Training in progress, step 2000, checkpoint
Browse files- checkpoint-2000/README.md +202 -0
- checkpoint-2000/adapter_config.json +32 -0
- checkpoint-2000/adapter_model.safetensors +3 -0
- checkpoint-2000/global_step4000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt +3 -0
- checkpoint-2000/global_step4000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt +3 -0
- checkpoint-2000/global_step4000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt +3 -0
- checkpoint-2000/global_step4000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt +3 -0
- checkpoint-2000/global_step4000/zero_pp_rank_0_mp_rank_00_model_states.pt +3 -0
- checkpoint-2000/global_step4000/zero_pp_rank_1_mp_rank_00_model_states.pt +3 -0
- checkpoint-2000/global_step4000/zero_pp_rank_2_mp_rank_00_model_states.pt +3 -0
- checkpoint-2000/global_step4000/zero_pp_rank_3_mp_rank_00_model_states.pt +3 -0
- checkpoint-2000/latest +1 -0
- checkpoint-2000/rng_state_0.pth +3 -0
- checkpoint-2000/rng_state_1.pth +3 -0
- checkpoint-2000/rng_state_2.pth +3 -0
- checkpoint-2000/rng_state_3.pth +3 -0
- checkpoint-2000/scheduler.pt +3 -0
- checkpoint-2000/special_tokens_map.json +17 -0
- checkpoint-2000/tokenizer.json +3 -0
- checkpoint-2000/tokenizer_config.json +2064 -0
- checkpoint-2000/trainer_state.json +0 -0
- checkpoint-2000/training_args.bin +3 -0
- checkpoint-2000/zero_to_fp32.py +758 -0
checkpoint-2000/README.md
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---
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base_model: meta-llama/Llama-3.1-8B-Instruct
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library_name: peft
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.14.0
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checkpoint-2000/adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
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"bias": "none",
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0.1,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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checkpoint-2000/adapter_model.safetensors
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checkpoint-2000/global_step4000/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
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checkpoint-2000/global_step4000/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt
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checkpoint-2000/global_step4000/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt
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checkpoint-2000/global_step4000/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt
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checkpoint-2000/global_step4000/zero_pp_rank_0_mp_rank_00_model_states.pt
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checkpoint-2000/global_step4000/zero_pp_rank_1_mp_rank_00_model_states.pt
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checkpoint-2000/global_step4000/zero_pp_rank_2_mp_rank_00_model_states.pt
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checkpoint-2000/global_step4000/zero_pp_rank_3_mp_rank_00_model_states.pt
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checkpoint-2000/latest
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global_step4000
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checkpoint-2000/rng_state_0.pth
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checkpoint-2000/rng_state_2.pth
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checkpoint-2000/rng_state_3.pth
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:38644c1be5962e4804c3e4e3bffba9c4c8238ad343b6633723f29f8b1eecad42
|
3 |
+
size 15024
|
checkpoint-2000/scheduler.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:5342979256b4fb06149467eb846293fa313ed9fad3c32e87e14129dd95f9df7b
|
3 |
+
size 1064
|
checkpoint-2000/special_tokens_map.json
ADDED
@@ -0,0 +1,17 @@
|
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|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "<|begin_of_text|>",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"eos_token": {
|
10 |
+
"content": "<|eot_id|>",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": "<|eot_id|>"
|
17 |
+
}
|
checkpoint-2000/tokenizer.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:d6c3a4e78b7bdd316840cd4f42a7612f5557899de2aeff6f82828176e8bb90bf
|
3 |
+
size 17210096
|
checkpoint-2000/tokenizer_config.json
ADDED
@@ -0,0 +1,2064 @@
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|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"128000": {
|
4 |
+
"content": "<|begin_of_text|>",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"128001": {
|
12 |
+
"content": "<|end_of_text|>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"128002": {
|
20 |
+
"content": "<|reserved_special_token_0|>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"128003": {
|
28 |
+
"content": "<|reserved_special_token_1|>",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"128004": {
|
36 |
+
"content": "<|finetune_right_pad_id|>",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
},
|
43 |
+
"128005": {
|
44 |
+
"content": "<|reserved_special_token_2|>",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": false,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"128006": {
|
52 |
+
"content": "<|start_header_id|>",
|
53 |
+
"lstrip": false,
|
54 |
+
"normalized": false,
|
55 |
+
"rstrip": false,
|
56 |
+
"single_word": false,
|
57 |
+
"special": true
|
58 |
+
},
|
59 |
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"128007": {
|
60 |
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"content": "<|end_header_id|>",
|
61 |
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"lstrip": false,
|
62 |
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"normalized": false,
|
63 |
+
"rstrip": false,
|
64 |
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"single_word": false,
|
65 |
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"special": true
|
66 |
+
},
|
67 |
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"128008": {
|
68 |
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"content": "<|eom_id|>",
|
69 |
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"lstrip": false,
|
70 |
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"normalized": false,
|
71 |
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"rstrip": false,
|
72 |
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"single_word": false,
|
73 |
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"special": true
|
74 |
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},
|
75 |
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"128009": {
|
76 |
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"content": "<|eot_id|>",
|
77 |
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"lstrip": false,
|
78 |
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"normalized": false,
|
79 |
+
"rstrip": false,
|
80 |
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"single_word": false,
|
81 |
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"special": true
|
82 |
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},
|
83 |
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"128010": {
|
84 |
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"content": "<|python_tag|>",
|
85 |
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"lstrip": false,
|
86 |
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"normalized": false,
|
87 |
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"rstrip": false,
|
88 |
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"single_word": false,
|
89 |
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"special": true
|
90 |
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},
|
91 |
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"128011": {
|
92 |
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"content": "<|reserved_special_token_3|>",
|
93 |
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"lstrip": false,
|
94 |
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"normalized": false,
|
95 |
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"rstrip": false,
|
96 |
+
"single_word": false,
|
97 |
+
"special": true
|
98 |
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},
|
99 |
+
"128012": {
|
100 |
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"content": "<|reserved_special_token_4|>",
|
101 |
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"lstrip": false,
|
102 |
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"normalized": false,
|
103 |
+
"rstrip": false,
|
104 |
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"single_word": false,
|
105 |
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"special": true
|
106 |
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},
|
107 |
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"128013": {
|
108 |
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"content": "<|reserved_special_token_5|>",
|
109 |
+
"lstrip": false,
|
110 |
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"normalized": false,
|
111 |
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"rstrip": false,
|
112 |
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"single_word": false,
|
113 |
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"special": true
|
114 |
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},
|
115 |
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"128014": {
|
116 |
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"content": "<|reserved_special_token_6|>",
|
117 |
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"lstrip": false,
|
118 |
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"normalized": false,
|
119 |
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"rstrip": false,
|
120 |
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|
121 |
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|
122 |
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},
|
123 |
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"128015": {
|
124 |
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"content": "<|reserved_special_token_7|>",
|
125 |
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"lstrip": false,
|
126 |
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"normalized": false,
|
127 |
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"rstrip": false,
|
128 |
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"single_word": false,
|
129 |
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"special": true
|
130 |
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},
|
131 |
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"128016": {
|
132 |
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"content": "<|reserved_special_token_8|>",
|
133 |
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"lstrip": false,
|
134 |
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"normalized": false,
|
135 |
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|
136 |
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|
137 |
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"special": true
|
138 |
+
},
|
139 |
+
"128017": {
|
140 |
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"content": "<|reserved_special_token_9|>",
|
141 |
+
"lstrip": false,
|
142 |
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"normalized": false,
|
143 |
+
"rstrip": false,
|
144 |
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"single_word": false,
|
145 |
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"special": true
|
146 |
+
},
|
147 |
+
"128018": {
|
148 |
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"content": "<|reserved_special_token_10|>",
|
149 |
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"lstrip": false,
|
150 |
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"normalized": false,
|
151 |
+
"rstrip": false,
|
152 |
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"single_word": false,
|
153 |
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"special": true
|
154 |
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},
|
155 |
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"128019": {
|
156 |
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"content": "<|reserved_special_token_11|>",
|
157 |
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"lstrip": false,
|
158 |
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"normalized": false,
|
159 |
+
"rstrip": false,
|
160 |
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"single_word": false,
|
161 |
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"special": true
|
162 |
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},
|
163 |
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"128020": {
|
164 |
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"content": "<|reserved_special_token_12|>",
|
165 |
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"lstrip": false,
|
166 |
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"normalized": false,
|
167 |
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"rstrip": false,
|
168 |
+
"single_word": false,
|
169 |
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"special": true
|
170 |
+
},
|
171 |
+
"128021": {
|
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1916 |
+
"content": "<|reserved_special_token_231|>",
|
1917 |
+
"lstrip": false,
|
1918 |
+
"normalized": false,
|
1919 |
+
"rstrip": false,
|
1920 |
+
"single_word": false,
|
1921 |
+
"special": true
|
1922 |
+
},
|
1923 |
+
"128240": {
|
1924 |
+
"content": "<|reserved_special_token_232|>",
|
1925 |
+
"lstrip": false,
|
1926 |
+
"normalized": false,
|
1927 |
+
"rstrip": false,
|
1928 |
+
"single_word": false,
|
1929 |
+
"special": true
|
1930 |
+
},
|
1931 |
+
"128241": {
|
1932 |
+
"content": "<|reserved_special_token_233|>",
|
1933 |
+
"lstrip": false,
|
1934 |
+
"normalized": false,
|
1935 |
+
"rstrip": false,
|
1936 |
+
"single_word": false,
|
1937 |
+
"special": true
|
1938 |
+
},
|
1939 |
+
"128242": {
|
1940 |
+
"content": "<|reserved_special_token_234|>",
|
1941 |
+
"lstrip": false,
|
1942 |
+
"normalized": false,
|
1943 |
+
"rstrip": false,
|
1944 |
+
"single_word": false,
|
1945 |
+
"special": true
|
1946 |
+
},
|
1947 |
+
"128243": {
|
1948 |
+
"content": "<|reserved_special_token_235|>",
|
1949 |
+
"lstrip": false,
|
1950 |
+
"normalized": false,
|
1951 |
+
"rstrip": false,
|
1952 |
+
"single_word": false,
|
1953 |
+
"special": true
|
1954 |
+
},
|
1955 |
+
"128244": {
|
1956 |
+
"content": "<|reserved_special_token_236|>",
|
1957 |
+
"lstrip": false,
|
1958 |
+
"normalized": false,
|
1959 |
+
"rstrip": false,
|
1960 |
+
"single_word": false,
|
1961 |
+
"special": true
|
1962 |
+
},
|
1963 |
+
"128245": {
|
1964 |
+
"content": "<|reserved_special_token_237|>",
|
1965 |
+
"lstrip": false,
|
1966 |
+
"normalized": false,
|
1967 |
+
"rstrip": false,
|
1968 |
+
"single_word": false,
|
1969 |
+
"special": true
|
1970 |
+
},
|
1971 |
+
"128246": {
|
1972 |
+
"content": "<|reserved_special_token_238|>",
|
1973 |
+
"lstrip": false,
|
1974 |
+
"normalized": false,
|
1975 |
+
"rstrip": false,
|
1976 |
+
"single_word": false,
|
1977 |
+
"special": true
|
1978 |
+
},
|
1979 |
+
"128247": {
|
1980 |
+
"content": "<|reserved_special_token_239|>",
|
1981 |
+
"lstrip": false,
|
1982 |
+
"normalized": false,
|
1983 |
+
"rstrip": false,
|
1984 |
+
"single_word": false,
|
1985 |
+
"special": true
|
1986 |
+
},
|
1987 |
+
"128248": {
|
1988 |
+
"content": "<|reserved_special_token_240|>",
|
1989 |
+
"lstrip": false,
|
1990 |
+
"normalized": false,
|
1991 |
+
"rstrip": false,
|
1992 |
+
"single_word": false,
|
1993 |
+
"special": true
|
1994 |
+
},
|
1995 |
+
"128249": {
|
1996 |
+
"content": "<|reserved_special_token_241|>",
|
1997 |
+
"lstrip": false,
|
1998 |
+
"normalized": false,
|
1999 |
+
"rstrip": false,
|
2000 |
+
"single_word": false,
|
2001 |
+
"special": true
|
2002 |
+
},
|
2003 |
+
"128250": {
|
2004 |
+
"content": "<|reserved_special_token_242|>",
|
2005 |
+
"lstrip": false,
|
2006 |
+
"normalized": false,
|
2007 |
+
"rstrip": false,
|
2008 |
+
"single_word": false,
|
2009 |
+
"special": true
|
2010 |
+
},
|
2011 |
+
"128251": {
|
2012 |
+
"content": "<|reserved_special_token_243|>",
|
2013 |
+
"lstrip": false,
|
2014 |
+
"normalized": false,
|
2015 |
+
"rstrip": false,
|
2016 |
+
"single_word": false,
|
2017 |
+
"special": true
|
2018 |
+
},
|
2019 |
+
"128252": {
|
2020 |
+
"content": "<|reserved_special_token_244|>",
|
2021 |
+
"lstrip": false,
|
2022 |
+
"normalized": false,
|
2023 |
+
"rstrip": false,
|
2024 |
+
"single_word": false,
|
2025 |
+
"special": true
|
2026 |
+
},
|
2027 |
+
"128253": {
|
2028 |
+
"content": "<|reserved_special_token_245|>",
|
2029 |
+
"lstrip": false,
|
2030 |
+
"normalized": false,
|
2031 |
+
"rstrip": false,
|
2032 |
+
"single_word": false,
|
2033 |
+
"special": true
|
2034 |
+
},
|
2035 |
+
"128254": {
|
2036 |
+
"content": "<|reserved_special_token_246|>",
|
2037 |
+
"lstrip": false,
|
2038 |
+
"normalized": false,
|
2039 |
+
"rstrip": false,
|
2040 |
+
"single_word": false,
|
2041 |
+
"special": true
|
2042 |
+
},
|
2043 |
+
"128255": {
|
2044 |
+
"content": "<|reserved_special_token_247|>",
|
2045 |
+
"lstrip": false,
|
2046 |
+
"normalized": false,
|
2047 |
+
"rstrip": false,
|
2048 |
+
"single_word": false,
|
2049 |
+
"special": true
|
2050 |
+
}
|
2051 |
+
},
|
2052 |
+
"bos_token": "<|begin_of_text|>",
|
2053 |
+
"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
|
2054 |
+
"clean_up_tokenization_spaces": true,
|
2055 |
+
"eos_token": "<|eot_id|>",
|
2056 |
+
"extra_special_tokens": {},
|
2057 |
+
"model_input_names": [
|
2058 |
+
"input_ids",
|
2059 |
+
"attention_mask"
|
2060 |
+
],
|
2061 |
+
"model_max_length": 131072,
|
2062 |
+
"pad_token": "<|eot_id|>",
|
2063 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
2064 |
+
}
|
checkpoint-2000/trainer_state.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
checkpoint-2000/training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e5cb48f33243cb7dd98135bb8562378245f5c1a682e4b0c528e7e55022950457
|
3 |
+
size 7224
|
checkpoint-2000/zero_to_fp32.py
ADDED
@@ -0,0 +1,758 @@
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|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
# Copyright (c) Microsoft Corporation.
|
4 |
+
# SPDX-License-Identifier: Apache-2.0
|
5 |
+
|
6 |
+
# DeepSpeed Team
|
7 |
+
|
8 |
+
# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
|
9 |
+
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
10 |
+
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
11 |
+
# application.
|
12 |
+
#
|
13 |
+
# example:
|
14 |
+
# python zero_to_fp32.py . output_dir/
|
15 |
+
# or
|
16 |
+
# python zero_to_fp32.py . output_dir/ --safe_serialization
|
17 |
+
|
18 |
+
import argparse
|
19 |
+
import torch
|
20 |
+
import glob
|
21 |
+
import math
|
22 |
+
import os
|
23 |
+
import re
|
24 |
+
import gc
|
25 |
+
import json
|
26 |
+
import numpy as np
|
27 |
+
from tqdm import tqdm
|
28 |
+
from collections import OrderedDict
|
29 |
+
from dataclasses import dataclass
|
30 |
+
|
31 |
+
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
32 |
+
# DeepSpeed data structures it has to be available in the current python environment.
|
33 |
+
from deepspeed.utils import logger
|
34 |
+
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
35 |
+
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
36 |
+
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
37 |
+
|
38 |
+
|
39 |
+
@dataclass
|
40 |
+
class zero_model_state:
|
41 |
+
buffers: dict()
|
42 |
+
param_shapes: dict()
|
43 |
+
shared_params: list
|
44 |
+
ds_version: int
|
45 |
+
frozen_param_shapes: dict()
|
46 |
+
frozen_param_fragments: dict()
|
47 |
+
|
48 |
+
|
49 |
+
debug = 0
|
50 |
+
|
51 |
+
# load to cpu
|
52 |
+
device = torch.device('cpu')
|
53 |
+
|
54 |
+
|
55 |
+
def atoi(text):
|
56 |
+
return int(text) if text.isdigit() else text
|
57 |
+
|
58 |
+
|
59 |
+
def natural_keys(text):
|
60 |
+
'''
|
61 |
+
alist.sort(key=natural_keys) sorts in human order
|
62 |
+
http://nedbatchelder.com/blog/200712/human_sorting.html
|
63 |
+
(See Toothy's implementation in the comments)
|
64 |
+
'''
|
65 |
+
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
66 |
+
|
67 |
+
|
68 |
+
def get_model_state_file(checkpoint_dir, zero_stage):
|
69 |
+
if not os.path.isdir(checkpoint_dir):
|
70 |
+
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
71 |
+
|
72 |
+
# there should be only one file
|
73 |
+
if zero_stage <= 2:
|
74 |
+
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
75 |
+
elif zero_stage == 3:
|
76 |
+
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
77 |
+
|
78 |
+
if not os.path.exists(file):
|
79 |
+
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
80 |
+
|
81 |
+
return file
|
82 |
+
|
83 |
+
|
84 |
+
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
85 |
+
# XXX: need to test that this simple glob rule works for multi-node setup too
|
86 |
+
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
87 |
+
|
88 |
+
if len(ckpt_files) == 0:
|
89 |
+
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
90 |
+
|
91 |
+
return ckpt_files
|
92 |
+
|
93 |
+
|
94 |
+
def get_optim_files(checkpoint_dir):
|
95 |
+
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
96 |
+
|
97 |
+
|
98 |
+
def get_model_state_files(checkpoint_dir):
|
99 |
+
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
100 |
+
|
101 |
+
|
102 |
+
def parse_model_states(files):
|
103 |
+
zero_model_states = []
|
104 |
+
for file in files:
|
105 |
+
state_dict = torch.load(file, map_location=device, weights_only=False)
|
106 |
+
|
107 |
+
if BUFFER_NAMES not in state_dict:
|
108 |
+
raise ValueError(f"{file} is not a model state checkpoint")
|
109 |
+
buffer_names = state_dict[BUFFER_NAMES]
|
110 |
+
if debug:
|
111 |
+
print("Found buffers:", buffer_names)
|
112 |
+
|
113 |
+
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
114 |
+
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
115 |
+
param_shapes = state_dict[PARAM_SHAPES]
|
116 |
+
|
117 |
+
# collect parameters that are included in param_shapes
|
118 |
+
param_names = []
|
119 |
+
for s in param_shapes:
|
120 |
+
for name in s.keys():
|
121 |
+
param_names.append(name)
|
122 |
+
|
123 |
+
# update with frozen parameters
|
124 |
+
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
125 |
+
if frozen_param_shapes is not None:
|
126 |
+
if debug:
|
127 |
+
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
128 |
+
param_names += list(frozen_param_shapes.keys())
|
129 |
+
|
130 |
+
# handle shared params
|
131 |
+
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
132 |
+
|
133 |
+
ds_version = state_dict.get(DS_VERSION, None)
|
134 |
+
|
135 |
+
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
136 |
+
|
137 |
+
z_model_state = zero_model_state(buffers=buffers,
|
138 |
+
param_shapes=param_shapes,
|
139 |
+
shared_params=shared_params,
|
140 |
+
ds_version=ds_version,
|
141 |
+
frozen_param_shapes=frozen_param_shapes,
|
142 |
+
frozen_param_fragments=frozen_param_fragments)
|
143 |
+
zero_model_states.append(z_model_state)
|
144 |
+
|
145 |
+
return zero_model_states
|
146 |
+
|
147 |
+
|
148 |
+
def parse_optim_states(files, ds_checkpoint_dir):
|
149 |
+
total_files = len(files)
|
150 |
+
state_dicts = []
|
151 |
+
for f in tqdm(files, desc='Loading checkpoint shards'):
|
152 |
+
state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
|
153 |
+
# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
|
154 |
+
# and also handle the case where it was already removed by another helper script
|
155 |
+
state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
|
156 |
+
state_dicts.append(state_dict)
|
157 |
+
|
158 |
+
if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
159 |
+
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
160 |
+
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
161 |
+
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
162 |
+
|
163 |
+
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
164 |
+
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
165 |
+
# use the max of the partition_count to get the dp world_size.
|
166 |
+
|
167 |
+
if type(world_size) is list:
|
168 |
+
world_size = max(world_size)
|
169 |
+
|
170 |
+
if world_size != total_files:
|
171 |
+
raise ValueError(
|
172 |
+
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
173 |
+
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
174 |
+
)
|
175 |
+
|
176 |
+
# the groups are named differently in each stage
|
177 |
+
if zero_stage <= 2:
|
178 |
+
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
179 |
+
elif zero_stage == 3:
|
180 |
+
fp32_groups_key = FP32_FLAT_GROUPS
|
181 |
+
else:
|
182 |
+
raise ValueError(f"unknown zero stage {zero_stage}")
|
183 |
+
|
184 |
+
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
185 |
+
return zero_stage, world_size, fp32_flat_groups
|
186 |
+
|
187 |
+
|
188 |
+
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
|
189 |
+
"""
|
190 |
+
Returns fp32 state_dict reconstructed from ds checkpoint
|
191 |
+
|
192 |
+
Args:
|
193 |
+
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
194 |
+
|
195 |
+
"""
|
196 |
+
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
197 |
+
|
198 |
+
optim_files = get_optim_files(ds_checkpoint_dir)
|
199 |
+
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
200 |
+
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
201 |
+
|
202 |
+
model_files = get_model_state_files(ds_checkpoint_dir)
|
203 |
+
|
204 |
+
zero_model_states = parse_model_states(model_files)
|
205 |
+
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
206 |
+
|
207 |
+
if zero_stage <= 2:
|
208 |
+
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
209 |
+
exclude_frozen_parameters)
|
210 |
+
elif zero_stage == 3:
|
211 |
+
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
212 |
+
exclude_frozen_parameters)
|
213 |
+
|
214 |
+
|
215 |
+
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
216 |
+
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
217 |
+
return
|
218 |
+
|
219 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
220 |
+
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
221 |
+
|
222 |
+
if debug:
|
223 |
+
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
224 |
+
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
225 |
+
|
226 |
+
wanted_params = len(frozen_param_shapes)
|
227 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
228 |
+
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
229 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
230 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
231 |
+
|
232 |
+
total_params = 0
|
233 |
+
total_numel = 0
|
234 |
+
for name, shape in frozen_param_shapes.items():
|
235 |
+
total_params += 1
|
236 |
+
unpartitioned_numel = shape.numel()
|
237 |
+
total_numel += unpartitioned_numel
|
238 |
+
|
239 |
+
state_dict[name] = frozen_param_fragments[name]
|
240 |
+
|
241 |
+
if debug:
|
242 |
+
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
243 |
+
|
244 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
245 |
+
|
246 |
+
|
247 |
+
def _has_callable(obj, fn):
|
248 |
+
attr = getattr(obj, fn, None)
|
249 |
+
return callable(attr)
|
250 |
+
|
251 |
+
|
252 |
+
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
253 |
+
param_shapes = zero_model_states[0].param_shapes
|
254 |
+
|
255 |
+
# Reconstruction protocol:
|
256 |
+
#
|
257 |
+
# XXX: document this
|
258 |
+
|
259 |
+
if debug:
|
260 |
+
for i in range(world_size):
|
261 |
+
for j in range(len(fp32_flat_groups[0])):
|
262 |
+
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
263 |
+
|
264 |
+
# XXX: memory usage doubles here (zero2)
|
265 |
+
num_param_groups = len(fp32_flat_groups[0])
|
266 |
+
merged_single_partition_of_fp32_groups = []
|
267 |
+
for i in range(num_param_groups):
|
268 |
+
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
269 |
+
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
270 |
+
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
271 |
+
avail_numel = sum(
|
272 |
+
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
273 |
+
|
274 |
+
if debug:
|
275 |
+
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
276 |
+
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
277 |
+
# not asserting if there is a mismatch due to possible padding
|
278 |
+
print(f"Have {avail_numel} numels to process.")
|
279 |
+
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
280 |
+
|
281 |
+
# params
|
282 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
283 |
+
# out-of-core computing solution
|
284 |
+
total_numel = 0
|
285 |
+
total_params = 0
|
286 |
+
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
287 |
+
offset = 0
|
288 |
+
avail_numel = full_single_fp32_vector.numel()
|
289 |
+
for name, shape in shapes.items():
|
290 |
+
|
291 |
+
unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
|
292 |
+
total_numel += unpartitioned_numel
|
293 |
+
total_params += 1
|
294 |
+
|
295 |
+
if debug:
|
296 |
+
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
297 |
+
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
298 |
+
offset += unpartitioned_numel
|
299 |
+
|
300 |
+
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
301 |
+
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
302 |
+
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
303 |
+
# live optimizer object, so we are checking that the numbers are within the right range
|
304 |
+
align_to = 2 * world_size
|
305 |
+
|
306 |
+
def zero2_align(x):
|
307 |
+
return align_to * math.ceil(x / align_to)
|
308 |
+
|
309 |
+
if debug:
|
310 |
+
print(f"original offset={offset}, avail_numel={avail_numel}")
|
311 |
+
|
312 |
+
offset = zero2_align(offset)
|
313 |
+
avail_numel = zero2_align(avail_numel)
|
314 |
+
|
315 |
+
if debug:
|
316 |
+
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
317 |
+
|
318 |
+
# Sanity check
|
319 |
+
if offset != avail_numel:
|
320 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
321 |
+
|
322 |
+
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
323 |
+
|
324 |
+
|
325 |
+
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
326 |
+
exclude_frozen_parameters):
|
327 |
+
state_dict = OrderedDict()
|
328 |
+
|
329 |
+
# buffers
|
330 |
+
buffers = zero_model_states[0].buffers
|
331 |
+
state_dict.update(buffers)
|
332 |
+
if debug:
|
333 |
+
print(f"added {len(buffers)} buffers")
|
334 |
+
|
335 |
+
if not exclude_frozen_parameters:
|
336 |
+
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
337 |
+
|
338 |
+
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
339 |
+
|
340 |
+
# recover shared parameters
|
341 |
+
for pair in zero_model_states[0].shared_params:
|
342 |
+
if pair[1] in state_dict:
|
343 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
344 |
+
|
345 |
+
return state_dict
|
346 |
+
|
347 |
+
|
348 |
+
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
349 |
+
remainder = unpartitioned_numel % world_size
|
350 |
+
padding_numel = (world_size - remainder) if remainder else 0
|
351 |
+
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
352 |
+
return partitioned_numel, padding_numel
|
353 |
+
|
354 |
+
|
355 |
+
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
356 |
+
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
357 |
+
return
|
358 |
+
|
359 |
+
if debug:
|
360 |
+
for i in range(world_size):
|
361 |
+
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
362 |
+
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
363 |
+
|
364 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
365 |
+
wanted_params = len(frozen_param_shapes)
|
366 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
367 |
+
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
368 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
369 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
370 |
+
|
371 |
+
total_params = 0
|
372 |
+
total_numel = 0
|
373 |
+
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
374 |
+
total_params += 1
|
375 |
+
unpartitioned_numel = shape.numel()
|
376 |
+
total_numel += unpartitioned_numel
|
377 |
+
|
378 |
+
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
379 |
+
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
380 |
+
|
381 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
382 |
+
|
383 |
+
if debug:
|
384 |
+
print(
|
385 |
+
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
386 |
+
)
|
387 |
+
|
388 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
389 |
+
|
390 |
+
|
391 |
+
class GatheredTensor:
|
392 |
+
"""
|
393 |
+
A pseudo tensor that collects partitioned weights.
|
394 |
+
It is more memory efficient when there are multiple groups.
|
395 |
+
"""
|
396 |
+
|
397 |
+
def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
|
398 |
+
self.flat_groups = flat_groups
|
399 |
+
self.flat_groups_offset = flat_groups_offset
|
400 |
+
self.offset = offset
|
401 |
+
self.partitioned_numel = partitioned_numel
|
402 |
+
self.shape = shape
|
403 |
+
self.dtype = self.flat_groups[0][0].dtype
|
404 |
+
|
405 |
+
def contiguous(self):
|
406 |
+
"""
|
407 |
+
Merge partitioned weights from flat_groups into a single tensor.
|
408 |
+
"""
|
409 |
+
end_idx = self.offset + self.partitioned_numel
|
410 |
+
world_size = len(self.flat_groups)
|
411 |
+
pad_flat_param_chunks = []
|
412 |
+
|
413 |
+
for rank_i in range(world_size):
|
414 |
+
# for each rank, we need to collect weights from related group/groups
|
415 |
+
flat_groups_at_rank_i = self.flat_groups[rank_i]
|
416 |
+
start_group_id = None
|
417 |
+
end_group_id = None
|
418 |
+
for group_id in range(len(self.flat_groups_offset)):
|
419 |
+
if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
|
420 |
+
start_group_id = group_id
|
421 |
+
if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
|
422 |
+
end_group_id = group_id
|
423 |
+
break
|
424 |
+
# collect weights from related group/groups
|
425 |
+
for group_id in range(start_group_id, end_group_id + 1):
|
426 |
+
flat_tensor = flat_groups_at_rank_i[group_id]
|
427 |
+
start_offset = self.offset - self.flat_groups_offset[group_id]
|
428 |
+
end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
|
429 |
+
pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
|
430 |
+
|
431 |
+
# collect weights from all ranks
|
432 |
+
pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
|
433 |
+
param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
|
434 |
+
return param
|
435 |
+
|
436 |
+
|
437 |
+
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
438 |
+
param_shapes = zero_model_states[0].param_shapes
|
439 |
+
avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
|
440 |
+
|
441 |
+
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
442 |
+
# param, re-consolidating each param, while dealing with padding if any
|
443 |
+
|
444 |
+
# merge list of dicts, preserving order
|
445 |
+
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
446 |
+
|
447 |
+
if debug:
|
448 |
+
for i in range(world_size):
|
449 |
+
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
450 |
+
|
451 |
+
wanted_params = len(param_shapes)
|
452 |
+
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
453 |
+
# not asserting if there is a mismatch due to possible padding
|
454 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
455 |
+
print(f"Trainable params: Have {avail_numel} numels to process.")
|
456 |
+
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
457 |
+
|
458 |
+
# params
|
459 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
460 |
+
# out-of-core computing solution
|
461 |
+
offset = 0
|
462 |
+
total_numel = 0
|
463 |
+
total_params = 0
|
464 |
+
flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
|
465 |
+
for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
|
466 |
+
unpartitioned_numel = shape.numel()
|
467 |
+
total_numel += unpartitioned_numel
|
468 |
+
total_params += 1
|
469 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
470 |
+
|
471 |
+
if debug:
|
472 |
+
print(
|
473 |
+
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
474 |
+
)
|
475 |
+
|
476 |
+
# memory efficient tensor
|
477 |
+
tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
|
478 |
+
state_dict[name] = tensor
|
479 |
+
offset += partitioned_numel
|
480 |
+
|
481 |
+
offset *= world_size
|
482 |
+
|
483 |
+
# Sanity check
|
484 |
+
if offset != avail_numel:
|
485 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
486 |
+
|
487 |
+
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
488 |
+
|
489 |
+
|
490 |
+
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
491 |
+
exclude_frozen_parameters):
|
492 |
+
state_dict = OrderedDict()
|
493 |
+
|
494 |
+
# buffers
|
495 |
+
buffers = zero_model_states[0].buffers
|
496 |
+
state_dict.update(buffers)
|
497 |
+
if debug:
|
498 |
+
print(f"added {len(buffers)} buffers")
|
499 |
+
|
500 |
+
if not exclude_frozen_parameters:
|
501 |
+
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
502 |
+
|
503 |
+
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
504 |
+
|
505 |
+
# recover shared parameters
|
506 |
+
for pair in zero_model_states[0].shared_params:
|
507 |
+
if pair[1] in state_dict:
|
508 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
509 |
+
|
510 |
+
return state_dict
|
511 |
+
|
512 |
+
|
513 |
+
def to_torch_tensor(state_dict, return_empty_tensor=False):
|
514 |
+
"""
|
515 |
+
Convert state_dict of GatheredTensor to torch tensor
|
516 |
+
"""
|
517 |
+
converted_tensors = {}
|
518 |
+
for name, tensor in state_dict.items():
|
519 |
+
tensor_id = id(tensor)
|
520 |
+
if tensor_id in converted_tensors:
|
521 |
+
shared_tensor = state_dict[converted_tensors[tensor_id]]
|
522 |
+
state_dict[name] = shared_tensor
|
523 |
+
else:
|
524 |
+
converted_tensors[tensor_id] = name
|
525 |
+
if return_empty_tensor:
|
526 |
+
state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
|
527 |
+
else:
|
528 |
+
state_dict[name] = tensor.contiguous()
|
529 |
+
return state_dict
|
530 |
+
|
531 |
+
|
532 |
+
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
533 |
+
tag=None,
|
534 |
+
exclude_frozen_parameters=False,
|
535 |
+
lazy_mode=False):
|
536 |
+
"""
|
537 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
538 |
+
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
539 |
+
via a model hub.
|
540 |
+
|
541 |
+
Args:
|
542 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder
|
543 |
+
- ``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``
|
544 |
+
- ``exclude_frozen_parameters``: exclude frozen parameters
|
545 |
+
- ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
|
546 |
+
Convert the pesduo tensor to torch tensor by ``.contiguous()``
|
547 |
+
|
548 |
+
Returns:
|
549 |
+
- pytorch ``state_dict``
|
550 |
+
|
551 |
+
A typical usage might be ::
|
552 |
+
|
553 |
+
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
554 |
+
# do the training and checkpoint saving
|
555 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
556 |
+
model = model.cpu() # move to cpu
|
557 |
+
model.load_state_dict(state_dict)
|
558 |
+
# submit to model hub or save the model to share with others
|
559 |
+
|
560 |
+
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
561 |
+
application. i.e. you will need to re-initialize the deepspeed engine, since
|
562 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
563 |
+
|
564 |
+
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
565 |
+
|
566 |
+
Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
|
567 |
+
You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
568 |
+
the checkpoint. Or you can load state_dict in lazy mode ::
|
569 |
+
|
570 |
+
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
571 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
|
572 |
+
for name, lazy_tensor in state_dict.item():
|
573 |
+
tensor = lazy_tensor.contiguous() # to cpu
|
574 |
+
print(name, tensor)
|
575 |
+
# del tensor to release memory if it no longer in use
|
576 |
+
"""
|
577 |
+
if tag is None:
|
578 |
+
latest_path = os.path.join(checkpoint_dir, 'latest')
|
579 |
+
if os.path.isfile(latest_path):
|
580 |
+
with open(latest_path, 'r') as fd:
|
581 |
+
tag = fd.read().strip()
|
582 |
+
else:
|
583 |
+
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
584 |
+
|
585 |
+
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
586 |
+
|
587 |
+
if not os.path.isdir(ds_checkpoint_dir):
|
588 |
+
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
589 |
+
|
590 |
+
state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
591 |
+
if lazy_mode:
|
592 |
+
return state_dict
|
593 |
+
else:
|
594 |
+
return to_torch_tensor(state_dict)
|
595 |
+
|
596 |
+
|
597 |
+
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
|
598 |
+
output_dir,
|
599 |
+
max_shard_size="5GB",
|
600 |
+
safe_serialization=False,
|
601 |
+
tag=None,
|
602 |
+
exclude_frozen_parameters=False):
|
603 |
+
"""
|
604 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
605 |
+
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
606 |
+
|
607 |
+
Args:
|
608 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
609 |
+
- ``output_dir``: directory to the pytorch fp32 state_dict output files
|
610 |
+
- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
|
611 |
+
- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
612 |
+
- ``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``
|
613 |
+
- ``exclude_frozen_parameters``: exclude frozen parameters
|
614 |
+
"""
|
615 |
+
|
616 |
+
# Dependency pre-check
|
617 |
+
if safe_serialization:
|
618 |
+
try:
|
619 |
+
from safetensors.torch import save_file
|
620 |
+
except ImportError:
|
621 |
+
print('If you want to use `safe_serialization`, please `pip install safetensors`')
|
622 |
+
raise
|
623 |
+
if max_shard_size is not None:
|
624 |
+
try:
|
625 |
+
from huggingface_hub import split_torch_state_dict_into_shards
|
626 |
+
except ImportError:
|
627 |
+
print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
|
628 |
+
raise
|
629 |
+
|
630 |
+
# Convert zero checkpoint to state_dict
|
631 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
632 |
+
tag,
|
633 |
+
exclude_frozen_parameters,
|
634 |
+
lazy_mode=True)
|
635 |
+
|
636 |
+
# Shard the model if it is too big.
|
637 |
+
weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
|
638 |
+
if max_shard_size is not None:
|
639 |
+
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
|
640 |
+
# an memory-efficient approach for sharding
|
641 |
+
empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
|
642 |
+
state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
|
643 |
+
filename_pattern=filename_pattern,
|
644 |
+
max_shard_size=max_shard_size)
|
645 |
+
else:
|
646 |
+
from collections import namedtuple
|
647 |
+
StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
|
648 |
+
state_dict_split = StateDictSplit(is_sharded=False,
|
649 |
+
filename_to_tensors={weights_name: list(state_dict.keys())})
|
650 |
+
|
651 |
+
# Save the model by shard
|
652 |
+
os.makedirs(output_dir, exist_ok=True)
|
653 |
+
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
654 |
+
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
655 |
+
shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
|
656 |
+
shard_state_dict = to_torch_tensor(shard_state_dict)
|
657 |
+
output_path = os.path.join(output_dir, shard_file)
|
658 |
+
if safe_serialization:
|
659 |
+
save_file(shard_state_dict, output_path, metadata={"format": "pt"})
|
660 |
+
else:
|
661 |
+
torch.save(shard_state_dict, output_path)
|
662 |
+
# release the memory of current shard
|
663 |
+
for tensor_name in shard_state_dict:
|
664 |
+
del state_dict[tensor_name]
|
665 |
+
del shard_state_dict
|
666 |
+
gc.collect()
|
667 |
+
|
668 |
+
# Save index if sharded
|
669 |
+
if state_dict_split.is_sharded:
|
670 |
+
index = {
|
671 |
+
"metadata": state_dict_split.metadata,
|
672 |
+
"weight_map": state_dict_split.tensor_to_filename,
|
673 |
+
}
|
674 |
+
save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
|
675 |
+
save_index_file = os.path.join(output_dir, save_index_file)
|
676 |
+
with open(save_index_file, "w", encoding="utf-8") as f:
|
677 |
+
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
|
678 |
+
f.write(content)
|
679 |
+
|
680 |
+
|
681 |
+
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
682 |
+
"""
|
683 |
+
1. Put the provided model to cpu
|
684 |
+
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
685 |
+
3. Load it into the provided model
|
686 |
+
|
687 |
+
Args:
|
688 |
+
- ``model``: the model object to update
|
689 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
690 |
+
- ``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``
|
691 |
+
|
692 |
+
Returns:
|
693 |
+
- ``model`: modified model
|
694 |
+
|
695 |
+
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
696 |
+
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
697 |
+
conveniently placed for you in the checkpoint folder.
|
698 |
+
|
699 |
+
A typical usage might be ::
|
700 |
+
|
701 |
+
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
702 |
+
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
703 |
+
# submit to model hub or save the model to share with others
|
704 |
+
|
705 |
+
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
706 |
+
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
707 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
708 |
+
|
709 |
+
"""
|
710 |
+
logger.info(f"Extracting fp32 weights")
|
711 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
712 |
+
|
713 |
+
logger.info(f"Overwriting model with fp32 weights")
|
714 |
+
model = model.cpu()
|
715 |
+
model.load_state_dict(state_dict, strict=False)
|
716 |
+
|
717 |
+
return model
|
718 |
+
|
719 |
+
|
720 |
+
if __name__ == "__main__":
|
721 |
+
parser = argparse.ArgumentParser()
|
722 |
+
parser.add_argument("checkpoint_dir",
|
723 |
+
type=str,
|
724 |
+
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
725 |
+
parser.add_argument("output_dir",
|
726 |
+
type=str,
|
727 |
+
help="directory to the pytorch fp32 state_dict output files"
|
728 |
+
"(e.g. path/checkpoint-12-output/)")
|
729 |
+
parser.add_argument(
|
730 |
+
"--max_shard_size",
|
731 |
+
type=str,
|
732 |
+
default="5GB",
|
733 |
+
help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
|
734 |
+
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
|
735 |
+
"We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
|
736 |
+
"without CPU OOM issues.")
|
737 |
+
parser.add_argument(
|
738 |
+
"--safe_serialization",
|
739 |
+
default=False,
|
740 |
+
action='store_true',
|
741 |
+
help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
|
742 |
+
parser.add_argument("-t",
|
743 |
+
"--tag",
|
744 |
+
type=str,
|
745 |
+
default=None,
|
746 |
+
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
747 |
+
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
748 |
+
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
749 |
+
args = parser.parse_args()
|
750 |
+
|
751 |
+
debug = args.debug
|
752 |
+
|
753 |
+
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
754 |
+
args.output_dir,
|
755 |
+
max_shard_size=args.max_shard_size,
|
756 |
+
safe_serialization=args.safe_serialization,
|
757 |
+
tag=args.tag,
|
758 |
+
exclude_frozen_parameters=args.exclude_frozen_parameters)
|