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Browse filesThis view is limited to 50 files because it contains too many changes.
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- added_tokens.json +5 -0
- checkpoint-1130/added_tokens.json +5 -0
- checkpoint-1130/config.json +79 -0
- checkpoint-1130/generation_config.json +14 -0
- checkpoint-1130/latest +1 -0
- checkpoint-1130/merges.txt +0 -0
- checkpoint-1130/model-00001-of-00004.safetensors +3 -0
- checkpoint-1130/model-00002-of-00004.safetensors +3 -0
- checkpoint-1130/model-00003-of-00004.safetensors +3 -0
- checkpoint-1130/model-00004-of-00004.safetensors +3 -0
- checkpoint-1130/model.safetensors.index.json +772 -0
- checkpoint-1130/rng_state_0.pth +3 -0
- checkpoint-1130/rng_state_1.pth +3 -0
- checkpoint-1130/rng_state_2.pth +3 -0
- checkpoint-1130/rng_state_3.pth +3 -0
- checkpoint-1130/rng_state_4.pth +3 -0
- checkpoint-1130/rng_state_5.pth +3 -0
- checkpoint-1130/rng_state_6.pth +3 -0
- checkpoint-1130/rng_state_7.pth +3 -0
- checkpoint-1130/scheduler.pt +3 -0
- checkpoint-1130/special_tokens_map.json +20 -0
- checkpoint-1130/tokenizer.json +0 -0
- checkpoint-1130/tokenizer_config.json +44 -0
- checkpoint-1130/trainer_state.json +0 -0
- checkpoint-1130/training_args.bin +3 -0
- checkpoint-1130/vocab.json +0 -0
- checkpoint-1130/zero_to_fp32.py +604 -0
- checkpoint-565/added_tokens.json +5 -0
- checkpoint-565/config.json +79 -0
- checkpoint-565/generation_config.json +14 -0
- checkpoint-565/latest +1 -0
- checkpoint-565/merges.txt +0 -0
- checkpoint-565/model-00001-of-00004.safetensors +3 -0
- checkpoint-565/model-00002-of-00004.safetensors +3 -0
- checkpoint-565/model-00003-of-00004.safetensors +3 -0
- checkpoint-565/model-00004-of-00004.safetensors +3 -0
- checkpoint-565/model.safetensors.index.json +772 -0
- checkpoint-565/rng_state_0.pth +3 -0
- checkpoint-565/rng_state_1.pth +3 -0
- checkpoint-565/rng_state_2.pth +3 -0
- checkpoint-565/rng_state_3.pth +3 -0
- checkpoint-565/rng_state_4.pth +3 -0
- checkpoint-565/rng_state_5.pth +3 -0
- checkpoint-565/rng_state_6.pth +3 -0
- checkpoint-565/rng_state_7.pth +3 -0
- checkpoint-565/scheduler.pt +3 -0
- checkpoint-565/special_tokens_map.json +20 -0
- checkpoint-565/tokenizer.json +0 -0
- checkpoint-565/tokenizer_config.json +44 -0
- checkpoint-565/trainer_state.json +0 -0
added_tokens.json
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{
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644
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}
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checkpoint-1130/added_tokens.json
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{
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644
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}
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checkpoint-1130/config.json
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{
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"_name_or_path": "/gpfs/public/research/junpeng/checkpoints/0919_llavanext_siglip_qwen2",
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"add_faster_video": false,
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"add_time_instruction": false,
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"architectures": [
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"LlavaQwenForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"faster_token_stride": 10,
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"force_sample": false,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"image_aspect_ratio": "anyres",
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"image_crop_resolution": null,
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"image_grid_pinpoints": [
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"image_split_resolution": null,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"mm_hidden_size": 1152,
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"mm_newline_position": "grid",
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"mm_patch_merge_type": "spatial_unpad",
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"mm_projector_lr": null,
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"mm_projector_type": "mlp2x_gelu",
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"mm_resampler_type": null,
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"mm_spatial_pool_mode": "bilinear",
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"mm_spatial_pool_stride": null,
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"mm_tunable_parts": "mm_vision_tower,mm_mlp_adapter,mm_language_model",
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"mm_use_im_patch_token": false,
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"mm_use_im_start_end": false,
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"mm_vision_select_feature": "patch",
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"mm_vision_select_layer": -2,
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"mm_vision_tower": "google/siglip-so400m-patch14-384",
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"mm_vision_tower_lr": 2e-06,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pos_skipping_range": 4096,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": 131072,
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"tie_word_embeddings": false,
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"tokenizer_model_max_length": 8192,
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"tokenizer_padding_side": "right",
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"torch_dtype": "bfloat16",
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"transformers_version": "4.41.0",
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"use_cache": false,
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"use_mm_proj": true,
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"use_pos_skipping": false,
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"use_sliding_window": false,
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"vision_tower_pretrained": null,
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"vocab_size": 152064
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}
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checkpoint-1130/generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.41.0"
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}
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checkpoint-1130/latest
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global_step1130
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checkpoint-1130/merges.txt
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checkpoint-1130/model-00001-of-00004.safetensors
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size 4877668032
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checkpoint-1130/model-00002-of-00004.safetensors
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size 4932751008
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checkpoint-1130/model-00003-of-00004.safetensors
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checkpoint-1130/model-00004-of-00004.safetensors
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checkpoint-1130/model.safetensors.index.json
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#!/usr/bin/env python
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# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
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# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
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# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
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# application.
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#
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# example: python zero_to_fp32.py . pytorch_model.bin
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import argparse
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import torch
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import glob
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import math
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import os
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import re
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from collections import OrderedDict
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from dataclasses import dataclass
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# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
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# DeepSpeed data structures it has to be available in the current python environment.
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from deepspeed.utils import logger
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from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
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FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
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FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
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@dataclass
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class zero_model_state:
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buffers: dict()
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param_shapes: dict()
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shared_params: list
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ds_version: int
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frozen_param_shapes: dict()
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frozen_param_fragments: dict()
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debug = 0
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# load to cpu
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device = torch.device('cpu')
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def atoi(text):
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return int(text) if text.isdigit() else text
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def natural_keys(text):
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'''
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alist.sort(key=natural_keys) sorts in human order
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http://nedbatchelder.com/blog/200712/human_sorting.html
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(See Toothy's implementation in the comments)
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'''
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return [atoi(c) for c in re.split(r'(\d+)', text)]
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def get_model_state_file(checkpoint_dir, zero_stage):
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if not os.path.isdir(checkpoint_dir):
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raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
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# there should be only one file
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if zero_stage <= 2:
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file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
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elif zero_stage == 3:
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file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
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if not os.path.exists(file):
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raise FileNotFoundError(f"can't find model states file at '{file}'")
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return file
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def get_checkpoint_files(checkpoint_dir, glob_pattern):
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# XXX: need to test that this simple glob rule works for multi-node setup too
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ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
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if len(ckpt_files) == 0:
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raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
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return ckpt_files
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def get_optim_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
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def get_model_state_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
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def parse_model_states(files):
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zero_model_states = []
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for file in files:
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state_dict = torch.load(file, map_location=device)
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if BUFFER_NAMES not in state_dict:
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raise ValueError(f"{file} is not a model state checkpoint")
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buffer_names = state_dict[BUFFER_NAMES]
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if debug:
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print("Found buffers:", buffer_names)
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# recover just the buffers while restoring them to fp32 if they were saved in fp16
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buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
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param_shapes = state_dict[PARAM_SHAPES]
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# collect parameters that are included in param_shapes
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param_names = []
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for s in param_shapes:
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for name in s.keys():
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param_names.append(name)
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# update with frozen parameters
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frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
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if frozen_param_shapes is not None:
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if debug:
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print(f"Found frozen_param_shapes: {frozen_param_shapes}")
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param_names += list(frozen_param_shapes.keys())
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# handle shared params
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shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
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ds_version = state_dict.get(DS_VERSION, None)
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frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
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z_model_state = zero_model_state(buffers=buffers,
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param_shapes=param_shapes,
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shared_params=shared_params,
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ds_version=ds_version,
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frozen_param_shapes=frozen_param_shapes,
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frozen_param_fragments=frozen_param_fragments)
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zero_model_states.append(z_model_state)
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return zero_model_states
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def parse_optim_states(files, ds_checkpoint_dir):
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total_files = len(files)
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state_dicts = []
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for f in files:
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state_dict = torch.load(f, map_location=device)
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# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
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# and also handle the case where it was already removed by another helper script
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state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
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state_dicts.append(state_dict)
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if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
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raise ValueError(f"{files[0]} is not a zero checkpoint")
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zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
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world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
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# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
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# parameters can be different from data parallelism for non-expert parameters. So we can just
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# use the max of the partition_count to get the dp world_size.
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if type(world_size) is list:
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world_size = max(world_size)
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if world_size != total_files:
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raise ValueError(
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f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
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"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
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)
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# the groups are named differently in each stage
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if zero_stage <= 2:
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fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
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elif zero_stage == 3:
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fp32_groups_key = FP32_FLAT_GROUPS
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else:
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raise ValueError(f"unknown zero stage {zero_stage}")
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if zero_stage <= 2:
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fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
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elif zero_stage == 3:
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# if there is more than one param group, there will be multiple flattened tensors - one
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# flattened tensor per group - for simplicity merge them into a single tensor
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#
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# XXX: could make the script more memory efficient for when there are multiple groups - it
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# will require matching the sub-lists of param_shapes for each param group flattened tensor
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fp32_flat_groups = [
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torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
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]
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return zero_stage, world_size, fp32_flat_groups
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def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
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"""
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Returns fp32 state_dict reconstructed from ds checkpoint
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Args:
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- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
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"""
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print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
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optim_files = get_optim_files(ds_checkpoint_dir)
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zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
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print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
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model_files = get_model_state_files(ds_checkpoint_dir)
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zero_model_states = parse_model_states(model_files)
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print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
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if zero_stage <= 2:
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return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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exclude_frozen_parameters)
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elif zero_stage == 3:
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return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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exclude_frozen_parameters)
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def _zero2_merge_frozen_params(state_dict, zero_model_states):
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if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
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return
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frozen_param_shapes = zero_model_states[0].frozen_param_shapes
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frozen_param_fragments = zero_model_states[0].frozen_param_fragments
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if debug:
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num_elem = sum(s.numel() for s in frozen_param_shapes.values())
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print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
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wanted_params = len(frozen_param_shapes)
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wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
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avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
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print(f'Frozen params: Have {avail_numel} numels to process.')
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print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
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total_params = 0
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total_numel = 0
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for name, shape in frozen_param_shapes.items():
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total_params += 1
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unpartitioned_numel = shape.numel()
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total_numel += unpartitioned_numel
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state_dict[name] = frozen_param_fragments[name]
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if debug:
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print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
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def _has_callable(obj, fn):
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attr = getattr(obj, fn, None)
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return callable(attr)
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def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
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param_shapes = zero_model_states[0].param_shapes
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# Reconstruction protocol:
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#
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# XXX: document this
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if debug:
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for i in range(world_size):
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for j in range(len(fp32_flat_groups[0])):
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print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
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# XXX: memory usage doubles here (zero2)
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num_param_groups = len(fp32_flat_groups[0])
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merged_single_partition_of_fp32_groups = []
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for i in range(num_param_groups):
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merged_partitions = [sd[i] for sd in fp32_flat_groups]
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full_single_fp32_vector = torch.cat(merged_partitions, 0)
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merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
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avail_numel = sum(
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[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
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if debug:
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wanted_params = sum([len(shapes) for shapes in param_shapes])
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wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
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# not asserting if there is a mismatch due to possible padding
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print(f"Have {avail_numel} numels to process.")
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print(f"Need {wanted_numel} numels in {wanted_params} params.")
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# params
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# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
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# out-of-core computing solution
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total_numel = 0
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total_params = 0
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for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
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offset = 0
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avail_numel = full_single_fp32_vector.numel()
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for name, shape in shapes.items():
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unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
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total_numel += unpartitioned_numel
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total_params += 1
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if debug:
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print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
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offset += unpartitioned_numel
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# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
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# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
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# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
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# live optimizer object, so we are checking that the numbers are within the right range
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align_to = 2 * world_size
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def zero2_align(x):
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return align_to * math.ceil(x / align_to)
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if debug:
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print(f"original offset={offset}, avail_numel={avail_numel}")
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offset = zero2_align(offset)
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avail_numel = zero2_align(avail_numel)
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if debug:
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print(f"aligned offset={offset}, avail_numel={avail_numel}")
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# Sanity check
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if offset != avail_numel:
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raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
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print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
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+
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def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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exclude_frozen_parameters):
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state_dict = OrderedDict()
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# buffers
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buffers = zero_model_states[0].buffers
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state_dict.update(buffers)
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if debug:
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print(f"added {len(buffers)} buffers")
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if not exclude_frozen_parameters:
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_zero2_merge_frozen_params(state_dict, zero_model_states)
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_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
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# recover shared parameters
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for pair in zero_model_states[0].shared_params:
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if pair[1] in state_dict:
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state_dict[pair[0]] = state_dict[pair[1]]
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return state_dict
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+
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def zero3_partitioned_param_info(unpartitioned_numel, world_size):
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remainder = unpartitioned_numel % world_size
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padding_numel = (world_size - remainder) if remainder else 0
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partitioned_numel = math.ceil(unpartitioned_numel / world_size)
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return partitioned_numel, padding_numel
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+
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+
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def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
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if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
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return
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+
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if debug:
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for i in range(world_size):
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num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
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print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
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+
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370 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
371 |
+
wanted_params = len(frozen_param_shapes)
|
372 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
373 |
+
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
374 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
375 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
376 |
+
|
377 |
+
total_params = 0
|
378 |
+
total_numel = 0
|
379 |
+
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
380 |
+
total_params += 1
|
381 |
+
unpartitioned_numel = shape.numel()
|
382 |
+
total_numel += unpartitioned_numel
|
383 |
+
|
384 |
+
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
385 |
+
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
386 |
+
|
387 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
388 |
+
|
389 |
+
if debug:
|
390 |
+
print(
|
391 |
+
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
392 |
+
)
|
393 |
+
|
394 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
395 |
+
|
396 |
+
|
397 |
+
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
398 |
+
param_shapes = zero_model_states[0].param_shapes
|
399 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
400 |
+
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
401 |
+
# param, re-consolidating each param, while dealing with padding if any
|
402 |
+
|
403 |
+
# merge list of dicts, preserving order
|
404 |
+
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
405 |
+
|
406 |
+
if debug:
|
407 |
+
for i in range(world_size):
|
408 |
+
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
409 |
+
|
410 |
+
wanted_params = len(param_shapes)
|
411 |
+
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
412 |
+
# not asserting if there is a mismatch due to possible padding
|
413 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
414 |
+
print(f"Trainable params: Have {avail_numel} numels to process.")
|
415 |
+
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
416 |
+
|
417 |
+
# params
|
418 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
419 |
+
# out-of-core computing solution
|
420 |
+
offset = 0
|
421 |
+
total_numel = 0
|
422 |
+
total_params = 0
|
423 |
+
for name, shape in param_shapes.items():
|
424 |
+
|
425 |
+
unpartitioned_numel = shape.numel()
|
426 |
+
total_numel += unpartitioned_numel
|
427 |
+
total_params += 1
|
428 |
+
|
429 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
430 |
+
|
431 |
+
if debug:
|
432 |
+
print(
|
433 |
+
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
434 |
+
)
|
435 |
+
|
436 |
+
# XXX: memory usage doubles here
|
437 |
+
state_dict[name] = torch.cat(
|
438 |
+
tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
|
439 |
+
0).narrow(0, 0, unpartitioned_numel).view(shape)
|
440 |
+
offset += partitioned_numel
|
441 |
+
|
442 |
+
offset *= world_size
|
443 |
+
|
444 |
+
# Sanity check
|
445 |
+
if offset != avail_numel:
|
446 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
447 |
+
|
448 |
+
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
449 |
+
|
450 |
+
|
451 |
+
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
452 |
+
exclude_frozen_parameters):
|
453 |
+
state_dict = OrderedDict()
|
454 |
+
|
455 |
+
# buffers
|
456 |
+
buffers = zero_model_states[0].buffers
|
457 |
+
state_dict.update(buffers)
|
458 |
+
if debug:
|
459 |
+
print(f"added {len(buffers)} buffers")
|
460 |
+
|
461 |
+
if not exclude_frozen_parameters:
|
462 |
+
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
463 |
+
|
464 |
+
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
465 |
+
|
466 |
+
# recover shared parameters
|
467 |
+
for pair in zero_model_states[0].shared_params:
|
468 |
+
if pair[1] in state_dict:
|
469 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
470 |
+
|
471 |
+
return state_dict
|
472 |
+
|
473 |
+
|
474 |
+
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False):
|
475 |
+
"""
|
476 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
477 |
+
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
478 |
+
via a model hub.
|
479 |
+
|
480 |
+
Args:
|
481 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder
|
482 |
+
- ``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``
|
483 |
+
- ``exclude_frozen_parameters``: exclude frozen parameters
|
484 |
+
|
485 |
+
Returns:
|
486 |
+
- pytorch ``state_dict``
|
487 |
+
|
488 |
+
Note: this approach may not work if your application doesn't have sufficient free CPU memory and
|
489 |
+
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
490 |
+
the checkpoint.
|
491 |
+
|
492 |
+
A typical usage might be ::
|
493 |
+
|
494 |
+
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
495 |
+
# do the training and checkpoint saving
|
496 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
497 |
+
model = model.cpu() # move to cpu
|
498 |
+
model.load_state_dict(state_dict)
|
499 |
+
# submit to model hub or save the model to share with others
|
500 |
+
|
501 |
+
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
502 |
+
application. i.e. you will need to re-initialize the deepspeed engine, since
|
503 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
504 |
+
|
505 |
+
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
506 |
+
|
507 |
+
"""
|
508 |
+
if tag is None:
|
509 |
+
latest_path = os.path.join(checkpoint_dir, 'latest')
|
510 |
+
if os.path.isfile(latest_path):
|
511 |
+
with open(latest_path, 'r') as fd:
|
512 |
+
tag = fd.read().strip()
|
513 |
+
else:
|
514 |
+
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
515 |
+
|
516 |
+
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
517 |
+
|
518 |
+
if not os.path.isdir(ds_checkpoint_dir):
|
519 |
+
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
520 |
+
|
521 |
+
return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
522 |
+
|
523 |
+
|
524 |
+
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None, exclude_frozen_parameters=False):
|
525 |
+
"""
|
526 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
527 |
+
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
528 |
+
|
529 |
+
Args:
|
530 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
531 |
+
- ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
|
532 |
+
- ``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``
|
533 |
+
- ``exclude_frozen_parameters``: exclude frozen parameters
|
534 |
+
"""
|
535 |
+
|
536 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
|
537 |
+
print(f"Saving fp32 state dict to {output_file}")
|
538 |
+
torch.save(state_dict, output_file)
|
539 |
+
|
540 |
+
|
541 |
+
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
542 |
+
"""
|
543 |
+
1. Put the provided model to cpu
|
544 |
+
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
545 |
+
3. Load it into the provided model
|
546 |
+
|
547 |
+
Args:
|
548 |
+
- ``model``: the model object to update
|
549 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
550 |
+
- ``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``
|
551 |
+
|
552 |
+
Returns:
|
553 |
+
- ``model`: modified model
|
554 |
+
|
555 |
+
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
556 |
+
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
557 |
+
conveniently placed for you in the checkpoint folder.
|
558 |
+
|
559 |
+
A typical usage might be ::
|
560 |
+
|
561 |
+
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
562 |
+
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
563 |
+
# submit to model hub or save the model to share with others
|
564 |
+
|
565 |
+
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
566 |
+
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
567 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
568 |
+
|
569 |
+
"""
|
570 |
+
logger.info(f"Extracting fp32 weights")
|
571 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
572 |
+
|
573 |
+
logger.info(f"Overwriting model with fp32 weights")
|
574 |
+
model = model.cpu()
|
575 |
+
model.load_state_dict(state_dict, strict=False)
|
576 |
+
|
577 |
+
return model
|
578 |
+
|
579 |
+
|
580 |
+
if __name__ == "__main__":
|
581 |
+
|
582 |
+
parser = argparse.ArgumentParser()
|
583 |
+
parser.add_argument("checkpoint_dir",
|
584 |
+
type=str,
|
585 |
+
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
586 |
+
parser.add_argument(
|
587 |
+
"output_file",
|
588 |
+
type=str,
|
589 |
+
help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
|
590 |
+
parser.add_argument("-t",
|
591 |
+
"--tag",
|
592 |
+
type=str,
|
593 |
+
default=None,
|
594 |
+
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
595 |
+
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
596 |
+
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
597 |
+
args = parser.parse_args()
|
598 |
+
|
599 |
+
debug = args.debug
|
600 |
+
|
601 |
+
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
602 |
+
args.output_file,
|
603 |
+
tag=args.tag,
|
604 |
+
exclude_frozen_parameters=args.exclude_frozen_parameters)
|
checkpoint-565/added_tokens.json
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"<|endoftext|>": 151643,
|
3 |
+
"<|im_end|>": 151645,
|
4 |
+
"<|im_start|>": 151644
|
5 |
+
}
|
checkpoint-565/config.json
ADDED
@@ -0,0 +1,79 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/gpfs/public/research/junpeng/checkpoints/0919_llavanext_siglip_qwen2",
|
3 |
+
"add_faster_video": false,
|
4 |
+
"add_time_instruction": false,
|
5 |
+
"architectures": [
|
6 |
+
"LlavaQwenForCausalLM"
|
7 |
+
],
|
8 |
+
"attention_dropout": 0.0,
|
9 |
+
"bos_token_id": 151643,
|
10 |
+
"eos_token_id": 151645,
|
11 |
+
"faster_token_stride": 10,
|
12 |
+
"force_sample": false,
|
13 |
+
"hidden_act": "silu",
|
14 |
+
"hidden_size": 3584,
|
15 |
+
"image_aspect_ratio": "anyres",
|
16 |
+
"image_crop_resolution": null,
|
17 |
+
"image_grid_pinpoints": [
|
18 |
+
[
|
19 |
+
384,
|
20 |
+
768
|
21 |
+
],
|
22 |
+
[
|
23 |
+
768,
|
24 |
+
384
|
25 |
+
],
|
26 |
+
[
|
27 |
+
768,
|
28 |
+
768
|
29 |
+
],
|
30 |
+
[
|
31 |
+
1152,
|
32 |
+
384
|
33 |
+
],
|
34 |
+
[
|
35 |
+
384,
|
36 |
+
1152
|
37 |
+
]
|
38 |
+
],
|
39 |
+
"image_split_resolution": null,
|
40 |
+
"initializer_range": 0.02,
|
41 |
+
"intermediate_size": 18944,
|
42 |
+
"max_position_embeddings": 32768,
|
43 |
+
"max_window_layers": 28,
|
44 |
+
"mm_hidden_size": 1152,
|
45 |
+
"mm_newline_position": "grid",
|
46 |
+
"mm_patch_merge_type": "spatial_unpad",
|
47 |
+
"mm_projector_lr": null,
|
48 |
+
"mm_projector_type": "mlp2x_gelu",
|
49 |
+
"mm_resampler_type": null,
|
50 |
+
"mm_spatial_pool_mode": "bilinear",
|
51 |
+
"mm_spatial_pool_stride": null,
|
52 |
+
"mm_tunable_parts": "mm_vision_tower,mm_mlp_adapter,mm_language_model",
|
53 |
+
"mm_use_im_patch_token": false,
|
54 |
+
"mm_use_im_start_end": false,
|
55 |
+
"mm_vision_select_feature": "patch",
|
56 |
+
"mm_vision_select_layer": -2,
|
57 |
+
"mm_vision_tower": "google/siglip-so400m-patch14-384",
|
58 |
+
"mm_vision_tower_lr": 2e-06,
|
59 |
+
"model_type": "qwen2",
|
60 |
+
"num_attention_heads": 28,
|
61 |
+
"num_hidden_layers": 28,
|
62 |
+
"num_key_value_heads": 4,
|
63 |
+
"pos_skipping_range": 4096,
|
64 |
+
"rms_norm_eps": 1e-06,
|
65 |
+
"rope_scaling": null,
|
66 |
+
"rope_theta": 1000000.0,
|
67 |
+
"sliding_window": 131072,
|
68 |
+
"tie_word_embeddings": false,
|
69 |
+
"tokenizer_model_max_length": 8192,
|
70 |
+
"tokenizer_padding_side": "right",
|
71 |
+
"torch_dtype": "bfloat16",
|
72 |
+
"transformers_version": "4.41.0",
|
73 |
+
"use_cache": false,
|
74 |
+
"use_mm_proj": true,
|
75 |
+
"use_pos_skipping": false,
|
76 |
+
"use_sliding_window": false,
|
77 |
+
"vision_tower_pretrained": null,
|
78 |
+
"vocab_size": 152064
|
79 |
+
}
|
checkpoint-565/generation_config.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token_id": 151643,
|
3 |
+
"do_sample": true,
|
4 |
+
"eos_token_id": [
|
5 |
+
151645,
|
6 |
+
151643
|
7 |
+
],
|
8 |
+
"pad_token_id": 151643,
|
9 |
+
"repetition_penalty": 1.05,
|
10 |
+
"temperature": 0.7,
|
11 |
+
"top_k": 20,
|
12 |
+
"top_p": 0.8,
|
13 |
+
"transformers_version": "4.41.0"
|
14 |
+
}
|
checkpoint-565/latest
ADDED
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24 |
+
"rstrip": false,
|
25 |
+
"single_word": false,
|
26 |
+
"special": true
|
27 |
+
}
|
28 |
+
},
|
29 |
+
"additional_special_tokens": [
|
30 |
+
"<|im_start|>",
|
31 |
+
"<|im_end|>"
|
32 |
+
],
|
33 |
+
"bos_token": null,
|
34 |
+
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
35 |
+
"clean_up_tokenization_spaces": false,
|
36 |
+
"eos_token": "<|im_end|>",
|
37 |
+
"errors": "replace",
|
38 |
+
"model_max_length": 8192,
|
39 |
+
"pad_token": "<|endoftext|>",
|
40 |
+
"padding_side": "right",
|
41 |
+
"split_special_tokens": false,
|
42 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
43 |
+
"unk_token": null
|
44 |
+
}
|
checkpoint-565/trainer_state.json
ADDED
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|
|