Update tokenizer_config.json

.gitattributes

@@ -25,4 +25,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,80 +1,45 @@
 ---
-license:
-- other
-- apache-2.0
-library_name: transformers
+license: apache-2.0
 tags:
 - generated_from_trainer
 - text-generation
-- OPT
+- opt
 - non-commercial
 - dialogue
 - chatbot
-- ai-msgbot
-pipeline_tag: text-generation
-widget:
-- text: 'If you could live anywhere, where would it be? peter szemraj:'
-  example_title: live anywhere
-- text: 'What would you sing at Karaoke night? peter szemraj:'
-  example_title: Karaoke
-- text: 'If you could hire someone to help you, would it be with cleaning, cooking,
-    or yard work? peter szemraj:'
-  example_title: help
-- text: 'What form of public transportation do you prefer? (air, boat, train, bus,
-    car, etc.) peter szemraj:'
-  example_title: transportation
-- text: 'What''s your favorite zoo animal? peter szemraj:'
-  example_title: animal
-- text: 'Do you like or dislike surprises? Why or why not? peter szemraj:'
-  example_title: surprises
-- text: 'What celebrity would you like to meet at Starbucks for a cup of coffee? peter
-    szemraj:'
-  example_title: 'celebrity '
-base_model: facebook/opt-2.7b
+
+inference: false
 ---
 
 # pszemraj/opt-peter-2.7B
 
- <a href="https://colab.research.google.com/gist/pszemraj/4068382a40bbf7aab50638b062bd97a9/opt-peter-2-7b-example-csearch-generation.ipynb">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
-
-This model is a fine-tuned version of [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) on about 80k WhatsApp/text messages (mine). Please use responsibly :)
+This model is a fine-tuned version of [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) on about 80k whatsapp/text messages (mine). Please use responsibly :)
 
-Test it out on Google Colab by clicking the button above.
+Test it out on Google Colab [here](https://colab.research.google.com/gist/pszemraj/26a69775c9d012051396ab5ae980f5c1/example-text-gen-pszemraj-opt-peter-2-7b.ipynb)!
 
-![chatdemo](https://i.imgur.com/1EgQYat.png)
+![chatdemo](demo.jpeg)
 
 ## Model description
 
 - Exploring to see how OPT does in terms of dialogue/conversational applications
 - Seems to do a lot better than GPT-Neo with similar training parameters 
-- you can create your own digital clone and deploy it leveraging [this repository I am working on](https://github.com/pszemraj/ai-msgbot).
-
-### sharded checkpoint
-
-As this model file is 10+ GB, it can impose some constraints with lower RAM runtimes and/or download speeds. To help with this issue, a sharded checkpoint of this model is available [here](https://huggingface.co/pszemraj/opt-peter-2.7B-sharded).
-
-The `pszemraj/opt-peter-2.7B-sharded` model can be used as a drop-in replacement for this one for all use cases.
 
 ## Intended uses & limitations
 
-> The base model has a custom license that propagates to this one. **Most importantly, it cannot be used commercially**. Read more here: [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) 
+> The base model has a custom license which propogates to this one. Most importantly, it cannot be used commercially. Read more here: [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) 
 
-- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 GB, Colab notebook linked above.
+- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 gb, Colab notebook linked above.
   - alternatively, you can message [a bot on telegram](http://t.me/GPTPeter_bot) where I test LLMs for dialogue generation
 - **any statements or claims made by this model do not reflect actual claims/statements by me.** Keep in mind it is a _fine-tuned_ version of the model on my data, so things from pre-training are also present in outputs.
 
 ## Training and evaluation data
 
-WhatsApp & iMessage data were parsed using [ai-msgbot](https://github.com/pszemraj/ai-msgbot) and then fed as a text dataset to the HF trainer.
+More information needed
 
 ## Training procedure
 
 ### Training hyperparameters
 
-**SESSION ONE**
-
 The following hyperparameters were used during training:
 - learning_rate: 4e-05
 - train_batch_size: 8
@@ -88,20 +53,8 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.01
 - num_epochs: 3
 
-**SESSION TWO**
+### Training results
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- distributed_type: multi-GPU
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.05
-- num_epochs: 4
 
 
 ### Framework versions
@@ -109,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.19.2
 - Pytorch 1.10.0+cu113
 - Datasets 2.2.2
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1

config.json

@@ -1,12 +1,12 @@
 {
-  "_name_or_path": "pszemraj/opt-peter-2.7B",
+  "_name_or_path": "facebook/opt-2.7b",
   "activation_dropout": 0.0,
   "activation_function": "relu",
   "architectures": [
     "OPTForCausalLM"
   ],
   "attention_dropout": 0.0,
-  "bos_token_id": 2,
+  "bos_token_id": 0,
   "do_layer_norm_before": true,
   "dropout": 0.1,
   "eos_token_id": 2,
@@ -20,10 +20,9 @@
   "num_hidden_layers": 32,
   "pad_token_id": 1,
   "prefix": "</s>",
-  "torch_dtype": "float32",
+  "torch_dtype": "bfloat16",
   "transformers_version": "4.19.2",
   "use_cache": false,
   "vocab_size": 50265,
-  "word_embed_proj_dim": 2560,
-  "_remove_final_layer_norm": true
-}
+  "word_embed_proj_dim": 2560
+}

generation_config.json

@@ -1,10 +0,0 @@
-{
-  "do_sample": true,
-  "eos_token_id": 2,
-  "max_length": 128,
-  "min_length": 16,
-  "pad_token_id": 2,
-  "penalty_alpha": 0.6,
-  "top_k": 4,
-  "transformers_version": "4.26.1"
-}

latest

@@ -0,0 +1 @@
+global_step1852

model.safetensors

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opt-2pt7b-ps_DS-msgs_Ep-3_Bs-8_training_metadata.json

@@ -1,101 +1 @@
-{
-   "output_dir":"/content/drive/MyDrive/Programming/hf-trainer/opt-2pt7b-ps_DS-msgs_Ep-3_Bs-8",
-   "overwrite_output_dir":true,
-   "do_train":false,
-   "do_eval":false,
-   "do_predict":false,
-   "evaluation_strategy":"no",
-   "prediction_loss_only":false,
-   "per_device_train_batch_size":8,
-   "per_device_eval_batch_size":8,
-   "per_gpu_train_batch_size":"None",
-   "per_gpu_eval_batch_size":"None",
-   "gradient_accumulation_steps":16,
-   "eval_accumulation_steps":8,
-   "eval_delay":0,
-   "learning_rate":4e-05,
-   "weight_decay":0.1,
-   "adam_beta1":0.9,
-   "adam_beta2":0.999,
-   "adam_epsilon":1e-08,
-   "max_grad_norm":1,
-   "num_train_epochs":3,
-   "max_steps":-1,
-   "lr_scheduler_type":"cosine",
-   "warmup_ratio":0.01,
-   "warmup_steps":0,
-   "log_level":-1,
-   "log_level_replica":-1,
-   "log_on_each_node":true,
-   "logging_dir":"/content/drive/MyDrive/Programming/hf-trainer/opt-2pt7b-ps_DS-msgs_Ep-3_Bs-8/logs",
-   "logging_strategy":"steps",
-   "logging_first_step":false,
-   "logging_steps":5,
-   "logging_nan_inf_filter":true,
-   "save_strategy":"epoch",
-   "save_steps":500,
-   "save_total_limit":1,
-   "save_on_each_node":false,
-   "no_cuda":false,
-   "seed":42,
-   "data_seed":"None",
-   "bf16":true,
-   "fp16":false,
-   "fp16_opt_level":"O1",
-   "half_precision_backend":"amp",
-   "bf16_full_eval":true,
-   "fp16_full_eval":false,
-   "tf32":"None",
-   "local_rank":0,
-   "xpu_backend":"None",
-   "tpu_num_cores":"None",
-   "tpu_metrics_debug":false,
-   "debug":"[]",
-   "dataloader_drop_last":false,
-   "eval_steps":"None",
-   "dataloader_num_workers":0,
-   "past_index":-1,
-   "run_name":"/content/drive/MyDrive/Programming/hf-trainer/opt-2pt7b-ps_DS-msgs_Ep-3_Bs-8",
-   "disable_tqdm":false,
-   "remove_unused_columns":true,
-   "label_names":"None",
-   "load_best_model_at_end":false,
-   "metric_for_best_model":"None",
-   "greater_is_better":"None",
-   "ignore_data_skip":false,
-   "sharded_ddp":"[]",
-   "fsdp":"[]",
-   "fsdp_min_num_params":0,
-   "deepspeed":"ds_config_zero2_bf16.json",
-   "label_smoothing_factor":0.0,
-   "optim":"adamw_hf",
-   "adafactor":false,
-   "group_by_length":false,
-   "length_column_name":"length",
-   "report_to":"['tensorboard']",
-   "ddp_find_unused_parameters":"None",
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-   "hub_strategy":"end",
-   "hub_token":"<HUB_TOKEN>",
-   "hub_private_repo":false,
-   "gradient_checkpointing":true,
-   "include_inputs_for_metrics":false,
-   "fp16_backend":"auto",
-   "push_to_hub_model_id":"None",
-   "push_to_hub_organization":"None",
-   "push_to_hub_token":"<PUSH_TO_HUB_TOKEN>",
-   "_n_gpu":1,
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-   "train_batch_size":8,
-   "eval_batch_size":8,
-   "configs_src":"opt-2pt7b-ps_DS-msgs_Ep-3_Bs-8",
-   "data_tag":"text-file-input"
-}
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opt-peter-2pt7B-ps_DS-msgs_Ep-4_Bs-16_training_metadata.json

@@ -1,101 +0,0 @@
-{
-   "output_dir":"/content/drive/MyDrive/Programming/hf-trainer/opt-peter-2pt7B-ps_DS-msgs_Ep-4_Bs-16",
-   "overwrite_output_dir":true,
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-   "per_gpu_eval_batch_size":"None",
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-   "adam_beta2":0.999,
-   "adam_epsilon":1e-08,
-   "max_grad_norm":1,
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-   "warmup_ratio":0.05,
-   "warmup_steps":0,
-   "log_level":-1,
-   "log_level_replica":-1,
-   "log_on_each_node":true,
-   "logging_dir":"/content/drive/MyDrive/Programming/hf-trainer/opt-peter-2pt7B-ps_DS-msgs_Ep-4_Bs-16/logs",
-   "logging_strategy":"steps",
-   "logging_first_step":false,
-   "logging_steps":5,
-   "logging_nan_inf_filter":true,
-   "save_strategy":"epoch",
-   "save_steps":500,
-   "save_total_limit":1,
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-   "seed":42,
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-   "fp16_opt_level":"O1",
-   "half_precision_backend":"amp",
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-   "hub_model_id":"opt-peter-2pt7B-ps_DS-msgs_Ep-4_Bs-16",
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-   "hub_token":"<HUB_TOKEN>",
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-   "data_tag":"text-file-input"
-}

pytorch_model.bin

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tokenizer_config.json

@@ -1 +1 @@
-{"unk_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "errors": "replace", "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_bos_token": true, "special_tokens_map_file": null, "name_or_path": "facebook/opt-2.7b", "model_max_length": 512}
+{"unk_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "errors": "replace", "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_bos_token": true, "special_tokens_map_file": null, "name_or_path": "facebook/opt-2.7b", "model_max_length": 512}

trainer_state.json

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 {
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   "is_world_process_zero": true,
   "log_history": [
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     }
   ],
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-  "num_train_epochs": 4,
-  "total_flos": 6.117066158416855e+17,
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+  "num_train_epochs": 3,
+  "total_flos": 1.8340911973547377e+18,
   "trial_name": null,
   "trial_params": null
 }

training_args.bin

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+size 4207

zero_to_fp32.py

@@ -0,0 +1,484 @@
+#!/usr/bin/env python
+
+# This script extracts fp32 consolidated weights from a zero 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
+
+# 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.
+import deepspeed
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION,
+                                            OPTIMIZER_STATE_DICT,
+                                            PARAM_SHAPES,
+                                            SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS,
+                                            ZERO_STAGE,
+                                            PARTITION_COUNT,
+                                            PARAM_SHAPES,
+                                            BUFFER_NAMES)
+
+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_optim_files(checkpoint_dir):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    optim_files = sorted(glob.glob(os.path.join(checkpoint_dir,
+                                                "*_optim_states.pt")),
+                         key=natural_keys)
+
+    if len(optim_files) == 0:
+        raise FileNotFoundError(
+            f"can't find '*_optim_states.pt' files in directory '{checkpoint_dir}'")
+
+    return optim_files
+
+
+def parse_model_state(file):
+    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]
+
+    ds_version = state_dict.get(DS_VERSION, None)
+
+    return buffers, param_shapes, ds_version
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+
+    total_files = len(files)
+    state_dicts = []
+    for f in files:
+        state_dicts.append(torch.load(f, map_location=device))
+
+    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):
+    """
+    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_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
+    buffers, param_shapes, ds_version = parse_model_state(model_file)
+    print(f'Parsing checkpoint created by deepspeed=={ds_version}')
+
+    if zero_stage == 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size,
+                                                          param_shapes,
+                                                          fp32_flat_groups,
+                                                          buffers)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size,
+                                                          param_shapes,
+                                                          fp32_flat_groups,
+                                                          buffers)
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
+                                               param_shapes,
+                                               fp32_flat_groups,
+                                               buffers):
+
+    # 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.")
+
+    state_dict = OrderedDict()
+
+    # buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    # 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()
+            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"
+    )
+
+    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 _get_fp32_state_dict_from_zero3_checkpoint(world_size,
+                                               param_shapes,
+                                               fp32_flat_groups,
+                                               buffers):
+
+    # 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
+
+    avail_numel = fp32_flat_groups[0].numel() * world_size
+    # 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
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    state_dict = OrderedDict()
+
+    # buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    # 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"{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 fp32 state dict with {total_params} params {total_numel} elements"
+    )
+
+    return state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
+    """
+    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``
+
+    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)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
+    """
+    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``
+    """
+
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+    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("-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)