Upload folder using huggingface_hub

114/README.md

@@ -0,0 +1,202 @@
+---
+base_model: ibm-granite/granite-3.3-8b-base
+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 0.15.2

114/adapter_config.json

@@ -0,0 +1,39 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "ibm-granite/granite-3.3-8b-base",
+  "bias": "none",
+  "corda_config": null,
+  "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": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "o_proj",
+    "k_proj",
+    "down_proj",
+    "up_proj",
+    "v_proj",
+    "gate_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

114/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:070ef5fa0f96aeda93a02298b0724e0cdb7a33dd9d05e972eb09b24c1d3c50d8
+size 791751704

114/latest

@@ -0,0 +1 @@
+global_step372

114/rng_state_0.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d6c493d74ead4a59aa24fd5e2f44dfd8afc89a0ed063984fbad381459a14d52
+size 15920

114/rng_state_1.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ea243cddda56fbf9456ecd61c705307b975836052f46d3397318d89ee9484eb
+size 15920

114/rng_state_2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd7a7a4e393284b3d20ce0e17d0082a8c24dc71ca101f9a533025d8bb52fb712
+size 15920

114/rng_state_3.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85b38db3e849147c037680211726ee37a60040643a95ca212a631bf590de59f2
+size 15920

114/rng_state_4.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2983fd7f98a045a14cf8cfd2f6aeb2380093968f1c111aa390d29d53733543c5
+size 15920

114/rng_state_5.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35a8e7200360e73300b00544d5e6eb1aa4c73befd34326c095e5ab50a76fa5a4
+size 15920

114/rng_state_6.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e1de9c7c60919babf709c038bb9980041d2f1ecd6296bcd0d6f96a97206d25c
+size 15920

114/rng_state_7.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a8b0a6aefc583b98d38534c21e32ca86e8654f68f8a5b3dfe28a8473c296aef
+size 15920

114/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0dcd61781504de00d634b762567307a5e0876cdf9c71876975ab8414b1b1fa24
+size 1064

114/special_tokens_map.json

@@ -0,0 +1,45 @@
+{
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<fim_prefix>",
+    "<fim_middle>",
+    "<fim_suffix>",
+    "<fim_pad>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<reponame>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

114/tokenizer_config.json

@@ -0,0 +1,188 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<fim_prefix>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<fim_middle>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<fim_suffix>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<fim_pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<filename>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<gh_stars>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<issue_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<issue_comment>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<issue_closed>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<jupyter_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<jupyter_text>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<jupyter_code>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<jupyter_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<empty_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<commit_before>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "<commit_msg>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "17": {
+      "content": "<commit_after>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "18": {
+      "content": "<reponame>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<fim_prefix>",
+    "<fim_middle>",
+    "<fim_suffix>",
+    "<fim_pad>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 8192,
+  "pad_token": "<reponame>",
+  "padding_side": "left",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}

114/trainer_state.json

@@ -0,0 +1,552 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 1.0,
+  "eval_steps": 500,
+  "global_step": 372,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.013458950201884253,
+      "grad_norm": 1.00712251663208,
+      "learning_rate": 1.2903225806451614e-06,
+      "loss": 1.264,
+      "step": 5
+    },
+    {
+      "epoch": 0.026917900403768506,
+      "grad_norm": 0.9430832266807556,
+      "learning_rate": 2.9032258064516128e-06,
+      "loss": 1.2378,
+      "step": 10
+    },
+    {
+      "epoch": 0.040376850605652756,
+      "grad_norm": 0.6684543490409851,
+      "learning_rate": 4.516129032258065e-06,
+      "loss": 1.239,
+      "step": 15
+    },
+    {
+      "epoch": 0.05383580080753701,
+      "grad_norm": 0.7191868424415588,
+      "learning_rate": 6.129032258064517e-06,
+      "loss": 1.2218,
+      "step": 20
+    },
+    {
+      "epoch": 0.06729475100942127,
+      "grad_norm": 0.6867708563804626,
+      "learning_rate": 7.741935483870968e-06,
+      "loss": 1.2301,
+      "step": 25
+    },
+    {
+      "epoch": 0.08075370121130551,
+      "grad_norm": 0.6379647254943848,
+      "learning_rate": 9.35483870967742e-06,
+      "loss": 1.2194,
+      "step": 30
+    },
+    {
+      "epoch": 0.09421265141318977,
+      "grad_norm": 0.5798360109329224,
+      "learning_rate": 1.0967741935483872e-05,
+      "loss": 1.2002,
+      "step": 35
+    },
+    {
+      "epoch": 0.10767160161507403,
+      "grad_norm": 0.5719746351242065,
+      "learning_rate": 1.2580645161290324e-05,
+      "loss": 1.1982,
+      "step": 40
+    },
+    {
+      "epoch": 0.12113055181695828,
+      "grad_norm": 0.5450358390808105,
+      "learning_rate": 1.4193548387096776e-05,
+      "loss": 1.1986,
+      "step": 45
+    },
+    {
+      "epoch": 0.13458950201884254,
+      "grad_norm": 0.5413178205490112,
+      "learning_rate": 1.5806451612903226e-05,
+      "loss": 1.1955,
+      "step": 50
+    },
+    {
+      "epoch": 0.1480484522207268,
+      "grad_norm": 0.5677768588066101,
+      "learning_rate": 1.741935483870968e-05,
+      "loss": 1.1496,
+      "step": 55
+    },
+    {
+      "epoch": 0.16150740242261102,
+      "grad_norm": 0.5705103278160095,
+      "learning_rate": 1.903225806451613e-05,
+      "loss": 1.1377,
+      "step": 60
+    },
+    {
+      "epoch": 0.17496635262449528,
+      "grad_norm": 0.6191664934158325,
+      "learning_rate": 2.0645161290322582e-05,
+      "loss": 1.1242,
+      "step": 65
+    },
+    {
+      "epoch": 0.18842530282637954,
+      "grad_norm": 0.41417160630226135,
+      "learning_rate": 2.2258064516129034e-05,
+      "loss": 1.0923,
+      "step": 70
+    },
+    {
+      "epoch": 0.2018842530282638,
+      "grad_norm": 0.5877333283424377,
+      "learning_rate": 2.3870967741935483e-05,
+      "loss": 1.1575,
+      "step": 75
+    },
+    {
+      "epoch": 0.21534320323014805,
+      "grad_norm": 0.5006933808326721,
+      "learning_rate": 2.548387096774194e-05,
+      "loss": 1.0873,
+      "step": 80
+    },
+    {
+      "epoch": 0.2288021534320323,
+      "grad_norm": 0.5454208254814148,
+      "learning_rate": 2.7096774193548387e-05,
+      "loss": 1.073,
+      "step": 85
+    },
+    {
+      "epoch": 0.24226110363391656,
+      "grad_norm": 0.4708370268344879,
+      "learning_rate": 2.870967741935484e-05,
+      "loss": 1.1246,
+      "step": 90
+    },
+    {
+      "epoch": 0.2557200538358008,
+      "grad_norm": 0.7446239590644836,
+      "learning_rate": 2.9999976292388853e-05,
+      "loss": 1.0893,
+      "step": 95
+    },
+    {
+      "epoch": 0.2691790040376851,
+      "grad_norm": 0.5205251574516296,
+      "learning_rate": 2.999914653386729e-05,
+      "loss": 1.0233,
+      "step": 100
+    },
+    {
+      "epoch": 0.28263795423956933,
+      "grad_norm": 0.5542618632316589,
+      "learning_rate": 2.9997131469727287e-05,
+      "loss": 1.0534,
+      "step": 105
+    },
+    {
+      "epoch": 0.2960969044414536,
+      "grad_norm": 0.5413870811462402,
+      "learning_rate": 2.9993931259209015e-05,
+      "loss": 1.0217,
+      "step": 110
+    },
+    {
+      "epoch": 0.30955585464333785,
+      "grad_norm": 0.5619310736656189,
+      "learning_rate": 2.998954615520871e-05,
+      "loss": 1.0357,
+      "step": 115
+    },
+    {
+      "epoch": 0.32301480484522205,
+      "grad_norm": 0.5382521152496338,
+      "learning_rate": 2.998397650425863e-05,
+      "loss": 1.0228,
+      "step": 120
+    },
+    {
+      "epoch": 0.3364737550471063,
+      "grad_norm": 0.6172555088996887,
+      "learning_rate": 2.997722274649974e-05,
+      "loss": 0.9711,
+      "step": 125
+    },
+    {
+      "epoch": 0.34993270524899056,
+      "grad_norm": 0.6441250443458557,
+      "learning_rate": 2.9969285415646874e-05,
+      "loss": 0.9977,
+      "step": 130
+    },
+    {
+      "epoch": 0.3633916554508748,
+      "grad_norm": 0.5927826166152954,
+      "learning_rate": 2.9960165138946572e-05,
+      "loss": 0.9778,
+      "step": 135
+    },
+    {
+      "epoch": 0.3768506056527591,
+      "grad_norm": 0.6550005078315735,
+      "learning_rate": 2.9949862637127523e-05,
+      "loss": 0.9335,
+      "step": 140
+    },
+    {
+      "epoch": 0.39030955585464333,
+      "grad_norm": 0.5886467099189758,
+      "learning_rate": 2.9938378724343604e-05,
+      "loss": 0.9681,
+      "step": 145
+    },
+    {
+      "epoch": 0.4037685060565276,
+      "grad_norm": 0.7246305346488953,
+      "learning_rate": 2.992571430810954e-05,
+      "loss": 0.9701,
+      "step": 150
+    },
+    {
+      "epoch": 0.41722745625841184,
+      "grad_norm": 0.7467737197875977,
+      "learning_rate": 2.9911870389229192e-05,
+      "loss": 0.9141,
+      "step": 155
+    },
+    {
+      "epoch": 0.4306864064602961,
+      "grad_norm": 0.7680078744888306,
+      "learning_rate": 2.9896848061716456e-05,
+      "loss": 0.8685,
+      "step": 160
+    },
+    {
+      "epoch": 0.44414535666218036,
+      "grad_norm": 0.7801365256309509,
+      "learning_rate": 2.9880648512708834e-05,
+      "loss": 0.9138,
+      "step": 165
+    },
+    {
+      "epoch": 0.4576043068640646,
+      "grad_norm": 0.6462291479110718,
+      "learning_rate": 2.9863273022373585e-05,
+      "loss": 0.9223,
+      "step": 170
+    },
+    {
+      "epoch": 0.47106325706594887,
+      "grad_norm": 0.6398594975471497,
+      "learning_rate": 2.9844722963806592e-05,
+      "loss": 0.9021,
+      "step": 175
+    },
+    {
+      "epoch": 0.4845222072678331,
+      "grad_norm": 0.683367133140564,
+      "learning_rate": 2.9824999802923846e-05,
+      "loss": 0.8599,
+      "step": 180
+    },
+    {
+      "epoch": 0.4979811574697174,
+      "grad_norm": 0.7672361731529236,
+      "learning_rate": 2.9804105098345575e-05,
+      "loss": 0.9282,
+      "step": 185
+    },
+    {
+      "epoch": 0.5114401076716016,
+      "grad_norm": 0.6266528367996216,
+      "learning_rate": 2.9782040501273126e-05,
+      "loss": 0.8664,
+      "step": 190
+    },
+    {
+      "epoch": 0.5248990578734859,
+      "grad_norm": 0.7748366594314575,
+      "learning_rate": 2.975880775535843e-05,
+      "loss": 0.8588,
+      "step": 195
+    },
+    {
+      "epoch": 0.5383580080753702,
+      "grad_norm": 0.892792820930481,
+      "learning_rate": 2.9734408696566224e-05,
+      "loss": 0.8517,
+      "step": 200
+    },
+    {
+      "epoch": 0.5518169582772544,
+      "grad_norm": 0.8755563497543335,
+      "learning_rate": 2.9708845253028988e-05,
+      "loss": 0.9136,
+      "step": 205
+    },
+    {
+      "epoch": 0.5652759084791387,
+      "grad_norm": 0.7859370708465576,
+      "learning_rate": 2.9682119444894545e-05,
+      "loss": 0.7955,
+      "step": 210
+    },
+    {
+      "epoch": 0.5787348586810229,
+      "grad_norm": 0.7947808504104614,
+      "learning_rate": 2.9654233384166435e-05,
+      "loss": 0.8695,
+      "step": 215
+    },
+    {
+      "epoch": 0.5921938088829072,
+      "grad_norm": 0.7226110696792603,
+      "learning_rate": 2.9625189274537004e-05,
+      "loss": 0.7672,
+      "step": 220
+    },
+    {
+      "epoch": 0.6056527590847914,
+      "grad_norm": 1.0677162408828735,
+      "learning_rate": 2.9594989411213266e-05,
+      "loss": 0.816,
+      "step": 225
+    },
+    {
+      "epoch": 0.6191117092866757,
+      "grad_norm": 0.8201079964637756,
+      "learning_rate": 2.9563636180735524e-05,
+      "loss": 0.7877,
+      "step": 230
+    },
+    {
+      "epoch": 0.6325706594885598,
+      "grad_norm": 0.961162805557251,
+      "learning_rate": 2.9531132060788763e-05,
+      "loss": 0.7566,
+      "step": 235
+    },
+    {
+      "epoch": 0.6460296096904441,
+      "grad_norm": 0.9038267731666565,
+      "learning_rate": 2.9497479620006873e-05,
+      "loss": 0.8061,
+      "step": 240
+    },
+    {
+      "epoch": 0.6594885598923284,
+      "grad_norm": 0.9068340063095093,
+      "learning_rate": 2.9462681517769638e-05,
+      "loss": 0.787,
+      "step": 245
+    },
+    {
+      "epoch": 0.6729475100942126,
+      "grad_norm": 0.8361156582832336,
+      "learning_rate": 2.94267405039926e-05,
+      "loss": 0.7835,
+      "step": 250
+    },
+    {
+      "epoch": 0.6864064602960969,
+      "grad_norm": 0.8174508213996887,
+      "learning_rate": 2.938965941890972e-05,
+      "loss": 0.7641,
+      "step": 255
+    },
+    {
+      "epoch": 0.6998654104979811,
+      "grad_norm": 0.8101845383644104,
+      "learning_rate": 2.9351441192848972e-05,
+      "loss": 0.76,
+      "step": 260
+    },
+    {
+      "epoch": 0.7133243606998654,
+      "grad_norm": 0.8047847747802734,
+      "learning_rate": 2.9312088846000733e-05,
+      "loss": 0.7404,
+      "step": 265
+    },
+    {
+      "epoch": 0.7267833109017496,
+      "grad_norm": 0.8787263035774231,
+      "learning_rate": 2.927160548817913e-05,
+      "loss": 0.7335,
+      "step": 270
+    },
+    {
+      "epoch": 0.7402422611036339,
+      "grad_norm": 0.9566526412963867,
+      "learning_rate": 2.9229994318576295e-05,
+      "loss": 0.7546,
+      "step": 275
+    },
+    {
+      "epoch": 0.7537012113055181,
+      "grad_norm": 0.9340652227401733,
+      "learning_rate": 2.9187258625509518e-05,
+      "loss": 0.6814,
+      "step": 280
+    },
+    {
+      "epoch": 0.7671601615074024,
+      "grad_norm": 1.0249980688095093,
+      "learning_rate": 2.9143401786161445e-05,
+      "loss": 0.7116,
+      "step": 285
+    },
+    {
+      "epoch": 0.7806191117092867,
+      "grad_norm": 0.8947973847389221,
+      "learning_rate": 2.9098427266313138e-05,
+      "loss": 0.6845,
+      "step": 290
+    },
+    {
+      "epoch": 0.7940780619111709,
+      "grad_norm": 0.9918004274368286,
+      "learning_rate": 2.9052338620070215e-05,
+      "loss": 0.7287,
+      "step": 295
+    },
+    {
+      "epoch": 0.8075370121130552,
+      "grad_norm": 0.9002223610877991,
+      "learning_rate": 2.9005139489582002e-05,
+      "loss": 0.7129,
+      "step": 300
+    },
+    {
+      "epoch": 0.8209959623149394,
+      "grad_norm": 0.8825509548187256,
+      "learning_rate": 2.8956833604753688e-05,
+      "loss": 0.6633,
+      "step": 305
+    },
+    {
+      "epoch": 0.8344549125168237,
+      "grad_norm": 0.8505289554595947,
+      "learning_rate": 2.8907424782951587e-05,
+      "loss": 0.6826,
+      "step": 310
+    },
+    {
+      "epoch": 0.847913862718708,
+      "grad_norm": 1.0375418663024902,
+      "learning_rate": 2.8856916928701467e-05,
+      "loss": 0.7003,
+      "step": 315
+    },
+    {
+      "epoch": 0.8613728129205922,
+      "grad_norm": 1.0597079992294312,
+      "learning_rate": 2.8805314033379985e-05,
+      "loss": 0.7151,
+      "step": 320
+    },
+    {
+      "epoch": 0.8748317631224765,
+      "grad_norm": 1.1800124645233154,
+      "learning_rate": 2.8752620174899284e-05,
+      "loss": 0.6594,
+      "step": 325
+    },
+    {
+      "epoch": 0.8882907133243607,
+      "grad_norm": 0.9276929497718811,
+      "learning_rate": 2.8698839517384726e-05,
+      "loss": 0.651,
+      "step": 330
+    },
+    {
+      "epoch": 0.901749663526245,
+      "grad_norm": 1.094962239265442,
+      "learning_rate": 2.8643976310845828e-05,
+      "loss": 0.6412,
+      "step": 335
+    },
+    {
+      "epoch": 0.9152086137281292,
+      "grad_norm": 0.9263754487037659,
+      "learning_rate": 2.8588034890840408e-05,
+      "loss": 0.6269,
+      "step": 340
+    },
+    {
+      "epoch": 0.9286675639300135,
+      "grad_norm": 0.9816908240318298,
+      "learning_rate": 2.8531019678131952e-05,
+      "loss": 0.6577,
+      "step": 345
+    },
+    {
+      "epoch": 0.9421265141318977,
+      "grad_norm": 1.0188283920288086,
+      "learning_rate": 2.847293517834029e-05,
+      "loss": 0.66,
+      "step": 350
+    },
+    {
+      "epoch": 0.955585464333782,
+      "grad_norm": 1.0503355264663696,
+      "learning_rate": 2.8413785981585518e-05,
+      "loss": 0.621,
+      "step": 355
+    },
+    {
+      "epoch": 0.9690444145356663,
+      "grad_norm": 0.9092134237289429,
+      "learning_rate": 2.8353576762125274e-05,
+      "loss": 0.6343,
+      "step": 360
+    },
+    {
+      "epoch": 0.9825033647375505,
+      "grad_norm": 0.9565970301628113,
+      "learning_rate": 2.8292312277985354e-05,
+      "loss": 0.6538,
+      "step": 365
+    },
+    {
+      "epoch": 0.9959623149394348,
+      "grad_norm": 1.014952301979065,
+      "learning_rate": 2.82299973705837e-05,
+      "loss": 0.6081,
+      "step": 370
+    }
+  ],
+  "logging_steps": 5,
+  "max_steps": 1860,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 5,
+  "save_steps": 2000,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 5.29235739645313e+17,
+  "train_batch_size": 2,
+  "trial_name": null,
+  "trial_params": null
+}

114/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:172c033ee011733dcab4f83b130b3ce88d4ead4e1c616867187f00f92a816ae0
+size 7736

114/zero_to_fp32.py

@@ -0,0 +1,604 @@
+#!/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 . pytorch_model.bin
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+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 param_shapes.items():
+
+        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_file, 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_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
+        - ``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
+    """
+
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
+    print(f"Saving fp32 state dict to {output_file}")
+    torch.save(state_dict, output_file)
+
+
+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_file",
+        type=str,
+        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
+    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_file,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)