tlhoang commited on Jul 16, 2025

Commit

da0f61f

1 Parent(s): 24f05f6

clean up

Browse files

Files changed (20) hide show

Example.ipynb +0 -291
README.md +0 -51
config.json +0 -75
data/vocab_morgan.txt +0 -2670
generation_config.json +0 -13
global_step4120000/mp_rank_00_model_states.pt +0 -3
global_step4120000/zero_pp_rank_0_mp_rank_00_optim_states.pt +0 -3
global_step4120000/zero_pp_rank_1_mp_rank_00_optim_states.pt +0 -3
latest +0 -1
model.safetensors +0 -3
model/__init__.py +0 -0
model/tokenizer.py +0 -325
model/trainer.py +0 -57
model/utils.py +0 -181
rng_state_0.pth +0 -3
rng_state_1.pth +0 -3
train.py +0 -109
trainer_state.json +0 -0
training_args.bin +0 -3
zero_to_fp32.py +0 -587

Example.ipynb DELETED Viewed

@@ -1,291 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "274b013f-9f96-4973-875f-f3910e2789c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "from model.utils import MorganFingerprint, morgan_fingerprint_to_text, clean_output, smiles_to_3d\n",
-    "from model.tokenizer import SmilesTokenizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b8c70b09-8c7d-439b-bd42-4cfbb41715ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "checkpoint_path=\"lamthuy/MorganGen\"\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)\n",
-    "tokenizer = SmilesTokenizer(vocab_file=\"data/vocab_morgan.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9c49a040-13fd-4a63-aef1-6112a42f3eed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[22:30:12] DEPRECATION WARNING: please use MorganGenerator\n"
-     ]
-    }
-   ],
-   "source": [
-    "smiles=\"CC(=O)OC1=CC=CC=C1C(=O)O\"\n",
-    "m = MorganFingerprint()\n",
-    "mf = m.smiles_to_morgan(smiles)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a7be100a-8cdd-48cf-a6cb-5444d793e9c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[389][456][650][695][807][909][1017][1035][1047][1057][1088][1199][1380][1410][1447][1468][1616][1729][1750][1775][1873][1917][1970][1991]\n"
-     ]
-    }
-   ],
-   "source": [
-    "s = morgan_fingerprint_to_text(mf)\n",
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6740dcbc-86a4-4b32-a439-91312dd4a9fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_ids = tokenizer.encode(s, return_tensors=\"pt\")\n",
-    "# Generate output sequence\n",
-    "output_ids = model.generate(input_ids, max_length=64, num_beams=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f423bbe8-5717-4757-84f1-ab6dd5531ea7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clean_output_ids = clean_output(output_ids[0])\n",
-    "# Decode the generated output\n",
-    "output_text = tokenizer.decode(clean_output_ids)\n",
-    "output_text = output_text.replace(\" \", \"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e2678826-caa9-4cb2-bc4d-0ab5e54f76d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CC(=O)Oc1ccc(OC(O)=C(C)O)cc1OC1CC1\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(output_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c4dcff84-4552-48ca-8b2c-8f2d4911ec8a",
-   "metadata": {},
-   "outputs": [
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-    "smiles_to_3d([smiles])"
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-   "execution_count": 9,
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-    "smiles_to_3d([output_text])"
-   ]
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-}

README.md DELETED Viewed

@@ -1,51 +0,0 @@
----
-license: mit
----
-## MorganGen
-A generative model trained on 120 million SMILES strings from the ZINC database. The model takes as input a sequence of indices representing the active bits in a 2048-bit Morgan fingerprint. Each index corresponds to a bit set to 1, while all other bits are 0.
-```
-s = [12][184][1200]
-```
-represents a fingerprint where only bits 12, 184, and 1200 are set to 1, and the remaining bits are 0.
-# Running example
-The following code snippet in the notebook demonstrates how to load the model from a checkpoint and generate a new SMILES string, conditioned on a given input SMILES.
-```python
-from transformers import AutoModelForSeq2SeqLM
-from model.utils import MorganFingerprint, morgan_fingerprint_to_text, clean_output, smiles_to_3d
-from model.tokenizer import SmilesTokenizer
-# Load the checkpoint and the tokenizer
-checkpoint_path="lamthuy/MorganGen"
-model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
-tokenizer = SmilesTokenizer(vocab_file="data/vocab_morgan.txt")
-# Given a SMILES, get its fingerpint
-smiles="CC(=O)OC1=CC=CC=C1C(=O)O"
-m = MorganFingerprint()
-mf = m.smiles_to_morgan(smiles)
-# convert it to the indices text format
-s = morgan_fingerprint_to_text(mf)
-# encode
-input_ids = tokenizer.encode(s, return_tensors="pt")
-# Generate output sequence
-output_ids = model.generate(input_ids, max_length=64, num_beams=5)
-# decode
-clean_output_ids = clean_output(output_ids[0])
-# Decode the generated output
-output_text = tokenizer.decode(clean_output_ids)
-output_text = output_text.replace(" ", "")
-```
-# Reference
-```
-@inproceedings{hoang2024morgangen,
-  title={MorganGen: Generative Modeling of SMILES Using Morgan Fingerprint Features},
-  author={Hoang, Lam Thanh and D{\'\i}az, Ra{\'u}l Fern{\'a}ndez and Lopez, Vanessa},
-  booktitle={American Chemical Society (ACS) Fall Meeting},
-  year={2024}
-}
-```

config.json DELETED Viewed

@@ -1,75 +0,0 @@
-{
-  "_name_or_path": "facebook/bart-base",
-  "activation_dropout": 0.1,
-  "activation_function": "gelu",
-  "add_bias_logits": false,
-  "add_final_layer_norm": false,
-  "architectures": [
-    "BartForConditionalGeneration"
-  ],
-  "attention_dropout": 0.1,
-  "bos_token_id": 0,
-  "classif_dropout": 0.1,
-  "classifier_dropout": 0.0,
-  "d_model": 768,
-  "decoder_attention_heads": 12,
-  "decoder_ffn_dim": 3072,
-  "decoder_layerdrop": 0.0,
-  "decoder_layers": 6,
-  "decoder_start_token_id": 2,
-  "dropout": 0.1,
-  "early_stopping": true,
-  "encoder_attention_heads": 12,
-  "encoder_ffn_dim": 3072,
-  "encoder_layerdrop": 0.0,
-  "encoder_layers": 6,
-  "eos_token_id": 2,
-  "forced_bos_token_id": 0,
-  "forced_eos_token_id": 2,
-  "gradient_checkpointing": false,
-  "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2"
-  },
-  "init_std": 0.02,
-  "is_encoder_decoder": true,
-  "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_2": 2
-  },
-  "max_position_embeddings": 1024,
-  "model_type": "bart",
-  "no_repeat_ngram_size": 3,
-  "normalize_before": false,
-  "normalize_embedding": true,
-  "num_beams": 4,
-  "num_hidden_layers": 6,
-  "pad_token_id": 1,
-  "scale_embedding": false,
-  "task_specific_params": {
-    "summarization": {
-      "length_penalty": 1.0,
-      "max_length": 128,
-      "min_length": 12,
-      "num_beams": 4
-    },
-    "summarization_cnn": {
-      "length_penalty": 2.0,
-      "max_length": 142,
-      "min_length": 56,
-      "num_beams": 4
-    },
-    "summarization_xsum": {
-      "length_penalty": 1.0,
-      "max_length": 62,
-      "min_length": 11,
-      "num_beams": 6
-    }
-  },
-  "torch_dtype": "float16",
-  "transformers_version": "4.37.0",
-  "use_cache": true,
-  "vocab_size": 50265
-}

data/vocab_morgan.txt DELETED Viewed

@@ -1,2670 +0,0 @@
-[PAD]
-[unused1]
-[unused2]
-[unused3]
-[unused4]
-[unused5]
-[unused6]
-[unused7]
-[unused8]
-[unused9]
-[unused10]
-[UNK]
-[CLS]
-[SEP]
-[MASK]
-c
-C
-(
-)
-O
-1
-2
-=
-N
-.
-n
-3
-F
-Cl
->>
-~
--
-4
-[C@H]
-S
-[C@@H]
-[O-]
-Br
-#
-/
-[nH]
-[N+]
-s
-5
-o
-P
-[Na+]
-[Si]
-I
-[Na]
-[Pd]
-[K+]
-[K]
-[P]
-B
-[C@]
-[C@@]
-[Cl-]
-6
-[OH-]
-\
-[N-]
-[Li]
-[H]
-[2H]
-[NH4+]
-[c-]
-[P-]
-[Cs+]
-[Li+]
-[Cs]
-[NaH]
-[H-]
-[O+]
-[BH4-]
-[Cu]
-7
-[Mg]
-[Fe+2]
-[n+]
-[Sn]
-[BH-]
-[Pd+2]
-[CH]
-[I-]
-[Br-]
-[C-]
-[Zn]
-[B-]
-[F-]
-[Al]
-[P+]
-[BH3-]
-[Fe]
-[C]
-[AlH4]
-[Ni]
-[SiH]
-8
-[Cu+2]
-[Mn]
-[AlH]
-[nH+]
-[AlH4-]
-[O-2]
-[Cr]
-[Mg+2]
-[NH3+]
-[S@]
-[Pt]
-[Al+3]
-[S@@]
-[S-]
-[Ti]
-[Zn+2]
-[PH]
-[NH2+]
-[Ru]
-[Ag+]
-[S+]
-[I+3]
-[NH+]
-[Ca+2]
-[Ag]
-9
-[Os]
-[Se]
-[SiH2]
-[Ca]
-[Ti+4]
-[Ac]
-[Cu+]
-[S]
-[Rh]
-[Cl+3]
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-%10
-[CH2-]
-[CH2]
-[n-]
-[Ce+4]
-[NH-]
-[Co]
-[I+]
-[PH2]
-[Pt+4]
-[Ce]
-[B]
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-[Ba+2]
-%11
-[Fe-3]
-[18F]
-[SH-]
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-[Zr+4]
-[N]
-[Ir]
-[Bi]
-[Ni+2]
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-[Zr]
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-[Ti+3]
-[Rh+2]
-[Sb]
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-%12
-[P@@]
-[Si@H]
-[Mn+4]
-p
-[Ba]
-[NH2-]
-[Ge]
-[Pb+4]
-[Cr+3]
-[Au]
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-%13
-[Br]
-[Sb-]
-[S@+]
-[I+2]
-[Ar]
-[V]
-[Cu-]
-[Al-]
-[Te]
-[13c]
-[13C]
-[Cl]
-[PH4+]
-[SiH4]
-[te]
-[CH3-]
-[S@@+]
-[Rh+3]
-[SH+]
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-[Br+2]
-[La]
-[La+3]
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-[N@]
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-[Sm]
-*
-[3H]
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-[Sn+4]
-[Hg+]
-[11CH3]
-[Hf]
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-[Cd]
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-[CH3]
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-%14
-[Cr+2]
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-[Hf+4]
-[14C]
-[Ta]
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-[123I]
-[c+]
-[Rb+]
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-%15
-[OH3+]
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-%16
-[N@@+]
-[Au+3]
-[C-4]
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-%17
-[SnH2]
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-[19F]
-%18
-[Tb]
-[14CH]
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-[BH]
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-%19
-%20
-[Xe]
-[Ir-]
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-[U+2]
-[Au-]
-%21
-%22
-[Au+]
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-b
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-[14cH]
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-[AsH3]
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-[17]
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-[19]
-[20]
-[21]
-[22]
-[23]
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generation_config.json DELETED Viewed

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-{
-  "_from_model_config": true,
-  "bos_token_id": 0,
-  "decoder_start_token_id": 2,
-  "early_stopping": true,
-  "eos_token_id": 2,
-  "forced_bos_token_id": 0,
-  "forced_eos_token_id": 2,
-  "no_repeat_ngram_size": 3,
-  "num_beams": 4,
-  "pad_token_id": 1,
-  "transformers_version": "4.37.0"
-}

global_step4120000/mp_rank_00_model_states.pt DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:21d1573828b5ea09d93b840a2d0fddc4ff050baed01d8e418d5a108a4ca2abb5
-size 279011960

global_step4120000/zero_pp_rank_0_mp_rank_00_optim_states.pt DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4124c72760850e3dfe47ef6271cf52c342902ddf4895ab2af48c1cc85df94b7b
-size 836532205

global_step4120000/zero_pp_rank_1_mp_rank_00_optim_states.pt DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b99a8df8657f8304bd84ea3872b48ab441afd17d7dc7a6beb9462944a1769b15
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latest DELETED Viewed

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1	- global_step4120000

model.safetensors DELETED Viewed

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-version https://git-lfs.github.com/spec/v1
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-size 510592834

model/__init__.py DELETED Viewed

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model/tokenizer.py DELETED Viewed

@@ -1,325 +0,0 @@
-# Requriments - transformers, tokenizers
-# Right now, the Smiles Tokenizer uses an exiesting vocab file from rxnfp that is fairly comprehensive and from the USPTO dataset.
-# The vocab may be expanded in the near future
-import collections
-import os
-import re
-import pkg_resources
-from typing import List
-from transformers import BertTokenizer
-from logging import getLogger
-from model.utils import get_atoms_from_smiles
-logger = getLogger(__name__)
-"""
-SMI_REGEX_PATTERN: str
-    SMILES regex pattern for tokenization. Designed by Schwaller et. al.
-References
-----------
-.. [1]  Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee
-        ACS Central Science 2019 5 (9): Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
-        1572-1583 DOI: 10.1021/acscentsci.9b00576
-"""
-SMI_REGEX_PATTERN = r"""(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])"""
-# add vocab_file dict
-VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
-def get_default_tokenizer():
-  default_vocab_path = (pkg_resources.resource_filename("deepchem",
-                                                        "feat/tests/vocab.txt"))
-  return SmilesTokenizer(default_vocab_path)
-class SmilesTokenizer(BertTokenizer):
-  """
-  Creates the SmilesTokenizer class. The tokenizer heavily inherits from the BertTokenizer
-  implementation found in Huggingface's transformers library. It runs a WordPiece tokenization
-  algorithm over SMILES strings using the tokenisation SMILES regex developed by Schwaller et. al.
-  Please see https://github.com/huggingface/transformers
-  and https://github.com/rxn4chemistry/rxnfp for more details.
-  Examples
-  --------
-  >>> from deepchem.feat.smiles_tokenizer import SmilesTokenizer
-  >>> current_dir = os.path.dirname(os.path.realpath(__file__))
-  >>> vocab_path = os.path.join(current_dir, 'tests/data', 'vocab.txt')
-  >>> tokenizer = SmilesTokenizer(vocab_path)
-  >>> print(tokenizer.encode("CC(=O)OC1=CC=CC=C1C(=O)O"))
-  [12, 16, 16, 17, 22, 19, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 16, 17, 22, 19, 18, 19, 13]
-  References
-  ----------
-  .. [1] Schwaller, Philippe; Probst, Daniel; Vaucher, Alain C.; Nair, Vishnu H; Kreutter, David;
-     Laino, Teodoro; et al. (2019): Mapping the Space of Chemical Reactions using Attention-Based Neural
-     Networks. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.9897365.v3
-  Note
-  ----
-  This class requires huggingface's transformers and tokenizers libraries to be installed.
-  """
-  vocab_files_names = VOCAB_FILES_NAMES
-  def __init__(
-      self,
-      vocab_file: str = '',
-      # unk_token="[UNK]",
-      # sep_token="[SEP]",
-      # pad_token="[PAD]",
-      # cls_token="[CLS]",
-      # mask_token="[MASK]",
-      **kwargs):
-    """Constructs a SmilesTokenizer.
-    Parameters
-    ----------
-    vocab_file: str
-      Path to a SMILES character per line vocabulary file.
-      Default vocab file is found in deepchem/feat/tests/data/vocab.txt
-    """
-    super().__init__(vocab_file, **kwargs)
-    # take into account special tokens in max length
-    # self.max_len_single_sentence = self.model_max_length - 2
-    # self.max_len_sentences_pair = self.model_max_length - 3
-    if not os.path.isfile(vocab_file):
-      raise ValueError(
-          "Can't find a vocab file at path '{}'.".format(vocab_file))
-    self.vocab = load_vocab(vocab_file)
-    self.highest_unused_index = max(
-        [i for i, v in enumerate(self.vocab.keys()) if v.startswith("[unused")])
-    self.ids_to_tokens = collections.OrderedDict(
-        [(ids, tok) for tok, ids in self.vocab.items()])
-    self.basic_tokenizer = BasicSmilesTokenizer()
-    self.init_kwargs["model_max_length"] = self.model_max_length
-  @property
-  def vocab_size(self):
-    return len(self.vocab)
-  @property
-  def vocab_list(self):
-    return list(self.vocab.keys())
-  def _tokenize(self, text: str):
-    """Tokenize a string into a list of tokens.
-    Parameters
-    ----------
-    text: str
-      Input string sequence to be tokenized.
-    """
-    split_tokens = [str(token[1]) for token in get_atoms_from_smiles(text)]
-    return split_tokens
-  @staticmethod
-  def get_atom_indices(text):
-      atoms = get_atoms_from_smiles(text)
-      indices = []
-      for i, a in enumerate(atoms):
-          if a[0] == 'ATOM':
-              indices.append(i)
-      return indices
-  def _convert_token_to_id(self, token: str):
-    """Converts a token (str/unicode) in an id using the vocab.
-    Parameters
-    ----------
-    token: str
-      String token from a larger sequence to be converted to a numerical id.
-    """
-    return self.vocab.get(token, self.vocab.get(self.unk_token))
-  def _convert_id_to_token(self, index: int):
-    """Converts an index (integer) in a token (string/unicode) using the vocab.
-    Parameters
-    ----------
-    index: int
-      Integer index to be converted back to a string-based token as part of a larger sequence.
-    """
-    return self.ids_to_tokens.get(index, self.unk_token)
-  def convert_tokens_to_string(self, tokens: List[str]):
-    """Converts a sequence of tokens (string) in a single string.
-    Parameters
-    ----------
-    tokens: List[str]
-      List of tokens for a given string sequence.
-    Returns
-    -------
-    out_string: str
-      Single string from combined tokens.
-    """
-    out_string: str = " ".join(tokens).replace(" ##", "").strip()
-    return out_string
-  def add_special_tokens_ids_single_sequence(self, token_ids: List[int]):
-    """Adds special tokens to the a sequence for sequence classification tasks.
-    A BERT sequence has the following format: [CLS] X [SEP]
-    Parameters
-    ----------
-    token_ids: list[int]
-      list of tokenized input ids. Can be obtained using the encode or encode_plus methods.
-    """
-    return [self.cls_token_id] + token_ids + [self.sep_token_id]
-  def add_special_tokens_single_sequence(self, tokens: List[str]):
-    """Adds special tokens to the a sequence for sequence classification tasks.
-    A BERT sequence has the following format: [CLS] X [SEP]
-    Parameters
-    ----------
-    tokens: List[str]
-      List of tokens for a given string sequence.
-    """
-    return [self.cls_token] + tokens + [self.sep_token]
-  def add_special_tokens_ids_sequence_pair(self, token_ids_0: List[int],
-                                           token_ids_1: List[int]) -> List[int]:
-    """Adds special tokens to a sequence pair for sequence classification tasks.
-    A BERT sequence pair has the following format: [CLS] A [SEP] B [SEP]
-    Parameters
-    ----------
-    token_ids_0: List[int]
-      List of ids for the first string sequence in the sequence pair (A).
-    token_ids_1: List[int]
-      List of tokens for the second string sequence in the sequence pair (B).
-    """
-    sep = [self.sep_token_id]
-    cls = [self.cls_token_id]
-    return cls + token_ids_0 + sep + token_ids_1 + sep
-  def add_padding_tokens(self,
-                         token_ids: List[int],
-                         length: int,
-                         right: bool = True) -> List[int]:
-    """Adds padding tokens to return a sequence of length max_length.
-    By default padding tokens are added to the right of the sequence.
-    Parameters
-    ----------
-    token_ids: list[int]
-      list of tokenized input ids. Can be obtained using the encode or encode_plus methods.
-    length: int
-      TODO
-    right: bool, default True
-      TODO
-    Returns
-    -------
-    List[int]
-      TODO
-    """
-    padding = [self.pad_token_id] * (length - len(token_ids))
-    if right:
-      return token_ids + padding
-    else:
-      return padding + token_ids
-  def save_vocabulary(
-      self, vocab_path: str
-  ):  # -> tuple[str]: doctest issue raised with this return type annotation
-    """Save the tokenizer vocabulary to a file.
-    Parameters
-    ----------
-    vocab_path: obj: str
-      The directory in which to save the SMILES character per line vocabulary file.
-      Default vocab file is found in deepchem/feat/tests/data/vocab.txt
-    Returns
-    -------
-    vocab_file: Tuple
-      Paths to the files saved.
-      typle with string to a SMILES character per line vocabulary file.
-      Default vocab file is found in deepchem/feat/tests/data/vocab.txt
-    """
-    index = 0
-    if os.path.isdir(vocab_path):
-      vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"])
-    else:
-      vocab_file = vocab_path
-    with open(vocab_file, "w", encoding="utf-8") as writer:
-      for token, token_index in sorted(
-          self.vocab.items(), key=lambda kv: kv[1]):
-        if index != token_index:
-          logger.warning(
-              "Saving vocabulary to {}: vocabulary indices are not consecutive."
-              " Please check that the vocabulary is not corrupted!".format(
-                  vocab_file))
-          index = token_index
-        writer.write(token + "\n")
-        index += 1
-    return (vocab_file,)
-class BasicSmilesTokenizer(object):
-  """
-  Run basic SMILES tokenization using a regex pattern developed by Schwaller et. al.
-  This tokenizer is to be used when a tokenizer that does not require the transformers library by HuggingFace is required.
-  Examples
-  --------
-  >>> from deepchem.feat.smiles_tokenizer import BasicSmilesTokenizer
-  >>> tokenizer = BasicSmilesTokenizer()
-  >>> print(tokenizer.tokenize("CC(=O)OC1=CC=CC=C1C(=O)O"))
-  ['C', 'C', '(', '=', 'O', ')', 'O', 'C', '1', '=', 'C', 'C', '=', 'C', 'C', '=', 'C', '1', 'C', '(', '=', 'O', ')', 'O']
-  References
-  ----------
-  .. [1] Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee
-     ACS Central Science 2019 5 (9): Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
-     1572-1583 DOI: 10.1021/acscentsci.9b00576
-  """
-  def __init__(self, regex_pattern: str = SMI_REGEX_PATTERN):
-    """Constructs a BasicSMILESTokenizer.
-    Parameters
-    ----------
-    regex: string
-        SMILES token regex
-    """
-    self.regex_pattern = regex_pattern
-    self.regex = re.compile(self.regex_pattern)
-  def tokenize(self, text):
-    """Basic Tokenization of a SMILES.
-    """
-    tokens = [token for token in self.regex.findall(text)]
-    return tokens
-def load_vocab(vocab_file):
-  """Loads a vocabulary file into a dictionary."""
-  vocab = collections.OrderedDict()
-  with open(vocab_file, "r", encoding="utf-8") as reader:
-    tokens = reader.readlines()
-  for index, token in enumerate(tokens):
-    token = token.rstrip("\n")
-    vocab[token] = index
-  return vocab

model/trainer.py DELETED Viewed

@@ -1,57 +0,0 @@
-from typing import Optional, Dict, Union, Any, List, Tuple
-from transformers import Trainer
-import torch
-from torch import nn
-from torch.utils.data import DataLoader, Dataset
-class CustomTrainer(Trainer):
-    def __init__(self, **kwargs):
-        self.num_chunks = kwargs.pop("num_chunks")
-        self.max_length = kwargs.pop("max_length")
-        self.my_tokenizer = kwargs.pop("my_tokenizer")
-        super(CustomTrainer, self).__init__(**kwargs)
-        print(f"Using device: {self.args.device}")
-    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
-        return super(CustomTrainer, self).training_step(model, inputs)
-    def prediction_step(
-            self,
-            model: nn.Module,
-            inputs: Dict[str, Union[torch.Tensor, Any]],
-            prediction_loss_only: bool,
-            ignore_keys: Optional[List[str]] = None,
-    ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
-        return super(CustomTrainer, self).prediction_step(model, inputs, prediction_loss_only, ignore_keys)
-    def get_train_dataloader(self):
-        train_dataloader = DataLoader(self.train_dataset,
-                                      batch_size=self.args.per_device_train_batch_size,
-                                      # num_workers=0,
-                                      pin_memory=True)
-        return train_dataloader
-    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
-        if not eval_dataset:
-            eval_dataset = self.eval_dataset
-        validation_dataloader = DataLoader(eval_dataset,
-                                           batch_size=self.args.per_device_eval_batch_size,
-                                           # num_workers=0,
-                                           pin_memory=True,
-                                           # shuffle=False
-                                           )
-        return validation_dataloader
-    def compute_loss(self, model, inputs, return_outputs=False):
-        """
-        How the loss is computed by Trainer. By default, all models return the loss in the first element.
-        Subclass and override for custom behavior.
-        """
-        outputs = model(**inputs)
-        if self.state.global_step % 501 == 0:
-            print({'loss': torch.mean(outputs['loss']).item(),
-                   'steps': self.state.global_step})
-        loss = outputs['loss']
-        return (loss, outputs) if return_outputs else loss

model/utils.py DELETED Viewed

@@ -1,181 +0,0 @@
-from typing import Optional
-import numpy as np
-import py3Dmol
-from rdkit import Chem, DataStructs
-from rdkit.Chem import AllChem
-import torch
-class MorganFingerprint:
-    def __init__(self, shape: Optional[int] = 2048, radius: Optional[int] = 2):
-        self.shape = shape
-        self.radius = radius
-    @staticmethod
-    def canonicalize(smiles):
-        mol = Chem.MolFromSmiles(smiles)
-        if mol is not None:
-            return Chem.MolToSmiles(mol, isomericSmiles=True)
-        else:
-            return smiles
-    def smiles_to_morgan(self, smile: str) -> torch.Tensor:
-        try:
-            smile = self.canonicalize(smile)
-            mol = Chem.MolFromSmiles(smile)
-            features_vec = AllChem.GetMorganFingerprintAsBitVect(
-                mol, self.radius, nBits=self.shape
-            )
-            features = np.zeros((1,))
-            DataStructs.ConvertToNumpyArray(features_vec, features)
-        except Exception as e:
-            features = np.zeros((self.shape,))
-        return torch.tensor(features, dtype=torch.float32)
-def get_morgan(input_sequences):
-    m = MorganFingerprint()
-    morgans = []
-    for s in input_sequences:
-        r = m.smiles_to_morgan(s)
-        indices_of_ones = torch.nonzero(r == 1.0, as_tuple=False)
-        indices_of_ones = indices_of_ones.squeeze(-1)
-        indices_of_ones = indices_of_ones.tolist()
-        s = ""
-        for i in indices_of_ones:
-            s += "[" + str(i) + "]"
-        morgans.append(s)
-    return morgans
-def prepare_input_and_labels_morgan(tokenizer, input_sequences, max_length):
-    outputs = {}
-    batch_size = len(input_sequences)
-    morgans = get_morgan(input_sequences)
-    input_sequences_morgans = input_sequences + morgans
-    inputs = tokenizer.batch_encode_plus(input_sequences_morgans, max_length=max_length, padding='max_length',
-                                         return_tensors='pt', truncation=True)
-    smiles_ids = inputs['input_ids'][:batch_size]
-    smiles_ids = torch.where(smiles_ids == 0, -100, smiles_ids)
-    morgan_ids = inputs['input_ids'][batch_size:]
-    morgan_attention_mask = inputs['attention_mask'][batch_size:]
-    outputs['labels'] = smiles_ids
-    outputs['input_ids'] = morgan_ids
-    outputs['attention_mask'] = morgan_attention_mask
-    return outputs
-def get_atoms_from_smiles(smiles):
-    """
-    Iterates over a SMILES string, yielding tokens and offsets
-    Parameters
-    ----------
-    smiles : iterable
-        The SMILES string to iterate over
-    Yields
-    ------
-    tuple(TokenType, str, int)
-        A tuple describing the type of token and the associated data and offset in the smiles string
-    """
-    organic_subset = 'B C N O P S F Cl Br I * b c n o s p'.split()
-    s = smiles
-    smiles = iter(smiles)
-    token = ''
-    peek = None
-    offset = -1
-    atoms = []
-    while True:
-        if peek:
-            char = peek
-        else:
-            char = next(smiles, '')
-            offset += 1
-        peek = None
-        if not char:
-            break
-        if char == '[':
-            token = char
-            move = 0
-            for char in smiles:
-                move += 1
-                token += char
-                if char == ']':
-                    break
-            atoms.append(('ATOM', token, offset))
-            offset += move
-        elif char in organic_subset:
-            peek = next(smiles, '')
-            if char + peek in organic_subset:
-                atoms.append(('ATOM', char + peek, offset))
-                peek = None
-            else:
-                atoms.append(('ATOM', char, offset))
-            offset += 1
-        elif char in '-=#$:.':
-            atoms.append(('BOND_TYPE', char, offset))
-        elif char == '(':
-            atoms.append(('BRANCH_START', '(', offset))
-        elif char == ')':
-            atoms.append(('BRANCH_END', ')', offset))
-        elif char == '%':
-            # If smiles is too short this will raise a ValueError, which is
-            # (slightly) prettier than a StopIteration.
-            atoms.append(('RING_NUM', int(next(smiles, '') + next(smiles, '')), offset + 1))
-            offset += 2
-        elif char in '/\\':
-            atoms.append(('EZSTEREO', char, offset))
-        elif char.isdigit():
-            atoms.append(('RING_NUM', int(char), offset))
-    for _, a, offset in atoms:
-        assert str(a) == s[offset: (offset + len(str(a)))]
-    return atoms
-def clean_output(output_ids):
-    clean_output_ids = []
-    start = False
-    for i in output_ids:
-        if i == 13:
-           break
-        if start:
-            if i > 14:
-                clean_output_ids.append(i)
-        if i == 0:
-            start = True
-    return clean_output_ids
-def morgan_fingerprint_to_text(morgan_fn):
-        indices_of_ones = torch.nonzero(morgan_fn == 1.0, as_tuple=False)
-        indices_of_ones = indices_of_ones.squeeze(-1)
-        indices_of_ones = indices_of_ones.tolist()
-        s = ""
-        for i in indices_of_ones:
-            s += "[" + str(i) + "]"
-        return s
-def smiles_to_3d(smiles_list, width=400, height=300):
-    # Visualize the 3D structure using py3Dmol
-    view = py3Dmol.view(width=width, height=height)
-    for smiles in smiles_list:
-        # Generate the RDKit molecule object
-        mol = Chem.MolFromSmiles(smiles)
-        if mol is None:
-            raise ValueError("Invalid SMILES string")
-        # Add hydrogens to the molecule
-        mol = Chem.AddHs(mol)
-        # Generate 3D coordinates
-        AllChem.EmbedMolecule(mol, randomSeed=42)
-        AllChem.UFFOptimizeMolecule(mol)
-        # Generate the 3D structure in the form of a pdb string
-        pdb = Chem.MolToPDBBlock(mol)
-        view.addModel(pdb, 'pdb')
-        view.setStyle({'stick': {}})
-        view.zoomTo()
-    return view

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@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6cd095dfb9a400482d1cbe7aeb660e1635d4c46839d11b4c9fd38b3d26db3672
-size 14512

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-version https://git-lfs.github.com/spec/v1
-oid sha256:92852906cdd6fb428f8147e3fdaadeb026c6cce1f3a0bf56c0c7593ab725edb5
-size 14512

train.py DELETED Viewed

@@ -1,109 +0,0 @@
-import argparse
-from transformers import TrainingArguments, IntervalStrategy, EarlyStoppingCallback, AutoModelForSeq2SeqLM
-from model.tokenizer import SmilesTokenizer
-from model.trainer import CustomTrainer
-from datasets import load_dataset
-from model.utils import prepare_input_and_labels_morgan
-import glob2
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='AntBrain training')
-    parser.add_argument(
-        '-t', '--train', default='./data/train_graph.jsonl.gz',
-        type=str,
-        help='Root directory with the training data')
-    parser.add_argument(
-        '-v', '--validation', default='./data/train_graph.jsonl.gz', type=str,
-        help='Root directory with the validation data')
-    parser.add_argument("--learning-rate", default=3e-05, type=float)
-    parser.add_argument("--per-device-train-batch-size", default=8, type=int)
-    parser.add_argument("--per-device-eval-batch-size", default=8, type=int)
-    parser.add_argument("--weight-decay", default=0.01, type=float)
-    parser.add_argument("--epochs", default=5, type=int)
-    parser.add_argument("--save-total-limit", default=3, type=int)
-    parser.add_argument("--saving_steps", default=1000, type=int)
-    parser.add_argument("--evaluation_steps", default=1, type=int)
-    parser.add_argument("--adam-eps", default=1e-08, type=float)
-    parser.add_argument("--adam-betas", default=(0.9, 0.999), nargs="+", type=float)
-    parser.add_argument("--warmup-updates", default=500, type=int)
-    parser.add_argument("--warmup_steps", default=500, type=int)
-    parser.add_argument("--max_steps", default=500, type=int)
-    parser.add_argument("--patience", default=200, type=int)
-    parser.add_argument("--num_workers", default=10, type=int)
-    parser.add_argument("--logging-steps", default=1, type=int)
-    parser.add_argument("--fp16", action='store_true')
-    parser.add_argument("--deepspeed", default=None, type=str, help="Deep speed configuration file")
-    parser.add_argument("--local_rank", type=int, default=-1)
-    parser.add_argument('-m', '--model', type=str, default='facebook/bart-base', help='model name')
-    parser.add_argument('--max_length', type=int, default=128, help='Max sequence length')
-    parser.add_argument('--num_chunks', type=int, default=2, help='number of chunks per training step')
-    parser.add_argument('--vocab_path', type=str, default="./data/vocab_morgan.txt", help='vocab file path.')
-    parser.add_argument('--output_dir', type=str, default="./results", help='output dir where the models are saved')
-    parser.add_argument('--checkpoint', type=str, default=None, help='Path to the check point.')
-    parser.add_argument('--ignore_data_skip', type=str, default='yes', help='whether skip checking data before training '
-                                                                           'from checkpoint')
-    args = parser.parse_args()
-    tokenizer = SmilesTokenizer(vocab_file=args.vocab_path)
-    # DataLoaders
-    file_lists = args.train.replace("'", "").split(",")
-    train_files = []
-    for file_list in file_lists:
-        train_files += glob2.glob(file_list)
-    train_files.sort()
-    print("Training data")
-    for file in train_files:
-        print(file)
-    validation_files = glob2.glob(args.validation.replace("'", ""))
-    validation_files.sort()
-    print("Validation data", validation_files)
-    data_set = load_dataset('json',
-                            data_files={'train': train_files,
-                                        'val': validation_files},
-                            streaming=True)
-    data_set = data_set.map(lambda e: prepare_input_and_labels_morgan(tokenizer=tokenizer, input_sequences=e['smiles'],
-                                                                      max_length=args.max_length),
-                            batched=True,
-                            remove_columns=["atom", "smiles", "bond_edges", "bond_types"],
-                            batch_size=100)
-    data_set = data_set.with_format('torch')
-    model = AutoModelForSeq2SeqLM.from_pretrained(args.model)
-    training_args = TrainingArguments(
-        output_dir=args.output_dir,
-        evaluation_strategy=IntervalStrategy.STEPS,
-        learning_rate=args.learning_rate,
-        per_device_train_batch_size=args.per_device_train_batch_size,
-        per_device_eval_batch_size=args.per_device_eval_batch_size,
-        weight_decay=args.weight_decay,
-        save_total_limit=args.save_total_limit,
-        save_steps=args.saving_steps,
-        eval_steps=args.evaluation_steps,
-        num_train_epochs=args.epochs,
-        logging_steps=args.logging_steps,
-        fp16=args.fp16,
-        dataloader_num_workers=args.num_workers,
-        load_best_model_at_end=True,
-        deepspeed=args.deepspeed,
-        max_steps=args.max_steps,
-        warmup_steps=args.warmup_steps,
-        ignore_data_skip=args.ignore_data_skip == 'yes'
-    )
-    trainer = CustomTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=data_set['train'],
-        eval_dataset=data_set['val'],
-        num_chunks=args.num_chunks,
-        my_tokenizer=tokenizer,
-        max_length=args.max_length,
-        callbacks=[EarlyStoppingCallback(early_stopping_patience=args.patience)]
-    )
-    if args.checkpoint:
-        trainer.train(args.checkpoint)
-    else:
-        trainer.train()
-    trainer.evaluate()

trainer_state.json DELETED Viewed

The diff for this file is too large to render. See raw diff

training_args.bin DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5b2db6ec78a7157dbeee04114a5472abebf66b70394acaffb21a3238de5d390e
-size 6200

zero_to_fp32.py DELETED Viewed

@@ -1,587 +0,0 @@
-#!/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):
-    """
-    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)
-    elif zero_stage == 3:
-        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-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 _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()
-            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):
-    state_dict = OrderedDict()
-    # buffers
-    buffers = zero_model_states[0].buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-    _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):
-    state_dict = OrderedDict()
-    # buffers
-    buffers = zero_model_states[0].buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-    _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):
-    """
-    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("-t",
-                        "--tag",
-                        type=str,
-                        default=None,
-                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
-    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)