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + use_ddp: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + warmup_ratio: 0.05 + warmup_steps: 0 + weight_decay: 1.0e-05 diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/dataset_statistics.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/dataset_statistics.json new file mode 100644 index 0000000000000000000000000000000000000000..a86ac2888898322487ce2f2d1c64b97d2c00fbf0 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/dataset_statistics.json @@ -0,0 +1,295 @@ +{ + "libero_panda": { + "state": { + "x": { + "min": [ + -0.4828203022480011 + ], + "max": [ + 0.21031762659549713 + ], + "mean": [ + -0.04190658777952194 + ], + "std": [ + 0.10743364691734314 + ], + "q01": [ + -0.3899900782108307 + ], + "q99": [ + 0.1530261474847791 + ] + }, + "y": { + "min": [ + -0.3255046010017395 + ], + "max": [ + 0.39128610491752625 + ], + "mean": [ + 0.03539430722594261 + ], + "std": [ + 0.14424669742584229 + ], + "q01": [ + -0.2838300323486328 + ], + "q99": [ + 0.32915401458740223 + ] + }, + "z": { + "min": [ + 0.445506751537323 + ], + "max": [ + 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a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/final_model_config.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/final_model_config.json new file mode 100644 index 0000000000000000000000000000000000000000..b9b19727e33cfe1437532403e4efd91b4125a276 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/final_model_config.json @@ -0,0 +1,58 @@ +{ + "model_type": "Gr00tN1d6", + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "backbone_model_type": "eagle", + "model_revision": null, + "tune_top_llm_layers": 4, + "backbone_embedding_dim": 2048, + "tune_llm": false, + "tune_visual": false, + "select_layer": 16, + "reproject_vision": false, + "use_flash_attention": true, + "load_bf16": true, + "collator_overwrite_image_inputs": false, + "eagle_collator": true, + "backbone_trainable_params_fp32": true, + "apply_sincos_state_encoding": true, + "use_relative_action": true, + "max_state_dim": 128, + "max_action_dim": 128, + "action_horizon": 50, + "hidden_size": 1024, + "input_embedding_dim": 1536, + "add_pos_embed": true, + "attn_dropout": 0.2, + "use_vlln": true, + "max_seq_len": 1024, + "use_alternate_vl_dit": true, + "attend_text_every_n_blocks": 2, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "num_inference_timesteps": 4, + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_timestep_buckets": 1000, + "tune_projector": true, + "tune_diffusion_model": true, + "tune_vlln": true, + "state_dropout_prob": 0.8, + "state_additive_noise_scale": 0.0, + "use_lie_group_rotation": true, + "lie_rot_start": 0, + "lie_rot_end": 6, + "lie_rot_loss_weight": 1.0, + "lie_num_langevin_steps": 100, + "max_num_embodiments": 32 +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/final_processor_config.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/final_processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6e73810a6eb1d340cdb71c5179d73207b5892f59 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/experiment_cfg/final_processor_config.json @@ -0,0 +1,27 @@ +{ + "modality_configs": "{'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}", + "state_action_processor": "StateActionProcessor(modality_configs={'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}, statistics={'behavior_r1_pro': {'state': {'robot_pos': {'min': [-15.878335952758789, -4.874791622161865, -0.0030556470155715942], 'max': [28.783069610595703, 25.5261173248291, 0.03920263051986694], 'mean': [8.377509353981011, 3.5519228390967257, 0.005352396491811134], 'std': [9.333495189323855, 6.293349382128611, 0.002169489814694542], 'q01': [-15.627003288269043, -3.923245668411255, 0.00464746356010437], 'q99': [28.161231212615963, 25.433036193847656, 0.019137874245643616]}, 'robot_ori_cos': {'min': [0.9964054226875305, 0.9807400703430176, -1.0], 'max': [1.0, 1.0, 1.0], 'mean': [0.9999999103013197, 0.9999999012025106, -0.04432371966161217], 'std': [1.0955394108707479e-05, 1.5141053684175453e-05, 0.7159057916231842], 'q01': [0.9996431469917297, 0.9996242523193359, -0.9999993443489075], 'q99': [0.9999995827674866, 0.9999999403953552, 0.9999995231628418]}, 'robot_ori_sin': {'min': [-0.08471290022134781, -0.0467030331492424, -1.0], 'max': [0.03247667849063873, 0.1953173577785492, 1.0], 'mean': [-0.0014819139726979956, 0.0011138110099060865, -0.1747246084485246], 'std': [0.0010692286968725616, 0.0011352230347134887, 0.6740228681643635], 'q01': [-0.026712577790021896, -0.027393728494644165, -0.9999995827674866], 'q99': [0.023761406727135177, 0.015501312464475616, 0.9999998211860657]}, 'robot_2d_ori': {'min': [-3.1415927410125732], 'max': [3.1415927410125732], 'mean': [-0.34116709549670976], 'std': [1.8744272700649074], 'q01': [-3.139659881591797], 'q99': [3.1393938064575195]}, 'robot_2d_ori_cos': {'min': [-1.0], 'max': [1.0], 'mean': [-0.04432376387887985], 'std': [0.7159057729093367], 'q01': [-0.9999993443489075], 'q99': [0.9999995231628418]}, 'robot_2d_ori_sin': {'min': [-1.0], 'max': [1.0], 'mean': [-0.1747246006771579], 'std': [0.6740228728055352], 'q01': [-0.9999995827674866], 'q99': [0.9999998211860657]}, 'robot_lin_vel': {'min': [-0.32111313939094543, -0.3024478554725647, -0.338993102312088], 'max': [0.2888447046279907, 0.29485562443733215, 0.27627527713775635], 'mean': [0.0008032824331051274, -0.000851410695872969, -0.014532478391340347], 'std': [0.04011179386851422, 0.03532797622061294, 0.010790340283590575], 'q01': [-0.1382001194357872, -0.13443224787712096, -0.05219040531665087], 'q99': [0.13666878938674926, 0.13471105083823204, 0.005502225868403539]}, 'robot_ang_vel': {'min': [-1.5866682529449463, -1.7150360345840454, -0.04071682691574097], 'max': [1.6171817779541016, 1.5271120071411133, 0.024320760741829872], 'mean': [0.004609276467004075, 0.004284130662207927, -5.683130788171653e-05], 'std': [0.18832387264077133, 0.2137817785121968, 0.0008538363202659394], 'q01': [-0.7182695555686951, -0.736412662267685, -0.008470569178462029], 'q99': [0.7163371068239212, 0.7284575819969177, 0.008367371931672096]}, 'arm_left_qpos': {'min': [-3.3108322620391846, -0.17451578378677368, -2.3561959266662598, -1.7452956438064575, -2.3561980724334717, -1.0472002029418945, -1.5708036422729492], 'max': [0.5505963563919067, 3.0216875076293945, 2.356196165084839, 0.16950564086437225, 2.3564183712005615, 1.0471999645233154, 1.57080078125], 'mean': [-0.44064831753394845, 0.044810599293520874, -0.09667299499241816, -0.6179559573275901, 0.37324754543867494, 0.16138558929363642, -0.04477622658352863], 'std': [0.4374789217045343, 0.16097317375158413, 0.4566687142938444, 0.5212846522479491, 0.5669997492408592, 0.5223829217451378, 0.5210728198228], 'q01': [-2.61892716884613, -0.17450003325939178, -2.296152515411377, -1.6367655992507935, -1.6587732124328614, -1.04719078540802, -1.5708000659942627], 'q99': [0.5170108753442764, 0.8608633208274836, 1.9160030841827416, 0.09510957151651382, 2.3561954498291016, 1.0471981763839722, 1.570236325263977]}, 'arm_left_qpos_sin': {'min': [-1.0, -0.17363129556179047, -1.0, -1.0, -1.0, -0.8660266995429993, -1.0], 'max': [0.5231955647468567, 1.0, 1.0, 0.1686950922012329, 1.0, 0.8660265803337097, 1.0], 'mean': [-0.3788414888506152, 0.043208541825812886, -0.07497595954692522, -0.49673881006309, 0.2950975242538935, 0.13821974557859906, -0.023564394694352786], 'std': [0.33500504759538763, 0.1550813208035862, 0.35555302155891993, 0.3650771441344372, 0.394534724671373, 0.4671246774437793, 0.4220550333326586], 'q01': [-0.9998769760131836, -0.17361578345298767, -0.9997559851408004, -0.999994695186615, -0.9991854596138, -0.866021990776062, -1.0], 'q99': [0.49428389251232147, 0.7477953410148599, 0.9990536546707154, 0.0949662446975708, 0.9998820424079895, 0.8660256862640381, 0.9999998211860657]}, 'arm_left_qpos_cos': {'min': [-1.0, -0.9928199648857117, -0.7071079611778259, -0.17361508309841156, -0.7072650790214539, 0.49999770522117615, -7.315477887459565e-06], 'max': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'mean': [0.8307857834306867, 0.9920001827094036, 0.9118657948785598, 0.7166911882650612, 0.810864158310747, 0.8620206050389164, 0.8824085596125505], 'std': [0.24568417534984152, 0.03566746717075397, 0.21956918727934097, 0.33274636593381524, 0.32686230999243526, 0.1594220496831616, 0.2283688098196029], 'q01': [-0.866491652727127, 0.6517829716205596, -0.6634020233154296, -0.06592143326997757, -0.7071074843406677, 0.499999463558197, -3.739199428309803e-06], 'q99': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, 'eef_left_pos': {'min': [-0.29318809509277344, -0.2712371051311493, 0.002660512924194336], 'max': [1.213043212890625, 1.040291666984558, 2.0455143451690674], 'mean': [0.5160713621676302, 0.2474851475100056, 0.6836650939637079], 'std': [0.21588456124837183, 0.12222884073343476, 0.27805653079791637], 'q01': [-0.1094246196746826, -0.1754944610595703, 0.02435866668820381], 'q99': [1.0699838256835932, 0.7893409729003906, 1.8705657005310052]}, 'eef_left_quat': {'min': [-0.8588797450065613, -0.8650590181350708, -0.841005265712738, -0.5], 'max': [0.9999929666519165, 1.0, 0.9845409393310547, 0.9617436528205872], 'mean': [-0.0663535199862692, 0.8611118942418127, -0.06413671264087122, 0.3115006830960212], 'std': [0.1792821484038788, 0.19792752849522144, 0.217055016999389, 0.20829947525926978], 'q01': [-0.6571075320243835, -0.6516380822658538, -0.6857465314865112, -0.48287599831819533], 'q99': [0.7483707463741305, 0.9999786615371704, 0.8158004015684127, 0.8217956781387328]}, 'gripper_left_qpos': {'min': [0.0, 0.0], 'max': [0.05000000074505806, 0.05000000074505806], 'mean': [0.04408877553139796, 0.038755202861951284], 'std': [0.011461306769436432, 0.0185620058519011], 'q01': [6.05472345682756e-08, 3.996996369437511e-08], 'q99': [0.05000000074505806, 0.05000000074505806]}, 'arm_right_qpos': {'min': [-3.206026315689087, -3.005096912384033, -2.356199026107788, -1.7454899549484253, -2.356196403503418, -1.0471998453140259, -1.570801854133606], 'max': [0.4984091520309448, 0.17452052235603333, 2.3561978340148926, 0.34479665756225586, 2.3562169075012207, 1.047200083732605, 1.8344576358795166], 'mean': [-0.4926937303094319, -0.05746068854244716, 0.060067987430524196, -0.6359656591353143, -0.23591194814488878, 0.16645281122902053, 0.048392212598411546], 'std': [0.41749482222866613, 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-0.7757885468006134, -0.7125183200836182], 'q99': [0.86768774271011, 0.8982121348381038, 0.6413099694252014]}, 'gripper_close': {'min': [0.0], 'max': [1.0], 'mean': [0.3555581543825369], 'std': [0.47875685759087344], 'q01': [0.0], 'q99': [1.0]}, 'base_motion': {'min': [0.0, 0.0, 0.0, 0.0], 'max': [0.0, 0.0, 0.0, 0.0], 'mean': [0.0, 0.0, 0.0, 0.0], 'std': [0.0, 0.0, 0.0, 0.0], 'q01': [0.0, 0.0, 0.0, 0.0], 'q99': [0.0, 0.0, 0.0, 0.0]}, 'control_mode': {'min': [0.0], 'max': [0.0], 'mean': [0.0], 'std': [0.0], 'q01': [0.0], 'q99': [0.0]}}}}, use_percentiles=False, clip_outliers=True, apply_sincos_state_encoding=True, use_relative_action=True)", + "use_percentiles": "False", + "clip_outliers": "True", + "apply_sincos_state_encoding": "True", + "use_relative_action": "True", + "formalize_language": "True", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "model_type": "eagle", + "max_state_dim": "128", + "max_action_dim": "128", + "max_action_horizon": "50", + "image_crop_size": "None", + "image_target_size": "None", + "random_rotation_angle": "None", + "color_jitter_params": "{'brightness': 0.3, 'contrast': 0.4, 'saturation': 0.5, 'hue': 0.08}", + "processor": "Eagle3_VLProcessor:\n- image_processor: Eagle3_VLImageProcessorFast {\n \"auto_map\": {\n \"AutoImageProcessor\": \"image_processing_eagle3_vl_fast.Eagle3_VLImageProcessorFast\",\n \"AutoProcessor\": \"processing_eagle3_vl.Eagle3_VLProcessor\"\n },\n \"crop_size\": null,\n \"data_format\": \"channels_first\",\n \"default_to_square\": false,\n \"device\": null,\n \"do_center_crop\": null,\n \"do_convert_rgb\": true,\n \"do_normalize\": true,\n \"do_pad\": false,\n \"do_rescale\": true,\n \"do_resize\": false,\n \"image_mean\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"image_processor_type\": \"Eagle3_VLImageProcessorFast\",\n \"image_std\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"input_data_format\": null,\n \"processor_class\": \"Eagle3_VLProcessor\",\n \"resample\": 3,\n \"rescale_factor\": 0.00392156862745098,\n \"return_tensors\": null,\n \"size\": {\n \"height\": 448,\n \"width\": 448\n }\n}\n\n- tokenizer: Qwen2TokenizerFast(name_or_path='/mnt/nfs/tars111060008/GR00T-lie/gr00t/model/gr00t_n1d6/../modules/nvidia/Eagle-Block2A-2B-v2', vocab_size=151643, model_max_length=32768, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>', '', '', '', '', '', '', '', '', '', '', '']}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151657: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151658: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151665: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151666: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151667: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151668: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n\t151669: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151670: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151671: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151672: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151673: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151674: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151675: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151676: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151677: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151678: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n\t151679: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n}\n)\n\n{\n \"auto_map\": {\n \"AutoProcessor\": \"processing_eagle3_vl.Eagle3_VLProcessor\"\n },\n \"image_end_token\": \"\",\n \"image_placeholder\": \"image\",\n \"image_start_token\": \"\",\n \"image_token\": \"\",\n \"pixels_per_token\": 784,\n \"processor_class\": \"Eagle3_VLProcessor\",\n \"video_placeholder\": \"video\",\n \"video_token\": \"\",\n \"vision_feature_select_strategy\": null\n}\n", + "embodiment_id_mapping": "{'robocasa_panda_omron': 13, 'gr1': 20, 'behavior_r1_pro': 24, 'unitree_g1': 8, 'oxe_google': 0, 'oxe_widowx': 1, 'libero_panda': 2, 'oxe_droid': 16, 'new_embodiment': 10}", + "shortest_image_edge": "256", + "crop_fraction": "0.95", + "use_albumentations": "True", + "train_image_transform": "ReplayCompose([\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n FractionalRandomCrop(p=1.0, crop_fraction=0.95),\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n ColorJitter(p=1.0, brightness=(0.7, 1.3), contrast=(0.6, 1.4), saturation=(0.5, 1.5), hue=(-0.08, 0.08)),\n], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True, save_key='replay')", + "eval_image_transform": "Compose([\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n FractionalCenterCrop(p=1.0, crop_fraction=0.95),\n SmallestMaxSize(p=1.0, max_size=[256], interpolation=3),\n], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True)", + "_collator": "Gr00tN1d6DataCollator(model_name=nvidia/Eagle-Block2A-2B-v2, model_type=eagle)", + "training": "True" +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/global_step50000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt new file mode 100644 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@@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3b4b6b7a40aed3b194aa39cbe94cab79ab7045c945f36259733461dcb736511 +size 7633 diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/wandb_config.json b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/wandb_config.json new file mode 100644 index 0000000000000000000000000000000000000000..df7b475940b7d322567596d4d9c01dd2f37d3e38 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/wandb_config.json @@ -0,0 +1 @@ +{"project": "finetune-gr00t-n1d6", "run_id": "libero_10_lie_gripper_noise_ckpt"} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/zero_to_fp32.py b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/checkpoint-50000/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters) diff --git a/libero_10_lie_gripper_noise_ckpt/config.json b/libero_10_lie_gripper_noise_ckpt/config.json new file mode 100644 index 0000000000000000000000000000000000000000..6fbc3429a62d8737dabbdc6c56c94080fd18128b --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/config.json @@ -0,0 +1,75 @@ +{ + "action_horizon": 50, + "add_pos_embed": true, + "apply_sincos_state_encoding": true, + "architectures": [ + "Gr00tN1d6" + ], + "attn_dropout": 0.2, + "attn_implementation": null, + "backbone_embedding_dim": 2048, + "backbone_model_type": "eagle", + "backbone_trainable_params_fp32": true, + "collator_overwrite_image_inputs": false, + "color_jitter_params": { + "brightness": 0.1, + "contrast": 0.1, + "hue": 0.1, + "saturation": 0.1 + }, + "crop_fraction": 0.95, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "eagle_collator": true, + "formalize_language": true, + "gemma_collator": false, + "hidden_size": 1024, + "image_crop_size": null, + "image_target_size": null, + "input_embedding_dim": 1536, + "lie_num_langevin_steps": 100, + "lie_rot_end": 6, + "lie_rot_loss_weight": 1.0, + "lie_rot_start": 0, + "load_bf16": true, + "max_action_dim": 128, + "max_num_embodiments": 32, + "max_seq_len": 1024, + "max_state_dim": 128, + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "model_type": "Gr00tN1d6", + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_inference_timesteps": 4, + "num_timestep_buckets": 1000, + "random_rotation_angle": null, + "reproject_vision": false, + "select_layer": 16, + "shortest_image_edge": 256, + "state_dropout_prob": 0.8, + "torch_dtype": "bfloat16", + "transformers_version": "4.51.3", + "tune_diffusion_model": true, + "tune_llm": false, + "tune_projector": true, + "tune_top_llm_layers": 4, + "tune_visual": false, + "tune_vlln": true, + "use_albumentations_transforms": true, + "use_alternate_vl_dit": true, + "use_flash_attention": true, + "use_lie_group_rotation": true, + "use_relative_action": true, + "use_vlln": true +} diff --git a/libero_10_lie_gripper_noise_ckpt/experiment_cfg/conf.yaml b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/conf.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a943c8711196aa9cd431e57c663bfd864e890663 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/conf.yaml @@ -0,0 +1,244 @@ +load_config_path: null +model: + model_type: Gr00tN1d6 + model_dtype: bfloat16 + model_name: nvidia/Eagle-Block2A-2B-v2 + backbone_model_type: eagle + model_revision: null + tune_top_llm_layers: 4 + backbone_embedding_dim: 2048 + tune_llm: false + tune_visual: false + select_layer: 16 + reproject_vision: false + use_flash_attention: true + load_bf16: false + collator_overwrite_image_inputs: false + eagle_collator: true + backbone_trainable_params_fp32: true + image_crop_size: null + image_target_size: null + shortest_image_edge: 256 + crop_fraction: 0.95 + random_rotation_angle: null + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + saturation: 0.5 + hue: 0.08 + use_albumentations_transforms: true + formalize_language: true + apply_sincos_state_encoding: false + use_relative_action: true + max_state_dim: 29 + max_action_dim: 29 + action_horizon: 16 + hidden_size: 1024 + input_embedding_dim: 1536 + add_pos_embed: true + attn_dropout: 0.2 + use_vlln: true + max_seq_len: 1024 + use_alternate_vl_dit: true + attend_text_every_n_blocks: 2 + diffusion_model_cfg: + positional_embeddings: null + num_layers: 32 + num_attention_heads: 32 + attention_head_dim: 48 + norm_type: ada_norm + dropout: 0.2 + final_dropout: true + output_dim: 1024 + interleave_self_attention: true + num_inference_timesteps: 4 + noise_beta_alpha: 1.5 + noise_beta_beta: 1.0 + noise_s: 0.999 + num_timestep_buckets: 1000 + tune_projector: true + tune_diffusion_model: true + tune_vlln: true + state_dropout_prob: 0.8 + state_additive_noise_scale: 0.0 + use_lie_group_rotation: true + lie_rot_start: 0 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_num_langevin_steps: 100 + max_num_embodiments: 32 +data: + datasets: + - dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + embodiment_tag: libero_panda + mix_ratio: 1.0 + dataset_type: physical_embodiment + val_dataset_path: null + modality_configs: + libero_panda: + video: + delta_indices: + - 0 + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + state: + delta_indices: + - 0 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + action: + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + modality_keys: + - x + - 'y' + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + - rep: ABSOLUTE + type: NON_EEF + format: DEFAULT + state_key: null + language: + delta_indices: + - 0 + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + mean_std_embedding_keys: null + action_configs: null + download_cache: false + shard_size: 1024 + episode_sampling_rate: 0.1 + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + mode: single_turn + random_chop: 0.0 + mock_dataset_mode: false + shuffle: true + seed: 42 + multiprocessing_context: fork + allow_padding: false + subsample_ratio: 1.0 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + video_backend: torchcodec +training: + output_dir: libero_10_lie_gripper_noise_ckpt + experiment_name: null + max_steps: 50000 + global_batch_size: 640 + batch_size: null + gradient_accumulation_steps: 1 + learning_rate: 0.0001 + lr_scheduler_type: cosine + weight_decay: 1.0e-05 + warmup_ratio: 0.05 + warmup_steps: 0 + max_grad_norm: 1.0 + optim: adamw_torch + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + fp16: false + bf16: true + eval_bf16: true + logging_steps: 10 + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + max_concurrent_uploads: 2 + eval_strategy: 'no' + eval_steps: 500 + eval_set_split_ratio: 0.1 + eval_batch_size: 2 + save_best_eval_metric_name: '' + save_best_eval_metric_greater_is_better: true + deepspeed_stage: 2 + gradient_checkpointing: false + transformers_trust_remote_code: true + transformers_local_files_only: false + transformers_cache_dir: null + transformers_access_token: null + use_ddp: false + ddp_bucket_cap_mb: 100 + num_gpus: 8 + dataloader_num_workers: 4 + remove_unused_columns: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + enable_profiling: false + max_retries: 3 + assert_loss_less_than: null + add_rl_callback: false + enable_open_loop_eval: false + open_loop_eval_traj_ids: + - 0 + open_loop_eval_steps_per_traj: 100 + open_loop_eval_plot_indices: null +max_steps: 50000 +save_steps: 1000 diff --git a/libero_10_lie_gripper_noise_ckpt/experiment_cfg/config.yaml b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c9f37fb4dd7c4e097e0e342b773cb738cb29588f --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/config.yaml @@ -0,0 +1,258 @@ +!!python/object:gr00t.configs.base_config.Config +data: !!python/object:gr00t.configs.data.data_config.DataConfig + allow_padding: false + datasets: + - !!python/object:gr00t.configs.data.data_config.SingleDatasetConfig + dataset_paths: + - examples/LIBERO/libero_10_no_noops_1.0.0_lerobot/ + dataset_type: physical_embodiment + embodiment_tag: libero_panda + mix_ratio: 1.0 + val_dataset_path: null + download_cache: false + episode_sampling_rate: 0.1 + image_crop_size: + - 244 + - 244 + image_target_size: + - 224 + - 224 + mock_dataset_mode: false + modality_configs: + libero_panda: + action: !!python/object:gr00t.data.types.ModalityConfig + action_configs: + - &id001 !!python/object:gr00t.data.types.ActionConfig + format: !!python/object/apply:gr00t.data.types.ActionFormat + - default + rep: !!python/object/apply:gr00t.data.types.ActionRepresentation + - absolute + state_key: null + type: !!python/object/apply:gr00t.data.types.ActionType + - non_eef + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + - *id001 + delta_indices: + - 0 + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + language: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - annotation.human.action.task_description + sin_cos_embedding_keys: null + state: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - x + - y + - z + - roll + - pitch + - yaw + - gripper + sin_cos_embedding_keys: null + video: !!python/object:gr00t.data.types.ModalityConfig + action_configs: null + delta_indices: + - 0 + mean_std_embedding_keys: null + modality_keys: + - image + - wrist_image + sin_cos_embedding_keys: null + mode: single_turn + multiprocessing_context: fork + num_shards_per_epoch: 100000 + override_pretraining_statistics: false + random_chop: 0.0 + seed: 42 + shard_size: 1024 + shuffle: true + subsample_ratio: 1.0 + video_backend: torchcodec +load_config_path: null +model: !!python/object:gr00t.configs.model.gr00t_n1d6.Gr00tN1d6Config + _attn_implementation_autoset: false + _attn_implementation_internal: null + _commit_hash: null + _name_or_path: '' + add_cross_attention: false + architectures: null + backbone_model_type: eagle + backbone_trainable_params_fp32: true + bad_words_ids: null + begin_suppress_tokens: null + bos_token_id: null + chunk_size_feed_forward: 0 + color_jitter_params: + brightness: 0.3 + contrast: 0.4 + hue: 0.08 + saturation: 0.5 + cross_attention_hidden_size: null + decoder_start_token_id: null + diffusion_model_cfg: + attention_head_dim: 48 + dropout: 0.2 + final_dropout: true + interleave_self_attention: true + norm_type: ada_norm + num_attention_heads: 32 + num_layers: 32 + output_dim: 1024 + positional_embeddings: null + diversity_penalty: 0.0 + do_sample: false + eagle_collator: true + early_stopping: false + encoder_no_repeat_ngram_size: 0 + eos_token_id: null + exponential_decay_length_penalty: null + finetuning_task: null + forced_bos_token_id: null + forced_eos_token_id: null + id2label: + 0: LABEL_0 + 1: LABEL_1 + is_decoder: false + is_encoder_decoder: false + label2id: + LABEL_0: 0 + LABEL_1: 1 + length_penalty: 1.0 + lie_num_langevin_steps: 100 + lie_rot_end: 6 + lie_rot_loss_weight: 1.0 + lie_rot_start: 0 + load_bf16: false + max_length: 20 + min_length: 0 + model_name: nvidia/Eagle-Block2A-2B-v2 + no_repeat_ngram_size: 0 + num_beam_groups: 1 + num_beams: 1 + num_return_sequences: 1 + output_attentions: false + output_hidden_states: false + output_scores: false + pad_token_id: null + prefix: null + problem_type: null + pruned_heads: {} + random_rotation_angle: null + remove_invalid_values: false + repetition_penalty: 1.0 + reproject_vision: false + return_dict: true + return_dict_in_generate: false + sep_token_id: null + state_dropout_prob: 0.8 + suppress_tokens: null + task_specific_params: null + temperature: 1.0 + tf_legacy_loss: false + tie_encoder_decoder: false + tie_word_embeddings: true + tokenizer_class: null + top_k: 50 + top_p: 1.0 + torch_dtype: null + torchscript: false + transformers_version: null + tune_diffusion_model: true + tune_llm: false + tune_projector: true + tune_visual: false + typical_p: 1.0 + use_bfloat16: false + use_lie_group_rotation: true + use_relative_action: true +training: !!python/object:gr00t.configs.training.training_config.TrainingConfig + add_rl_callback: false + assert_loss_less_than: null + batch_size: null + bf16: true + dataloader_num_workers: 4 + ddp_bucket_cap_mb: 100 + deepspeed_stage: 2 + enable_open_loop_eval: false + enable_profiling: false + eval_batch_size: 2 + eval_bf16: true + eval_set_split_ratio: 0.1 + eval_steps: 500 + eval_strategy: 'no' + experiment_name: null + fp16: false + global_batch_size: 640 + gradient_accumulation_steps: 1 + gradient_checkpointing: false + learning_rate: 0.0001 + logging_steps: 10 + lr_scheduler_type: cosine + max_concurrent_uploads: 2 + max_grad_norm: 1.0 + max_retries: 3 + max_steps: 50000 + num_gpus: 8 + open_loop_eval_plot_indices: null + open_loop_eval_steps_per_traj: 100 + open_loop_eval_traj_ids: + - 0 + optim: adamw_torch + output_dir: libero_10_lie_gripper_noise_ckpt + remove_unused_columns: false + save_best_eval_metric_greater_is_better: true + save_best_eval_metric_name: '' + save_steps: 1000 + save_total_limit: 5 + save_vl_model: false + start_from_checkpoint: nvidia/GR00T-N1.6-3B + tf32: true + transformers_access_token: null + transformers_cache_dir: null + transformers_local_files_only: false + transformers_trust_remote_code: true + upload_checkpoints: false + upload_every: 1000 + upload_last_n_checkpoints: 5 + use_ddp: false + use_wandb: true + wandb_project: finetune-gr00t-n1d6 + warmup_ratio: 0.05 + warmup_steps: 0 + weight_decay: 1.0e-05 diff --git a/libero_10_lie_gripper_noise_ckpt/experiment_cfg/dataset_statistics.json b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/dataset_statistics.json new file mode 100644 index 0000000000000000000000000000000000000000..a86ac2888898322487ce2f2d1c64b97d2c00fbf0 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/dataset_statistics.json @@ -0,0 +1,295 @@ +{ + "libero_panda": { + "state": { + "x": { + "min": [ + -0.4828203022480011 + ], + "max": [ + 0.21031762659549713 + ], + "mean": [ + -0.04190658777952194 + ], + "std": [ + 0.10743364691734314 + ], + "q01": [ + -0.3899900782108307 + ], + "q99": [ + 0.1530261474847791 + ] + }, + "y": { + "min": [ + -0.3255046010017395 + ], + "max": [ + 0.39128610491752625 + ], + 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+ 0.49815231561660767 + ], + "q01": [ + 0.0 + ], + "q99": [ + 1.0 + ] + } + }, + "relative_action": {} + } +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/experiment_cfg/final_model_config.json b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/final_model_config.json new file mode 100644 index 0000000000000000000000000000000000000000..b9b19727e33cfe1437532403e4efd91b4125a276 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/final_model_config.json @@ -0,0 +1,58 @@ +{ + "model_type": "Gr00tN1d6", + "model_dtype": "bfloat16", + "model_name": "nvidia/Eagle-Block2A-2B-v2", + "backbone_model_type": "eagle", + "model_revision": null, + "tune_top_llm_layers": 4, + "backbone_embedding_dim": 2048, + "tune_llm": false, + "tune_visual": false, + "select_layer": 16, + "reproject_vision": false, + "use_flash_attention": true, + "load_bf16": true, + "collator_overwrite_image_inputs": false, + "eagle_collator": true, + "backbone_trainable_params_fp32": true, + "apply_sincos_state_encoding": true, + "use_relative_action": true, + "max_state_dim": 128, + "max_action_dim": 128, + "action_horizon": 50, + "hidden_size": 1024, + "input_embedding_dim": 1536, + "add_pos_embed": true, + "attn_dropout": 0.2, + "use_vlln": true, + "max_seq_len": 1024, + "use_alternate_vl_dit": true, + "attend_text_every_n_blocks": 2, + "diffusion_model_cfg": { + "attention_head_dim": 48, + "dropout": 0.2, + "final_dropout": true, + "interleave_self_attention": true, + "norm_type": "ada_norm", + "num_attention_heads": 32, + "num_layers": 32, + "output_dim": 1024, + "positional_embeddings": null + }, + "num_inference_timesteps": 4, + "noise_beta_alpha": 1.5, + "noise_beta_beta": 1.0, + "noise_s": 0.999, + "num_timestep_buckets": 1000, + "tune_projector": true, + "tune_diffusion_model": true, + "tune_vlln": true, + "state_dropout_prob": 0.8, + "state_additive_noise_scale": 0.0, + "use_lie_group_rotation": true, + "lie_rot_start": 0, + "lie_rot_end": 6, + "lie_rot_loss_weight": 1.0, + "lie_num_langevin_steps": 100, + "max_num_embodiments": 32 +} \ No newline at end of file diff --git a/libero_10_lie_gripper_noise_ckpt/experiment_cfg/final_processor_config.json b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/final_processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6e73810a6eb1d340cdb71c5179d73207b5892f59 --- /dev/null +++ b/libero_10_lie_gripper_noise_ckpt/experiment_cfg/final_processor_config.json @@ -0,0 +1,27 @@ +{ + "modality_configs": "{'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}", + "state_action_processor": "StateActionProcessor(modality_configs={'behavior_r1_pro': {'video': ModalityConfig(delta_indices=[0], modality_keys=['observation.images.rgb.head_256_256', 'observation.images.rgb.left_wrist_256_256', 'observation.images.rgb.right_wrist_256_256'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['robot_pos', 'robot_ori_cos', 'robot_ori_sin', 'robot_2d_ori', 'robot_2d_ori_cos', 'robot_2d_ori_sin', 'robot_lin_vel', 'robot_ang_vel', 'arm_left_qpos', 'arm_left_qpos_sin', 'arm_left_qpos_cos', 'eef_left_pos', 'eef_left_quat', 'gripper_left_qpos', 'arm_right_qpos', 'arm_right_qpos_sin', 'arm_right_qpos_cos', 'eef_right_pos', 'eef_right_quat', 'gripper_right_qpos', 'trunk_qpos'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], modality_keys=['base', 'torso', 'left_arm', 'left_gripper', 'right_arm', 'right_gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='trunk_qpos'), ActionConfig(rep=, type=, format=, state_key='arm_left_qpos'), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key='arm_right_qpos'), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.coarse_action'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'gr1': {'video': ModalityConfig(delta_indices=[0], modality_keys=['ego_view_bg_crop_pad_res256_freq20'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['left_arm', 'right_arm', 'left_hand', 'right_hand', 'waist'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['task'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'robocasa_panda_omron': {'video': ModalityConfig(delta_indices=[0], modality_keys=['res256_image_side_0', 'res256_image_side_1', 'res256_image_wrist_0'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['end_effector_position_relative', 'end_effector_rotation_relative', 'gripper_qpos', 'base_position', 'base_rotation'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['end_effector_position', 'end_effector_rotation', 'gripper_close', 'base_motion', 'control_mode'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}, 'libero_panda': {'video': ModalityConfig(delta_indices=[0], modality_keys=['image', 'wrist_image'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'state': ModalityConfig(delta_indices=[0], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None), 'action': ModalityConfig(delta_indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], modality_keys=['x', 'y', 'z', 'roll', 'pitch', 'yaw', 'gripper'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=[ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None), ActionConfig(rep=, type=, format=, state_key=None)]), 'language': ModalityConfig(delta_indices=[0], modality_keys=['annotation.human.action.task_description'], sin_cos_embedding_keys=None, mean_std_embedding_keys=None, action_configs=None)}}, 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"image_target_size": "None", + "random_rotation_angle": "None", + "color_jitter_params": "{'brightness': 0.3, 'contrast': 0.4, 'saturation': 0.5, 'hue': 0.08}", + "processor": "Eagle3_VLProcessor:\n- image_processor: Eagle3_VLImageProcessorFast {\n \"auto_map\": {\n \"AutoImageProcessor\": \"image_processing_eagle3_vl_fast.Eagle3_VLImageProcessorFast\",\n \"AutoProcessor\": \"processing_eagle3_vl.Eagle3_VLProcessor\"\n },\n \"crop_size\": null,\n \"data_format\": \"channels_first\",\n \"default_to_square\": false,\n \"device\": null,\n \"do_center_crop\": null,\n \"do_convert_rgb\": true,\n \"do_normalize\": true,\n \"do_pad\": false,\n \"do_rescale\": true,\n \"do_resize\": false,\n \"image_mean\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"image_processor_type\": \"Eagle3_VLImageProcessorFast\",\n \"image_std\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"input_data_format\": null,\n \"processor_class\": \"Eagle3_VLProcessor\",\n \"resample\": 3,\n \"rescale_factor\": 0.00392156862745098,\n 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