Training in progress, step 250, checkpoint

checkpoint-250/README.md

@@ -0,0 +1,202 @@
+---
+base_model: meta-llama/CodeLlama-7b-Instruct-hf
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.14.0

checkpoint-250/adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/CodeLlama-7b-Instruct-hf",
+  "bias": "none",
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "down_proj",
+    "k_proj",
+    "gate_proj",
+    "o_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

checkpoint-250/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20f162ea214e0953700bd73b322d14375f02ad63b80dfd9c31601fbe2b9ff081
+size 40036488

checkpoint-250/global_step250/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c557ee1c29bd139575fdf3abbf4764ea566094f0ef4c6af1161429e0ca895a30
+size 29992112

checkpoint-250/global_step250/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:079c8eb23f652a12ac0ad3ffa72a6a8d26947b1909ebeba3bf54f939c16918b8
+size 29992112

checkpoint-250/global_step250/bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eae898baaad25aeef59d853b872dd94939fc69e98a16c4052364875001f1bb67
+size 29992176

checkpoint-250/global_step250/bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54657c00ea3416b41b69f22de67dd5a9b91354e6e6a7a0a3f249c29117a041f3
+size 29992176

checkpoint-250/global_step250/bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d95d80e8cee9dc2e734fbe409e2d2d017e2eaca6518aa9baf02648377a0fa38
+size 29992176

checkpoint-250/global_step250/bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff97f68c0eda8f7903f36e3e4761c5842a279a349595275478051b1de8e2a296
+size 29992176

checkpoint-250/global_step250/bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6293f057d59e469fcba9d5c11c36193fb64ed96da6731f37e4d44a161dad5778
+size 29992176

checkpoint-250/global_step250/bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6125e7c4a4bcbd9cfb73137a5afea334788a9133be2680b61e4ad66b60446c67
+size 29992176

checkpoint-250/global_step250/mp_rank_00_model_states.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:01c66f89bcb5b9e3cb24e44d00869f88612a2d8e527062b52c28692ea3d09154
+size 40324204

checkpoint-250/latest

@@ -0,0 +1 @@
+global_step250

checkpoint-250/rng_state_0.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69ec6e3926fa071bede113523efa3dc6e630c3c7958c54a9ca321cf4d62ed145
+size 15984

checkpoint-250/rng_state_1.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6127ee4f0c13500ec5038fce65af8f7beec63c137c7d4b7c157aa6303cf5879
+size 15984

checkpoint-250/rng_state_2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da01d1c5eb2cc3a323f97c1f590d13ccfac2a4c5b1479bd378b4e643304f5a4f
+size 15984

checkpoint-250/rng_state_3.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49a3f04d76c0d3acc7d3dd95a04215f368f35a451ae8cba8a2fdba38cda9ca0a
+size 15984

checkpoint-250/rng_state_4.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df7d2c9825dba80cb544920f8cc0c72122f96514e6cd259052a8765b034393e2
+size 15984

checkpoint-250/rng_state_5.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a20a42d44ff48cc162224010190e898fe28598ddad8cd1896d330a3bb1d8ec3
+size 15984

checkpoint-250/rng_state_6.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18ac0dc4f09f25179860561fcea7c5c8f997aabdc46a170665f9dc5a72bc27c6
+size 15984

checkpoint-250/rng_state_7.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a16fcb5411ff961b47eff7378d85105fe9837e0492d19ea5ce3b7c4b77aa3b6
+size 15984

checkpoint-250/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bab046ea484a3835c9c055d707f85d7de13eb96e8bc90f01101b3deef243b92c
+size 1064

checkpoint-250/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "additional_special_tokens": [
+    "▁<PRE>",
+    "▁<MID>",
+    "▁<SUF>",
+    "▁<EOT>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-250/tokenizer_config.json

@@ -0,0 +1,87 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "▁<PRE>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "▁<SUF>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "▁<MID>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "▁<EOT>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "▁<PRE>",
+    "▁<MID>",
+    "▁<SUF>",
+    "▁<EOT>"
+  ],
+  "bos_token": "<s>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if loop.index0 == 0 and system_message is defined %}{% set content = '<<SYS>>\n' + system_message + '\n<</SYS>>\n\n' + message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '<s>' + '[INST] ' + content + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content + ' ' + '</s>' }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "eot_token": "▁<EOT>",
+  "extra_special_tokens": {},
+  "fill_token": "<FILL_ME>",
+  "legacy": null,
+  "middle_token": "▁<MID>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "prefix_token": "▁<PRE>",
+  "sp_model_kwargs": {},
+  "split_special_tokens": false,
+  "suffix_token": "▁<SUF>",
+  "tokenizer_class": "CodeLlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

checkpoint-250/trainer_state.json

@@ -0,0 +1,488 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.1812086617740328,
+  "eval_steps": 50,
+  "global_step": 250,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.007248346470961312,
+      "grad_norm": 0.07235438376665115,
+      "learning_rate": 4.998766400914329e-06,
+      "logits/chosen": -1.9785633087158203,
+      "logits/rejected": -2.5380234718322754,
+      "logps/chosen": -0.28137442469596863,
+      "logps/rejected": -0.37793582677841187,
+      "loss": 7.3908,
+      "rewards/accuracies": 0.550000011920929,
+      "rewards/chosen": -0.42206162214279175,
+      "rewards/margins": 0.14484205842018127,
+      "rewards/rejected": -0.5669037103652954,
+      "step": 10
+    },
+    {
+      "epoch": 0.014496692941922623,
+      "grad_norm": 0.07455909997224808,
+      "learning_rate": 4.9950668210706795e-06,
+      "logits/chosen": -2.0576319694519043,
+      "logits/rejected": -2.499069929122925,
+      "logps/chosen": -0.2769497036933899,
+      "logps/rejected": -0.33560773730278015,
+      "loss": 7.3753,
+      "rewards/accuracies": 0.574999988079071,
+      "rewards/chosen": -0.41542449593544006,
+      "rewards/margins": 0.08798708021640778,
+      "rewards/rejected": -0.503411591053009,
+      "step": 20
+    },
+    {
+      "epoch": 0.021745039412883936,
+      "grad_norm": 0.09561269730329514,
+      "learning_rate": 4.9889049115077e-06,
+      "logits/chosen": -2.1018643379211426,
+      "logits/rejected": -2.377673625946045,
+      "logps/chosen": -0.2673099935054779,
+      "logps/rejected": -0.3057115972042084,
+      "loss": 7.4516,
+      "rewards/accuracies": 0.4375,
+      "rewards/chosen": -0.40096497535705566,
+      "rewards/margins": 0.05760239437222481,
+      "rewards/rejected": -0.45856744050979614,
+      "step": 30
+    },
+    {
+      "epoch": 0.028993385883845247,
+      "grad_norm": 0.08169445395469666,
+      "learning_rate": 4.980286753286196e-06,
+      "logits/chosen": -2.1627352237701416,
+      "logits/rejected": -2.3873391151428223,
+      "logps/chosen": -0.27636194229125977,
+      "logps/rejected": -0.3703404664993286,
+      "loss": 7.3888,
+      "rewards/accuracies": 0.6499999761581421,
+      "rewards/chosen": -0.4145428538322449,
+      "rewards/margins": 0.14096775650978088,
+      "rewards/rejected": -0.5555106401443481,
+      "step": 40
+    },
+    {
+      "epoch": 0.03624173235480656,
+      "grad_norm": 0.08877147734165192,
+      "learning_rate": 4.9692208514878445e-06,
+      "logits/chosen": -2.1400368213653564,
+      "logits/rejected": -2.44627046585083,
+      "logps/chosen": -0.25384199619293213,
+      "logps/rejected": -0.309225857257843,
+      "loss": 7.518,
+      "rewards/accuracies": 0.512499988079071,
+      "rewards/chosen": -0.3807629942893982,
+      "rewards/margins": 0.08307582885026932,
+      "rewards/rejected": -0.4638388752937317,
+      "step": 50
+    },
+    {
+      "epoch": 0.03624173235480656,
+      "eval_logits/chosen": -2.1149837970733643,
+      "eval_logits/rejected": -2.4759538173675537,
+      "eval_logps/chosen": -0.28288090229034424,
+      "eval_logps/rejected": -0.3434317111968994,
+      "eval_loss": 0.9120966196060181,
+      "eval_rewards/accuracies": 0.5089285969734192,
+      "eval_rewards/chosen": -0.42432135343551636,
+      "eval_rewards/margins": 0.09082622081041336,
+      "eval_rewards/rejected": -0.5151475667953491,
+      "eval_runtime": 30.2473,
+      "eval_samples_per_second": 29.49,
+      "eval_steps_per_second": 3.703,
+      "step": 50
+    },
+    {
+      "epoch": 0.04349007882576787,
+      "grad_norm": 0.11774340271949768,
+      "learning_rate": 4.9557181268217225e-06,
+      "logits/chosen": -2.0567967891693115,
+      "logits/rejected": -2.545668125152588,
+      "logps/chosen": -0.2460743933916092,
+      "logps/rejected": -0.32390663027763367,
+      "loss": 7.3929,
+      "rewards/accuracies": 0.5625,
+      "rewards/chosen": -0.3691115975379944,
+      "rewards/margins": 0.1167483925819397,
+      "rewards/rejected": -0.4858599603176117,
+      "step": 60
+    },
+    {
+      "epoch": 0.05073842529672919,
+      "grad_norm": 0.10012238472700119,
+      "learning_rate": 4.939791904846869e-06,
+      "logits/chosen": -1.9286606311798096,
+      "logits/rejected": -2.4620895385742188,
+      "logps/chosen": -0.25992274284362793,
+      "logps/rejected": -0.3665553629398346,
+      "loss": 7.1863,
+      "rewards/accuracies": 0.6000000238418579,
+      "rewards/chosen": -0.3898841440677643,
+      "rewards/margins": 0.15994893014431,
+      "rewards/rejected": -0.5498330593109131,
+      "step": 70
+    },
+    {
+      "epoch": 0.057986771767690494,
+      "grad_norm": 0.07387609034776688,
+      "learning_rate": 4.921457902821578e-06,
+      "logits/chosen": -2.0778262615203857,
+      "logits/rejected": -2.4614813327789307,
+      "logps/chosen": -0.24521782994270325,
+      "logps/rejected": -0.3450419306755066,
+      "loss": 7.3662,
+      "rewards/accuracies": 0.5874999761581421,
+      "rewards/chosen": -0.3678267002105713,
+      "rewards/margins": 0.1497361809015274,
+      "rewards/rejected": -0.5175628662109375,
+      "step": 80
+    },
+    {
+      "epoch": 0.0652351182386518,
+      "grad_norm": 0.09718403220176697,
+      "learning_rate": 4.900734214192358e-06,
+      "logits/chosen": -1.998186707496643,
+      "logits/rejected": -2.4382071495056152,
+      "logps/chosen": -0.24080593883991241,
+      "logps/rejected": -0.3280099332332611,
+      "loss": 7.268,
+      "rewards/accuracies": 0.6000000238418579,
+      "rewards/chosen": -0.36120888590812683,
+      "rewards/margins": 0.13080602884292603,
+      "rewards/rejected": -0.49201488494873047,
+      "step": 90
+    },
+    {
+      "epoch": 0.07248346470961312,
+      "grad_norm": 0.08059138059616089,
+      "learning_rate": 4.8776412907378845e-06,
+      "logits/chosen": -1.894728422164917,
+      "logits/rejected": -2.475407838821411,
+      "logps/chosen": -0.2019994705915451,
+      "logps/rejected": -0.29733040928840637,
+      "loss": 7.2544,
+      "rewards/accuracies": 0.5874999761581421,
+      "rewards/chosen": -0.30299919843673706,
+      "rewards/margins": 0.14299637079238892,
+      "rewards/rejected": -0.445995569229126,
+      "step": 100
+    },
+    {
+      "epoch": 0.07248346470961312,
+      "eval_logits/chosen": -2.1233773231506348,
+      "eval_logits/rejected": -2.4810428619384766,
+      "eval_logps/chosen": -0.24400465190410614,
+      "eval_logps/rejected": -0.3260345160961151,
+      "eval_loss": 0.8920583724975586,
+      "eval_rewards/accuracies": 0.5625,
+      "eval_rewards/chosen": -0.3660070598125458,
+      "eval_rewards/margins": 0.12304472178220749,
+      "eval_rewards/rejected": -0.4890517592430115,
+      "eval_runtime": 30.28,
+      "eval_samples_per_second": 29.458,
+      "eval_steps_per_second": 3.699,
+      "step": 100
+    },
+    {
+      "epoch": 0.07973181118057443,
+      "grad_norm": 0.060006480664014816,
+      "learning_rate": 4.852201922385564e-06,
+      "logits/chosen": -2.1128883361816406,
+      "logits/rejected": -2.5161118507385254,
+      "logps/chosen": -0.23731942474842072,
+      "logps/rejected": -0.31481316685676575,
+      "loss": 7.1762,
+      "rewards/accuracies": 0.550000011920929,
+      "rewards/chosen": -0.3559790849685669,
+      "rewards/margins": 0.11624068021774292,
+      "rewards/rejected": -0.4722197651863098,
+      "step": 110
+    },
+    {
+      "epoch": 0.08698015765153574,
+      "grad_norm": 0.0602690726518631,
+      "learning_rate": 4.824441214720629e-06,
+      "logits/chosen": -2.0212602615356445,
+      "logits/rejected": -2.4894890785217285,
+      "logps/chosen": -0.18956038355827332,
+      "logps/rejected": -0.31935763359069824,
+      "loss": 7.1442,
+      "rewards/accuracies": 0.625,
+      "rewards/chosen": -0.2843405604362488,
+      "rewards/margins": 0.19469590485095978,
+      "rewards/rejected": -0.47903648018836975,
+      "step": 120
+    },
+    {
+      "epoch": 0.09422850412249706,
+      "grad_norm": 0.06425223499536514,
+      "learning_rate": 4.794386564209953e-06,
+      "logits/chosen": -2.06717848777771,
+      "logits/rejected": -2.5086405277252197,
+      "logps/chosen": -0.2075866460800171,
+      "logps/rejected": -0.3124083876609802,
+      "loss": 7.1449,
+      "rewards/accuracies": 0.612500011920929,
+      "rewards/chosen": -0.31137990951538086,
+      "rewards/margins": 0.15723267197608948,
+      "rewards/rejected": -0.46861258149147034,
+      "step": 130
+    },
+    {
+      "epoch": 0.10147685059345837,
+      "grad_norm": 0.08793757855892181,
+      "learning_rate": 4.762067631165049e-06,
+      "logits/chosen": -2.0242953300476074,
+      "logits/rejected": -2.4943737983703613,
+      "logps/chosen": -0.19989363849163055,
+      "logps/rejected": -0.32057636976242065,
+      "loss": 7.122,
+      "rewards/accuracies": 0.625,
+      "rewards/chosen": -0.29984045028686523,
+      "rewards/margins": 0.18102414906024933,
+      "rewards/rejected": -0.48086461424827576,
+      "step": 140
+    },
+    {
+      "epoch": 0.10872519706441967,
+      "grad_norm": 0.09698841720819473,
+      "learning_rate": 4.72751631047092e-06,
+      "logits/chosen": -2.098658800125122,
+      "logits/rejected": -2.582111120223999,
+      "logps/chosen": -0.18897351622581482,
+      "logps/rejected": -0.363709419965744,
+      "loss": 7.1253,
+      "rewards/accuracies": 0.6625000238418579,
+      "rewards/chosen": -0.28346025943756104,
+      "rewards/margins": 0.2621038556098938,
+      "rewards/rejected": -0.5455641150474548,
+      "step": 150
+    },
+    {
+      "epoch": 0.10872519706441967,
+      "eval_logits/chosen": -2.1921768188476562,
+      "eval_logits/rejected": -2.556370973587036,
+      "eval_logps/chosen": -0.22214026749134064,
+      "eval_logps/rejected": -0.3240993022918701,
+      "eval_loss": 0.8764163851737976,
+      "eval_rewards/accuracies": 0.5803571343421936,
+      "eval_rewards/chosen": -0.33321040868759155,
+      "eval_rewards/margins": 0.15293852984905243,
+      "eval_rewards/rejected": -0.48614898324012756,
+      "eval_runtime": 30.273,
+      "eval_samples_per_second": 29.465,
+      "eval_steps_per_second": 3.7,
+      "step": 150
+    },
+    {
+      "epoch": 0.11597354353538099,
+      "grad_norm": 0.09119638800621033,
+      "learning_rate": 4.690766700109659e-06,
+      "logits/chosen": -2.2321066856384277,
+      "logits/rejected": -2.5475690364837646,
+      "logps/chosen": -0.19602122902870178,
+      "logps/rejected": -0.27682799100875854,
+      "loss": 7.0307,
+      "rewards/accuracies": 0.5375000238418579,
+      "rewards/chosen": -0.2940318286418915,
+      "rewards/margins": 0.12121014297008514,
+      "rewards/rejected": -0.4152420163154602,
+      "step": 160
+    },
+    {
+      "epoch": 0.1232218900063423,
+      "grad_norm": 0.11496366560459137,
+      "learning_rate": 4.65185506750986e-06,
+      "logits/chosen": -2.1506271362304688,
+      "logits/rejected": -2.506520986557007,
+      "logps/chosen": -0.18018664419651031,
+      "logps/rejected": -0.3084454834461212,
+      "loss": 7.1652,
+      "rewards/accuracies": 0.637499988079071,
+      "rewards/chosen": -0.27028003334999084,
+      "rewards/margins": 0.19238826632499695,
+      "rewards/rejected": -0.462668240070343,
+      "step": 170
+    },
+    {
+      "epoch": 0.1304702364773036,
+      "grad_norm": 0.09201692789793015,
+      "learning_rate": 4.610819813755038e-06,
+      "logits/chosen": -2.285245895385742,
+      "logits/rejected": -2.573941707611084,
+      "logps/chosen": -0.1844937801361084,
+      "logps/rejected": -0.3112415075302124,
+      "loss": 7.0022,
+      "rewards/accuracies": 0.625,
+      "rewards/chosen": -0.2767406404018402,
+      "rewards/margins": 0.1901216208934784,
+      "rewards/rejected": -0.4668622612953186,
+      "step": 180
+    },
+    {
+      "epoch": 0.13771858294826492,
+      "grad_norm": 0.13191230595111847,
+      "learning_rate": 4.567701435686405e-06,
+      "logits/chosen": -2.265538454055786,
+      "logits/rejected": -2.569638252258301,
+      "logps/chosen": -0.21122264862060547,
+      "logps/rejected": -0.3469196856021881,
+      "loss": 7.004,
+      "rewards/accuracies": 0.5874999761581421,
+      "rewards/chosen": -0.3168340027332306,
+      "rewards/margins": 0.20354552567005157,
+      "rewards/rejected": -0.5203795433044434,
+      "step": 190
+    },
+    {
+      "epoch": 0.14496692941922623,
+      "grad_norm": 0.1503789722919464,
+      "learning_rate": 4.522542485937369e-06,
+      "logits/chosen": -2.2021026611328125,
+      "logits/rejected": -2.655961513519287,
+      "logps/chosen": -0.19664816558361053,
+      "logps/rejected": -0.34679359197616577,
+      "loss": 7.0539,
+      "rewards/accuracies": 0.6499999761581421,
+      "rewards/chosen": -0.2949722409248352,
+      "rewards/margins": 0.22521813213825226,
+      "rewards/rejected": -0.5201903581619263,
+      "step": 200
+    },
+    {
+      "epoch": 0.14496692941922623,
+      "eval_logits/chosen": -2.325611114501953,
+      "eval_logits/rejected": -2.715676784515381,
+      "eval_logps/chosen": -0.2261900007724762,
+      "eval_logps/rejected": -0.3570065200328827,
+      "eval_loss": 0.8565592169761658,
+      "eval_rewards/accuracies": 0.5714285969734192,
+      "eval_rewards/chosen": -0.3392849862575531,
+      "eval_rewards/margins": 0.19622473418712616,
+      "eval_rewards/rejected": -0.5355097055435181,
+      "eval_runtime": 30.2681,
+      "eval_samples_per_second": 29.47,
+      "eval_steps_per_second": 3.7,
+      "step": 200
+    },
+    {
+      "epoch": 0.15221527589018755,
+      "grad_norm": 0.12589682638645172,
+      "learning_rate": 4.475387530939226e-06,
+      "logits/chosen": -2.24491810798645,
+      "logits/rejected": -2.739323616027832,
+      "logps/chosen": -0.19889305531978607,
+      "logps/rejected": -0.32832199335098267,
+      "loss": 6.9013,
+      "rewards/accuracies": 0.5874999761581421,
+      "rewards/chosen": -0.2983395457267761,
+      "rewards/margins": 0.1941433995962143,
+      "rewards/rejected": -0.492482990026474,
+      "step": 210
+    },
+    {
+      "epoch": 0.15946362236114886,
+      "grad_norm": 0.1417039930820465,
+      "learning_rate": 4.426283106939474e-06,
+      "logits/chosen": -2.278883695602417,
+      "logits/rejected": -2.7108216285705566,
+      "logps/chosen": -0.2170490026473999,
+      "logps/rejected": -0.3783469796180725,
+      "loss": 6.9366,
+      "rewards/accuracies": 0.699999988079071,
+      "rewards/chosen": -0.32557350397109985,
+      "rewards/margins": 0.2419470101594925,
+      "rewards/rejected": -0.5675204992294312,
+      "step": 220
+    },
+    {
+      "epoch": 0.16671196883211017,
+      "grad_norm": 0.1472843438386917,
+      "learning_rate": 4.3752776740761495e-06,
+      "logits/chosen": -2.16560697555542,
+      "logits/rejected": -2.7911431789398193,
+      "logps/chosen": -0.20280452072620392,
+      "logps/rejected": -0.3874067962169647,
+      "loss": 6.8823,
+      "rewards/accuracies": 0.699999988079071,
+      "rewards/chosen": -0.3042067885398865,
+      "rewards/margins": 0.2769034504890442,
+      "rewards/rejected": -0.5811101794242859,
+      "step": 230
+    },
+    {
+      "epoch": 0.1739603153030715,
+      "grad_norm": 0.15212437510490417,
+      "learning_rate": 4.322421568553529e-06,
+      "logits/chosen": -2.2929282188415527,
+      "logits/rejected": -2.8649649620056152,
+      "logps/chosen": -0.1841541975736618,
+      "logps/rejected": -0.3838128447532654,
+      "loss": 6.7139,
+      "rewards/accuracies": 0.6625000238418579,
+      "rewards/chosen": -0.2762312889099121,
+      "rewards/margins": 0.29948797821998596,
+      "rewards/rejected": -0.5757192373275757,
+      "step": 240
+    },
+    {
+      "epoch": 0.1812086617740328,
+      "grad_norm": 0.22261729836463928,
+      "learning_rate": 4.267766952966369e-06,
+      "logits/chosen": -2.436549663543701,
+      "logits/rejected": -2.827937126159668,
+      "logps/chosen": -0.2322833091020584,
+      "logps/rejected": -0.39587104320526123,
+      "loss": 6.7737,
+      "rewards/accuracies": 0.550000011920929,
+      "rewards/chosen": -0.3484249711036682,
+      "rewards/margins": 0.24538159370422363,
+      "rewards/rejected": -0.5938066244125366,
+      "step": 250
+    },
+    {
+      "epoch": 0.1812086617740328,
+      "eval_logits/chosen": -2.4983742237091064,
+      "eval_logits/rejected": -2.8988120555877686,
+      "eval_logps/chosen": -0.269414484500885,
+      "eval_logps/rejected": -0.45964303612709045,
+      "eval_loss": 0.816527247428894,
+      "eval_rewards/accuracies": 0.5714285969734192,
+      "eval_rewards/chosen": -0.4041217863559723,
+      "eval_rewards/margins": 0.2853427231311798,
+      "eval_rewards/rejected": -0.6894644498825073,
+      "eval_runtime": 30.2705,
+      "eval_samples_per_second": 29.468,
+      "eval_steps_per_second": 3.7,
+      "step": 250
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 1000,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 1,
+  "save_steps": 50,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 9.284468685374751e+17,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-250/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:602622297466a1bfe52292d356feac019e4207cdfec4d9f242a9474fbe739126
+size 7224

checkpoint-250/zero_to_fp32.py

@@ -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)