Merge pull request #15 from Bitbol-Lab/proteingym_branch

Fix number of issues with version, add docker etc

Author: CyrilMa

Dockerfile

@@ -0,0 +1,24 @@
+# Using an official Python runtime with CUDA support as a parent image (https://hub.docker.com/r/nvidia/cuda/)
+FROM pytorch/pytorch:2.3.1-cuda11.8-cudnn8-devel as base
+
+ENV HOST docker
+ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
+ENV TZ Europe/Paris
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+
+# TO CHANGE IF NEEDED
+RUN useradd -m -U -s /bin/bash user
+RUN apt-get update && apt-get install -y git
+
+# Set the working directory: TO CHANGE IF NEEDED
+USER user
+
+# Disable pip cache: https://stackoverflow.com/questions/45594707/what-is-pips-no-cache-dir-good-for
+ENV PIP_NO_CACHE_DIR=1
+#install from requirements.txt
+COPY requirements.txt /home/user/requirements.txt
+
+#apt get git
+RUN pip install --no-cache-dir -r /home/user/requirements.txt
+
+WORKDIR /home/user

ProtMamba_ssm/__init__.py

@@ -1,5 +1,4 @@
 __version__ = "0.0.1"
-from .core import *
 from .dataloaders import *
 from .fim import *
 from .modules import *

ProtMamba_ssm/_modidx.py

@@ -1,9 +1,3 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/03_dataloaders.ipynb.
-
-# %% auto 0
-__all__ = ['Uniclust30_Dataset', 'make_dataloader', 'DataCollatorForUniclust30Dataset']
-
-# %% ../nbs/03_dataloaders.ipynb 3
 from .utils import AA_TO_ID
 from .fim import NoFIM, SingleSpanFIM, MultipleSpanFIM
 import pickle
@@ -14,8 +8,6 @@
 from dataclasses import dataclass
 from typing import Dict, Sequence
 
-
-# %% ../nbs/03_dataloaders.ipynb 4
 # Make dataset
 class Uniclust30_Dataset(Dataset):
     """
@@ -78,14 +70,14 @@ def __init__(self, filename="encoded_MSAs_train.pkl",
     def __len__(self):
         return len(self.cluster_names)
 
-    def __getitem__(self, idx):
+    def __getitem__(self, idx, shuffle=True):
         # get all the sequences in the cluster
         sequences = self.get_sequences(idx)
         # get total number of sequences in the cluster and choose how many to sample
         orig_num_sequences = len(self.get_index_start_of_sequences(sequences))
         num_sequences = np.random.randint(1, orig_num_sequences + 1) if self.sample else orig_num_sequences
         # sample the sequences
-        sequences, position_ids = self.sample_sequences(sequences, num_sequences)
+        sequences, position_ids = self.sample_sequences(sequences, num_sequences, shuffle=shuffle)
         # with probability 0.5, reverse the sequences and move the last token to the front
         sequences, position_ids = self.reverse_sequences(sequences, position_ids) if (
                 self.reverse and np.random.rand() > 0.5) else sequences, position_ids
@@ -128,7 +120,7 @@ def reverse_sequences(self, sequence, position_ids=None):
         return np.concatenate([sequence[-1:], sequence[:-1]]), np.concatenate(
             [position_ids[-1:], position_ids[:-1]]) if position_ids is not None else None
 
-    def sample_sequences(self, sequences, num_sequences, shuffle=True):
+    def sample_sequences(self, sequences, num_sequences, shuffle=True, which_seqs=None):
         """Sample `num_sequences` from the sequences in the cluster."""
         L = len(sequences)
         # get the indexes of the start of each sequence
@@ -137,10 +129,11 @@ def sample_sequences(self, sequences, num_sequences, shuffle=True):
         assert len(inds) > 0, "No sequences found in cluster."
         assert len(inds) >= num_sequences, "Not enough sequences in cluster."
         # sample n_sequences randomly from the sequences
-        if shuffle:
-            which_seqs = np.random.choice(np.arange(len(inds)), num_sequences, replace=False)
-        else:
-            which_seqs = np.arange(len(inds))[-num_sequences:]
+        if which_seqs is None:
+            if shuffle:
+                which_seqs = np.random.choice(np.arange(len(inds)), num_sequences, replace=False)
+            else:
+                which_seqs = np.arange(len(inds))[-num_sequences:]
         # get the tuples of start and end indexes of the sequences
         tuples = [(inds[i], inds[i + 1]) if i < len(inds) - 1 else (inds[i], L) for i in which_seqs]
         if self.troubleshoot:
@@ -154,9 +147,7 @@ def make_dataloader(dataset):
     """Basic function to make a dataloader.
     """
     dataloader = DataLoader(dataset)
-    return dataloader
 
-# %% ../nbs/03_dataloaders.ipynb 5
 @dataclass
 class DataCollatorForUniclust30Dataset(object):
     """

ProtMamba_ssm/dataloaders.py

@@ -1,13 +1,6 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/04_fim.ipynb.
-
-# %% auto 0
-__all__ = ['AbstractFIM', 'NoFIM', 'SingleSpanFIM', 'MultipleSpanFIM']
-
-# %% ../nbs/04_fim.ipynb 3
 from .utils import MASK_TO_ID, AA_TO_ID
 import numpy as np
 
-# %% ../nbs/04_fim.ipynb 4
 class AbstractFIM(object):
     def __init__(self,
                  max_patches=5,

ProtMamba_ssm/fim.py

@@ -1,19 +1,7 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/01_modules.ipynb.
-
-# %% auto 0
-__all__ = ['MambaConfig', 'sample_safe', 'decode_safe', 'GenerationMixinSafe', 'CheckpointedModule', 'create_block',
-           'MixerModelSafe', 'MambaLMHeadModelSafe', 'MixerModelWithPosids', 'MixerModelWith2DPosids',
-           'MambaLMHeadModelwithPosids', 'MambaLMHeadModelwith2DPosids', 'load_model']
-
-# %% ../nbs/01_modules.ipynb 3
-import torch
-import torch.nn as  nn
+import torch.nn as nn
 
 import json
 import os
-from collections import namedtuple
-from dataclasses import field, dataclass
-from functools import partial
 
 from mamba_ssm.models.config_mamba import MambaConfig
 from mamba_ssm.modules.block import Block
@@ -722,10 +710,11 @@ def protected_forward(self, input_ids, position_ids=None, inference_params=None,
         if num_last_tokens > 0:
             hidden_states = hidden_states[:, -num_last_tokens:]
         lm_logits = self.lm_head(hidden_states)
-        CausalLMOutput = namedtuple("CausalLMOutput", ["loss", "logits", "hidden_states"])
         if len(save_layer) > 0:
+            CausalLMOutput = namedtuple("CausalLMOutput", ["loss", "logits", "hidden_states"])
             return CausalLMOutput(loss=None, logits=lm_logits, hidden_states=hidden_states)
-        return CausalLMOutput(loss=None, logits=lm_logits, hidden_states=None)
+        CausalLMOutput = namedtuple("CausalLMOutput", ["loss", "logits"])
+        return CausalLMOutput(loss=None, logits=lm_logits)
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, checkpoint_mixer=False, **kwargs):

ProtMamba_ssm/modules.py

@@ -1,10 +1,4 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/02_trainer.ipynb.
-
-# %% auto 0
-__all__ = ['PREFIX_CHECKPOINT_DIR', 'MambaTrainer', 'get_last_checkpoint', 'EarlyStoppingCallback']
-
-# %% ../nbs/02_trainer.ipynb 3
-from transformers import Trainer, TrainerCallback, TrainerState, TrainerControl
+from transformers import Trainer, TrainerCallback
 from .utils import *
 import re
 import torch

ProtMamba_ssm/trainer.py

@@ -1,13 +1,9 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/99_utils.ipynb.
-
-# %% auto 0
 __all__ = ['AA_TO_ID', 'MASK_TO_ID', 'ID_TO_AA', 'encode_sequence', 'decode_sequence', 'clean_sequence', 'tokenizer',
            'reorder_masked_sequence', 'load_from_file', 'generate_sequence', 'prepare_dataset_for_fim_generation',
            'prepare_tokens', 'prepare_target', 'load_tensorboard_data', 'filter_datapoints', 'save_to_tensorboard',
            'merge_loggings', 'concatenate_loggings', 'print_number_of_parameters', 'find_fim_indices',
            'compute_metrics']
 
-# %% ../nbs/99_utils.ipynb 3
 # Constants
 AA_TO_ID = {'<cls>': 0,
             '<pad>': 1,
@@ -53,7 +49,6 @@
 
 ID_TO_AA = {v: k for k, v in AA_TO_ID.items()}
 
-# %% ../nbs/99_utils.ipynb 4
 import numpy as np
 import torch
 from Bio import SeqIO
@@ -271,7 +266,6 @@ def prepare_target(target, use_fim=None):
         assert new_target.shape[1] == new_pos_ids.shape[1]
         return new_target, new_pos_ids, is_fim_dict
 
-# %% ../nbs/99_utils.ipynb 5
 from tensorboard.backend.event_processing import event_accumulator
 from tensorboard.backend.event_processing.event_accumulator import ScalarEvent
 from torch.utils.tensorboard import SummaryWriter

ProtMamba_ssm/utils.py

@@ -1,12 +1,12 @@
-data_dir: ""
-output_dir: "results/"
+data_dir: "/home/user/data/" #"/home/malbrank/nas/protmamba2/data/" and "/nvme1/common/OpenProteinSet/"
+output_dir: "/home/user/results/"
 namedir: "test0"
-train_dataset_path: "encoded_MSAs_train.pkl"
+train_dataset_path: "encoded_MSAs_subset-100.pkl"
 eval_dataset_path: '...'
-batch_size: 4 # mamba trained with total 0.5M tokens per batch
+batch_size: 2 # mamba trained with total 0.5M tokens per batch
 d_model: 1024
-gradient_accumulation_steps: 8
-checkpoint_mixer: True
+gradient_accumulation_steps: 1
+checkpoint_mixer: False
 learning_rate: 0.0006 # mamba default (x5), decrease to 0.0006 for sizes > 100M params and 0.0002 for sizes > 1B params
 weight_decay: 0.1 # mamba default
 beta1: 0.9 # mamba default
@@ -25,9 +25,9 @@ seed_sequence_sampling: 42
 seed_datasets: 0
 save_steps: 250
 eval_steps: 50
-size_validation: 192
+size_validation: 4
 logging_steps: 10
-eval_accumulation_steps: 200
+eval_accumulation_steps: 10
 save_total_limit: 50
 dtype: "bfloat16"
 fim_strategy: "multiple_span" #["no-scramble", "one_span", "multiple_span"]