初始化项目，由ModelHub XC社区提供模型
Model: Aurore-Reveil/Koto-Small-7B-IT Source: Original Platform
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---
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license: mit
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language:
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- en
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base_model:
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- allura-org/Koto-Small-7B-PT
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library_name: transformers
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tags:
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- writing
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- creative-writing
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- roleplay
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---
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# Koto Small 7B (Instruct-Tuned)
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Koto-Small-7B-IT is an instruct-tuned version of [Koto-Small-7B-PT](https://huggingface.co/allura-org/Koto-Small-7B-PT), which was trained on MiMo-7B-Base for almost a billion tokens of creative-writing data. This model is meant for roleplaying and instruct usecases.
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## Usage
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### Chat template
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Trained with ChatML formatting, A typical input would look like this:
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```
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<|im_start|>system
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system prompt<|im_end|>
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<|im_start|>user
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Hi there!<|im_end|>
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<|im_start|>assistant
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Nice to meet you!<|im_end|>
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<|im_start|>user
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Can I ask a question?<|im_end|>
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<|im_start|>assistant
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```
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## Samplers
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We found that 1.25 temperature and 0.05 min_p worked best, but YMMV!
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## Datasets
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```yaml
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datasets:
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- path: Delta-Vector/Hydrus-General-Reasoning
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- path: Delta-Vector/Hydrus-IF-Mix-Ai2
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- path: Delta-Vector/Hydrus-Army-Inst
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- path: Delta-Vector/Hydrus-AM-thinking-Science
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- path: Delta-Vector/Hydrus-AM-Thinking-Code-Filtered
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- path: Delta-Vector/Hydrus-AM-Thinking-IF-No-Think
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- path: Delta-Vector/Hydrus-Tulu-SFT-Mix-V2
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- path: Delta-Vector/Hydrus-System-Chat-2.0
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- path: Delta-Vector/Orion-Praxis-Co-Writer
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- path: Delta-Vector/Orion-Co-Writer-51K
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- path: Delta-Vector/Orion-Creative_Writing-Complexity
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- path: Delta-Vector/Orion-vanilla-backrooms-claude-sharegpt
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- path: Delta-Vector/Hydrus-AM-Thinking-Multi-Turn
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- path: PocketDoc/Dans-Failuremaxx-Adventure
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- path: PocketDoc/Dans-Logicmaxx-SAT-AP
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- path: PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
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- path: PocketDoc/Dans-Taskmaxx-DataPrepper
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- path: PocketDoc/Dans-Prosemaxx-Instructwriter-Long
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- path: PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2
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- path: PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3
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- path: PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2
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- path: PocketDoc/Dans-Systemmaxx
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```
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## Acknowledgements
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- Thank you very much to [Delta-Vector](https://huggingface.co/Delta-Vector)/[Mango](https://x.com/MangoSweet78) for providing the compute used to train this model.
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- Fizz for the pretrain.
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- Pocketdoc/Anthracite for da cool datasets.
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- Hensen chat.
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- Thank you to the illustrator of WataNare for drawing the art used in the model card!
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- Thanks to Curse for testing, ideas.
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- Thanks to Toasty for some data, ideas.
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- Thanks to everyone else in allura!
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ilya <3
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## Call for Help
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If you would like to help build on this model (RP SFT, further annealing on higher quality data, etc)...
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Please join [the allura discord](https://discord.gg/PPBMhF2vgC) or [the matrix](https://matrix.to/#/#allura:allura.moe)! <3
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## Technical Appendix
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<details>
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### Training Notes
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Same as before, It was trained over the course of 12 hours for over 2 epochs, on an 8xA100 DGX node, Using Ademamix and REX LR schedular, High grad-clipping was used for regularization with NO WEIGHTDECAY because it sucks.
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### [WandB](https://wandb.ai/new-eden/Koto-Small/runs/fgln5fjh?nw=nwuserdeltavector)
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### Axolotl Config
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```yaml
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# =============================================================================
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# Model + Saving
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# =============================================================================
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base_model: allura-forge/Koto-Small-7b-rc1
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output_dir: ./koto-sft
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saves_per_epoch: 2
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deepcompile: true
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# =============================================================================
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# DATASET CONFIGURATION
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# =============================================================================
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datasets:
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- path: /home/Ubuntu/Mango/pretok/test-koto-sft-7b-rc-1.parquet
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ds_type: parquet
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type:
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shuffle_merged_datasets: true
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dataset_prepared_path: ./dataset_prepared
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train_on_inputs: false
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# =============================================================================
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# EVALUATION SETTINGS
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# =============================================================================
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#evals_per_epoch: 4
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#eval_table_size:
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#eval_max_new_tokens: 128
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#eval_sample_packing: false
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val_set_size: 0.0
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# =============================================================================
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# MEMORY OPTIMIZATION
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# =============================================================================
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_layer_norm: true
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liger_glu_activation: true
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liger_fused_linear_cross_entropy: false
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cut_cross_entropy: true
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sample_packing: true
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pad_to_sequence_len: true
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gradient_checkpointing: true
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flash_attention: true
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# =============================================================================
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# MULTI-GPU TRAINING
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# =============================================================================
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deepspeed: ./deepspeed_configs/zero2.json
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# =============================================================================
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# LOGGING & MONITORING
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# =============================================================================
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wandb_project: Koto-Small
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wandb_entity:
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wandb_watch:
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wandb_name: sft
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wandb_log_model:
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logging_steps: 1
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debug: false
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# =============================================================================
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# TRAINING PARAMETERS
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# =============================================================================
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micro_batch_size: 6
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gradient_accumulation_steps: 2
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num_epochs: 2
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sequence_len: 16000
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optimizer: paged_ademamix_8bit
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lr_scheduler: rex
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learning_rate: 8e-6
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warmup_ratio: 0.1
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max_grad_norm: 0.0001
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weight_decay: 0.0
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# =============================================================================
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# ADDITIONAL SETTINGS
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# =============================================================================
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local_rank:
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group_by_length: false
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early_stopping_patience:
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save_safetensors: true
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bf16: auto
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special_tokens:
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```
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</details>
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.0,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 32768,
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"max_window_layers": 32,
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"model_type": "qwen2",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 640000,
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"sliding_window": 32768,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": false,
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"use_mrope": false,
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"use_sliding_window": false,
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"vocab_size": 151680
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}
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"max_new_tokens": 2048,
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"transformers_version": "4.51.3"
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}
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||||||
3
rng_state_0.pth
Normal file
3
rng_state_0.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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oid sha256:8bfe1981024ef92f2da08a90c72c7c793d1cc9de1547abd2556c968be70232eb
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size 16389
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||||||
3
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Normal file
3
rng_state_1.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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|
oid sha256:a35b845d476d830805793c3dcf8ac2daad87fec289bff3f7eda9e72fc374eda1
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||||||
|
size 16389
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3
rng_state_2.pth
Normal file
3
rng_state_2.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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|
oid sha256:03e9880996b01262a807d1ec3ebd91eee540e08130a14a45a4648731fd0d48a9
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|
size 16389
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3
rng_state_3.pth
Normal file
3
rng_state_3.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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|
oid sha256:ee25c237d6fe62ec76adcf7daf899d7ed32eab5d1a5b447b911f4451c9a1b258
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||||||
|
size 16389
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3
rng_state_4.pth
Normal file
3
rng_state_4.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:7a6b31133f29a8fc0cb538aa807d6a403bd51939336bfd425cd3d122d8c5595c
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||||||
|
size 16389
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3
rng_state_5.pth
Normal file
3
rng_state_5.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:a26c55b5c7fa0522b1d27b2c00a7ea77ad010f19a1321991165c5c972b8fa97a
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||||||
|
size 16389
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3
rng_state_6.pth
Normal file
3
rng_state_6.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:4a1a3cf85626196804f25a8293e22dc561bba068a70fb123e04afe4896c33972
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||||||
|
size 16389
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3
rng_state_7.pth
Normal file
3
rng_state_7.pth
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:28f87c1ee5f5db346c7b913137cbccd196eaf8ec5a4cf9f192418a3069269b49
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||||||
|
size 16389
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||||||
3
scheduler.pt
Normal file
3
scheduler.pt
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:21ea0cd99e8e7440d46bdba046928d1ffca8c408e33ddcf64818c009258acf27
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||||||
|
size 1465
|
||||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
|||||||
|
{
|
||||||
|
"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|>"
|
||||||
|
],
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
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|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
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|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||||
|
size 11421896
|
||||||
208
tokenizer_config.json
Normal file
208
tokenizer_config.json
Normal file
@@ -0,0 +1,208 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": false,
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
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|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
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|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
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|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|object_ref_start|>",
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151648": {
|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151650": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151651": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151655": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"151661": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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|>"
|
||||||
|
],
|
||||||
|
"bos_token": null,
|
||||||
|
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": {},
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
5186
trainer_state.json
Normal file
5186
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:8ddc3b29ba35dcf1aef3983ad011f747681e3870159f5ad20a8d2d53e88486ff
|
||||||
|
size 8785
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
674
zero_to_fp32.py
Normal file
674
zero_to_fp32.py
Normal file
@@ -0,0 +1,674 @@
|
|||||||
|
#!/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 json
|
||||||
|
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)
|
||||||
|
|
||||||
|
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 files:
|
||||||
|
state_dict = torch.load(f, map_location=device)
|
||||||
|
# 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 not ZERO_STAGE 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}")
|
||||||
|
|
||||||
|
if zero_stage <= 2:
|
||||||
|
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
||||||
|
elif zero_stage == 3:
|
||||||
|
# if there is more than one param group, there will be multiple flattened tensors - one
|
||||||
|
# flattened tensor per group - for simplicity merge them into a single tensor
|
||||||
|
#
|
||||||
|
# XXX: could make the script more memory efficient for when there are multiple groups - it
|
||||||
|
# will require matching the sub-lists of param_shapes for each param group flattened tensor
|
||||||
|
|
||||||
|
fp32_flat_groups = [
|
||||||
|
torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) 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")
|
||||||
|
|
||||||
|
|
||||||
|
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 = fp32_flat_groups[0].numel() * 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
|
||||||
|
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}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# XXX: memory usage doubles here
|
||||||
|
state_dict[name] = torch.cat(
|
||||||
|
tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
|
||||||
|
0).narrow(0, 0, unpartitioned_numel).view(shape)
|
||||||
|
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 get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=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
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
- pytorch ``state_dict``
|
||||||
|
|
||||||
|
Note: this approach may not work if your application doesn't have sufficient free CPU memory and
|
||||||
|
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
||||||
|
the checkpoint.
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
|
"""
|
||||||
|
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")
|
||||||
|
|
||||||
|
return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
||||||
|
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
# 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")
|
||||||
|
state_dict_split = split_torch_state_dict_into_shards(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
|
||||||
|
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
||||||
|
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
||||||
|
shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
|
||||||
|
output_path = os.path.join(output_dir, shard_file)
|
||||||
|
if safe_serialization:
|
||||||
|
save_file(shard, output_path, metadata={"format": "pt"})
|
||||||
|
else:
|
||||||
|
torch.save(shard, output_path)
|
||||||
|
|
||||||
|
# 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(f"Extracting fp32 weights")
|
||||||
|
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
||||||
|
|
||||||
|
logger.info(f"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)
|
||||||
Reference in New Issue
Block a user