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LLMatic-main/.gitignore

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+# Ignore python compile stuff
+*.pyc
+*.dat
+
+data
+outputs
+multirun
+logs
+rough

LLMatic-main/LICENSE

@@ -0,0 +1,201 @@
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LLMatic-main/README.md

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+# LLMatic
+
+### Abstract:
+_Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have made a significant impact is in the domain of code generation. Here, we propose using the coding abilities of LLMs to introduce meaningful variations to code defining neural networks. Meanwhile, Quality-Diversity (QD) algorithms are known to discover diverse and robust solutions. By merging the code-generating abilities of LLMs with the diversity and robustness of QD solutions, we introduce \texttt{LLMatic}, a Neural Architecture Search (NAS) algorithm. While LLMs struggle to conduct NAS directly through prompts, \texttt{LLMatic} uses a procedural approach, leveraging QD for prompts and network architecture to create diverse and high-performing networks. We test \texttt{LLMatic} on the CIFAR-10 and NAS-bench-201 benchmarks, demonstrating that it can produce competitive networks while evaluating just $2,000$ candidates, even without prior knowledge of the benchmark domain or exposure to any previous top-performing models for the benchmark._
+
+### To run experiments:
+
+Clone this repository:
+
+```git clone https://github.com/umair-nasir14/LLMatic.git```
+
+Install all dependencies:
+
+```
+cd LLMatic
+conda env create -f environment.yaml
+conda activate llmatic
+```
+
+Run:
+
+```python llmatic.py```
+
+All configs are present in `conf/config.py`.
+
+### Cite:
+
+```
+@article{nasir2023llmatic,
+  title={Llmatic: Neural architecture search via large language models and quality-diversity optimization},
+  author={Nasir, Muhammad U and Earle, Sam and Togelius, Julian and James, Steven and Cleghorn, Christopher},
+  journal={arXiv preprint arXiv:2306.01102},
+  year={2023}
+}
+```

LLMatic-main/centroids_100_2.dat

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+0.651900557168317 0.20902488539835712 

LLMatic-main/codegen_tests.py

@@ -0,0 +1,306 @@
+from mutation_models import codegen_mutate
+from utils import read_python_file, extract_code_section, get_class
+from evaluations import is_trainable
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+import numpy as np
+import os
+import random
+import hydra
+import ray
+
+from conf.config import Config
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 1, 1)
+        self.fc1 = nn.Linear(1024, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        return x
+
+
+def fixer_test(cfg):
+    ray.init()
+    exp_name = "0003"
+    path_nets = f"codegen_tests/{exp_name}"
+    if not os.path.exists(path_nets):
+        os.makedirs(os.path.normpath(path_nets))
+    main_net_focus = read_python_file(os.path.normpath("/mnt/lustre/users/mnasir/NAS-LLM/best_net.py"))
+    print(main_net_focus)
+    #fixing_prompt = '"""Create another class that inherits from nn.Module to edit the above network such that it works when torch.zeros((1, 3, 32, 32)) is passed as an input"""'
+    #fixing_prompt = '"""Fix the above neural network bu editing the layers such that it takes torch.zeros((1,3,32,32)) as inputs and output as tensor of size torch.size((1,10))"""'
+    fixing_prompt = '"""The above neural network does not work in the current form. Add or delete layers to fix the above neural network such that it takes torch.zeros((1,3,32,32)) as inputs and output as tensor of size torch.size((1,10))"""'
+    for i in range(10):
+        (print(f"Test {i}"))
+        
+        fixed_net_results = codegen_mutate.remote(cfg=cfg, prompt=main_net_focus + "\n" + fixing_prompt, temperature = 0.1)
+        extract_code_section(ray.get(fixed_net_results), fixing_prompt, file_path=os.path.normpath(f"{path_nets}/fixed_net_{i}.py"))
+        print(read_python_file(os.path.normpath(f"{path_nets}/fixed_net_{i}.py")))
+        try:
+            Net = get_class(os.path.normpath(f"{path_nets}/fixed_net_{i}.py"))             
+            net = Net()
+            if is_trainable(net):
+                print("TRUE")
+            else:
+                print("FALSE")
+        except Exception as e:
+            print(f"FALSE because of {e}")
+
+
+def init_net_test(cfg):
+    ray.init()
+    exp_name = "init_net_test_0007"
+    path_nets = f"codegen_tests/{exp_name}"
+    if not os.path.exists(path_nets):
+        os.makedirs(os.path.normpath(path_nets))
+    init_net_prompt = '"""Create a simple neural network class that inherits from nn.Module pytorch class. It should accept input tensor of size 32 x 32 x 3 and output 10 neurons for classification task"""'
+    for i in range(10):
+        (print(f"Test {i}"))
+        
+        fixed_net_results = codegen_mutate.remote(cfg=cfg, prompt=init_net_prompt, temperature = 0.3)
+        extract_code_section(ray.get(fixed_net_results), init_net_prompt, file_path=os.path.normpath(f"{path_nets}/init_net_{i}.py"))
+        print(read_python_file(os.path.normpath(f"{path_nets}/init_net_{i}.py")))
+        try:
+            Net = get_class(os.path.normpath(f"{path_nets}/init_net_{i}.py"))             
+            net = Net()
+            if is_trainable(net):
+                print("TRUE")
+            else:
+                print("FALSE")
+        except Exception as e:
+            print(f"FALSE because of {e}")
+
+def mutation_test(cfg):
+    ray.init()
+    exp_name = "mutation_test_0005"
+
+    prompts = ['"""Add a layer to improve the above network"""',
+               '"""Delete a layer to improve the above network"""',
+               '"""Increase the width of the above neural network"""',
+               '"""Decrease the width of the above neural network"""',
+               '"""Increase the depth of the above neural network"""',
+               '"""Decrease the depth of the above neural network"""',
+               '"""Add fully connected layer to improve the above network"""',
+               '"""Add convolutional layer to improve the above network"""',
+               '"""Add pooling layer to improve the above network"""',
+               '"""Add residual connection to improve the above network"""',
+               '"""Add multiple residual connections to improve the above network"""',
+               '"""Add dropout layer to improve the above network"""',
+               '"""Add normalization layer to improve the above network"""',
+                ]
+    path_nets = f"codegen_tests/{exp_name}"
+    if not os.path.exists(path_nets):
+        os.makedirs(os.path.normpath(path_nets))
+
+    
+    seed_value = 1
+    for prompt in prompts:
+        (print(f"Test on prompt: {prompt}"))
+
+        seed_value = seed_value + 10
+        torch.manual_seed(seed_value)
+
+        mutation_prompt = \
+        f'''class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 1, 1)
+        self.fc1 = nn.Linear(1024, 10)
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        return x
+    
+{prompt}'''
+        
+        fixed_net_results = codegen_mutate.remote(cfg=cfg, prompt=mutation_prompt, temperature = 0.6)
+        extract_code_section(ray.get(fixed_net_results), mutation_prompt, file_path=os.path.normpath(f"{path_nets}/mu_net_{prompt}.py"))
+        print(read_python_file(os.path.normpath(f"{path_nets}/mu_net_{prompt}.py")))
+        try:
+            Net = get_class(os.path.normpath(f"{path_nets}/mu_net_{prompt}.py"))             
+            net = Net()
+            if is_trainable(net):
+                print("TRUE")
+            else:
+                print("FALSE")
+        except Exception as e:
+            print(f"FALSE because of {e}")
+
+
+def crossover_test(cfg):
+    ray.init()
+    exp_name = "crossover_test_0001"
+
+    prompt = '''
+
+    class Net(nn.Module):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.conv1 = nn.Conv2d(3, 6, 5)
+            self.pool = nn.MaxPool2d(2, 2)
+            self.conv2 = nn.Conv2d(6, 16, 5)
+            self.fc1 = nn.Linear(16 * 5 * 5, 120)
+            self.fc2 = nn.Linear(120, 84)
+            self.fc3 = nn.Linear(84, 10)
+
+        def forward(self, x):
+            x = self.pool(F.relu(self.conv1(x)))
+            x = self.pool(F.relu(self.conv2(x)))
+            x = x.view(-1, 16 * 5 * 5)
+            x = F.relu(self.fc1(x))
+            x = F.relu(self.fc2(x))
+            x = self.fc3(x)
+            return x
+
+    class Net2(nn.Module):
+        def __init__(self):
+            super(Net2, self).__init__()
+            self.conv1 = nn.Conv2d(3, 1, 1)
+            self.fc1 = nn.Linear(1024 128)
+            self.fc2 = nn.Linear(128, 64)
+            self.fc3 = nn.Linear(64, 32)
+            self.fc4 = nn.Linear(32, 10)
+    
+        def forward(self, x):
+            x = self.pool(F.relu(self.conv1(x)))
+            x = torch.flatten(x, 1)
+            x = F.relu(self.fc1(x))
+            x = F.relu(self.fc2(x))
+            x = F.relu(self.fc3(x))
+            x = self.fc4(x)
+            return x
+    
+    """Perform a crossover between above two neural networks and create a third neural network class that gets the best layers from above two networks"""'''
+    path_nets = f"codegen_tests/{exp_name}"
+    if not os.path.exists(path_nets):
+        os.makedirs(os.path.normpath(path_nets))
+    for i in range(10):
+        (print(f"Test {i}"))
+        
+        fixed_net_results = codegen_mutate.remote(cfg=cfg, prompt=prompt, temperature = 0.6)
+        extract_code_section(ray.get(fixed_net_results), prompt, file_path=os.path.normpath(f"{path_nets}/init_net_{i}.py"))
+        print(read_python_file(os.path.normpath(f"{path_nets}/init_net_{i}.py")))
+        try:
+            Net = get_class(os.path.normpath(f"{path_nets}/init_net_{i}.py"))             
+            net = Net()
+            if is_trainable(net):
+                print("TRUE")
+            else:
+                print("FALSE")
+        except Exception as e:
+            print(f"FALSE because of {e}")
+
+
+def diff_mutate_test(cfg):
+    ray.init()
+    exp_name = "diff_mutate_test_0004"
+    path_nets = f"codegen_tests/{exp_name}"
+    if not os.path.exists(path_nets):
+        os.makedirs(os.path.normpath(path_nets))
+    diff_prompts = ['<NME> initial_net.py\n'
+    '<BFE> import torch\n'
+    'import torch.nn as nn\n'
+    'import torch.nn.functional as F\n'
+    '"""Returns a pytorch neural network class that takes an image of 3 x 32 x 32 as input and outputs 10 neurons."""\n'
+    'class Net(nn.Module):\n'
+    '   def __init__(self):\n'
+    '       super().__init__()\n'
+    '       self.conv1 = nn.Conv2d(3, 1, 1)\n'
+    '       self.fc1 = nn.Linear(1024, 10)\n'
+    '   def forward(self, x):\n'
+    '       x = F.relu(self.conv1(x))\n'
+    '       x = torch.flatten(x, 1)\n'
+    '       x = F.relu(self.fc1(x))\n'
+    '       return x\n'
+    '<MSG> Added a nn.Conv2d layer to improve the neural network.\n',
+    '<NME> initial_net.py\n'
+    '<BFE> import torch\n'
+    'import torch.nn as nn\n'
+    'import torch.nn.functional as F\n'
+    '"""Returns a pytorch neural network class that takes an image of 3 x 32 x 32 as input and outputs 10 neurons."""\n'
+    'class Net(nn.Module):\n'
+    '   def __init__(self):\n'
+    '       super().__init__()\n'
+    '       self.conv1 = nn.Conv2d(3, 1, 1)\n'
+    '       self.fc1 = nn.Linear(1024, 10)\n'
+    '   def forward(self, x):\n'
+    '       x = F.relu(self.conv1(x))\n'
+    '       x = torch.flatten(x, 1)\n'
+    '       x = F.relu(self.fc1(x))\n'
+    '       return x\n'
+    '<MSG> Added a nn.Linear layer to improve the neural network.\n',
+    '<NME> initial_net.py\n'
+    '<BFE> import torch\n'
+    'import torch.nn as nn\n'
+    'import torch.nn.functional as F\n'
+    '"""Returns a pytorch neural network class that takes an image of 3 x 32 x 32 as input and outputs 10 neurons."""\n'
+    'class Net(nn.Module):\n'
+    '   def __init__(self):\n'
+    '       super().__init__()\n'
+    '       self.conv1 = nn.Conv2d(3, 1, 1)\n'
+    '       self.fc1 = nn.Linear(1024, 10)\n'
+    '   def forward(self, x):\n'
+    '       x = F.relu(self.conv1(x))\n'
+    '       x = torch.flatten(x, 1)\n'
+    '       x = F.relu(self.fc1(x))\n'
+    '       return x\n'
+    '<MSG> Added a nn.Conv2d and a nn.Linear layer to improve the neural network.\n',
+    '<NME> initial_net.py\n'
+    '<BFE> import torch\n'
+    'import torch.nn as nn\n'
+    'import torch.nn.functional as F\n'
+    '"""Returns a pytorch neural network class that takes an image of 3 x 32 x 32 as input and outputs 10 neurons."""\n'
+    'class Net(nn.Module):\n'
+    '   def __init__(self):\n'
+    '       super().__init__()\n'
+    '       self.conv1 = nn.Conv2d(3, 1, 1)\n'
+    '       self.fc1 = nn.Linear(1024, 10)\n'
+    '   def forward(self, x):\n'
+    '       x = F.relu(self.conv1(x))\n'
+    '       x = torch.flatten(x, 1)\n'
+    '       x = F.relu(self.fc1(x))\n'
+    '       return x\n'
+    '<MSG> Added layers to improve the neural network.\n']
+    for i,diff_prompt in enumerate(diff_prompts):
+        (print(f"Test {i}"))
+        
+        fixed_net_results = codegen_mutate.remote(cfg=cfg, prompt=diff_prompt, temperature = 0.8)
+        res = ray.get(fixed_net_results)
+        print(res)
+        extract_code_section(res, diff_prompt, file_path=os.path.normpath(f"{path_nets}/diff_prompt_{i}.py"))
+        print(read_python_file(os.path.normpath(f"{path_nets}/diff_prompt_{i}.py")))
+        try:
+            Net = get_class(os.path.normpath(f"{path_nets}/diff_prompt_{i}.py"))             
+            net = Net()
+            if is_trainable(net):
+                print("TRUE")
+            else:
+                print("FALSE")
+        except Exception as e:
+            print(f"FALSE because of {e}")
+
+
+@hydra.main(version_base="1.3.0", config_path="conf", config_name="config")    
+def main(cfg: Config):
+    #fixer_test(cfg)
+    #init_net_test(cfg)
+    #mutation_test(cfg)
+    crossover_test(cfg)
+    #diff_mutate_test(cfg)
+
+
+if __name__ == "__main__":
+    main()

LLMatic-main/conf/config.py

@@ -0,0 +1,43 @@
+from dataclasses import MISSING, dataclass
+import typing
+import multiprocessing as mp
+
+from hydra.core.config_store import ConfigStore
+from map_elites import common as cm
+
+@dataclass
+class Config:
+    """This class provides a "schema" for the config file, validating types."""
+
+    RANDOM_INIT_NETS: int = 10 # INIT Nets to be accepted in archive
+    INIT_NUM_NETS: int = 10 # INIT Nets created per generation
+    ROLL_OUTS: int = 4 # For GPU training. ROLL_OUTS * (INIT_NUM_NETS or NUM_NETS) = Total nets created in each generation
+    NUM_NETS: int = 10 # Mutation and crossover nets to be created
+    START_FROM_CHECKPOINT: bool = False
+    RANDOM_NETWORKS: bool = False
+
+    MUTATION: str = "codegen-6B-mono"
+    #MUTATION: str = "codex"
+
+    GENERATIONS: int = 200
+    NET_TRAINING_EPOCHS: int = 50
+    TEMPERATURE: float = 0.0000000
+
+    DEVICE: str = "cuda" # options: ["cuda", "cpu", "both"]
+
+    NUM_PROCESSES: int = mp.cpu_count() - 1
+    RAY: bool = True
+    NUM_CPUS: int = NUM_PROCESSES
+    NUM_GPUS: int = 1 # per task
+    NUM_GPUS_TOTAL: int  = 4 # total available
+ 
+    # MAP-ELITES
+
+    DIM_MAP: int = 2
+    N_NICHES: int = 100
+    
+    SAVE_DIR: str = "./"
+             
+
+cs = ConfigStore.instance()
+cs.store(name="base_config", node=Config)

LLMatic-main/conf/config.yaml

@@ -0,0 +1,23 @@
+defaults:
+  - base_config
+  - _self_
+
+  - override hydra/launcher: submitit_slurm 
+  # - override hydra/launcher: submitit_local
+
+
+hydra:
+  sweeper:
+    params:
+      NUM_NETS: 10, 20
+      # GENERATIONS: 25, 50, 100
+      NET_TRAINING_EPOCHS: 10, 30
+      TEMPERATURE: 0.3, 0.5, 0.7
+
+  # SLURM-specific arguments
+  launcher:
+    tasks_per_node: 1
+    cpus_per_task: 1
+    gpus_per_node: 1
+    timeout_min: 2880
+    mem_gb: 30

LLMatic-main/datasets.py

@@ -0,0 +1,29 @@
+import torch
+import torchvision
+import torchvision.transforms as transforms
+
+
+import numpy as np
+
+
+def get_datasets():
+    
+    transform = transforms.Compose(
+        [transforms.ToTensor(),
+         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+    batch_size = 1
+
+    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
+                                            download=True, transform=transform)
+    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
+                                              shuffle=True, num_workers=1)
+
+    validset = torchvision.datasets.CIFAR10(root='./data', train=False,
+                                           download=True, transform=transform)
+    validloader = torch.utils.data.DataLoader(validset, batch_size=batch_size,
+                                             shuffle=False, num_workers=1)
+
+    return trainset, trainloader, validset, validloader
+
+

LLMatic-main/environment.yaml

@@ -0,0 +1,91 @@
+name: llmatic
+channels:
+  - defaults
+dependencies:
+  - ca-certificates=2023.01.10=haa95532_0
+  - certifi=2022.12.7=py39haa95532_0
+  - openssl=1.1.1t=h2bbff1b_0
+  - pip=22.3.1=py39haa95532_0
+  - python=3.9.16=h6244533_0
+  - setuptools=65.6.3=py39haa95532_0
+  - sqlite=3.40.1=h2bbff1b_0
+  - tzdata=2022g=h04d1e81_0
+  - vc=14.2=h21ff451_1
+  - vs2015_runtime=14.27.29016=h5e58377_2
+  - wheel=0.38.4=py39haa95532_0
+  - wincertstore=0.2=py39haa95532_2
+  - pip:
+    - aiohttp==3.8.4
+    - aiosignal==1.3.1
+    - antlr4-python3-runtime==4.9.3
+    - async-timeout==4.0.2
+    - attrs==22.2.0
+    - charset-normalizer==3.0.1
+    - click==8.1.3
+    - cloudpickle==2.2.1
+    - colorama==0.4.6
+    - contourpy==1.0.7
+    - cycler==0.11.0
+    - filelock==3.9.0
+    - flopth==0.1.3
+    - fonttools==4.38.0
+    - frozenlist==1.3.3
+    - fvcore==0.1.5.post20221221
+    - huggingface-hub==0.12.1
+    - hydra-core==1.3.1
+    - hydra-submitit-launcher==1.2.0
+    - idna==3.4
+    - importlib-resources==5.12.0
+    - inquirerpy==0.3.4
+    - iopath==0.1.10
+    - joblib==1.2.0
+    - jsonschema==4.21.1
+    - jsonschema-specifications==2023.12.1
+    - kiwisolver==1.4.4
+    - levenshtein==0.20.9
+    - matplotlib==3.7.0
+    - msgpack==1.0.8
+    - multidict==6.0.4
+    - networkx==3.0
+    - numpy==1.24.2
+    - omegaconf==2.3.0
+    - openai==0.26.5
+    - packaging==23.0
+    - pandas==1.5.3
+    - pfzy==0.3.4
+    - pillow==9.4.0
+    - portalocker==2.7.0
+    - prompt-toolkit==3.0.37
+    - protobuf==5.26.1
+    - pyparsing==3.0.9
+    - python-dateutil==2.8.2
+    - pytz==2022.7.1
+    - pywin32==305
+    - pyyaml==6.0
+    - rapidfuzz==2.13.7
+    - ray==2.10.0
+    - referencing==0.34.0
+    - regex==2022.10.31
+    - requests==2.28.2
+    - rpds-py==0.18.0
+    - sacremoses==0.0.53
+    - scikit-learn==1.2.2
+    - scipy==1.10.1
+    - six==1.16.0
+    - submitit==1.4.5
+    - tabulate==0.9.0
+    - termcolor==2.2.0
+    - threadpoolctl==3.1.0
+    - tokenizers==0.13.2
+    - torch==1.13.1
+    - torchvision==0.14.1
+    - tqdm==4.64.1
+    - transformers==4.25.1
+    - typing-extensions==4.5.0
+    - urllib3==1.26.14
+    - wcwidth==0.2.6
+    - xmltodict==0.13.0
+    - yacs==0.1.8
+    - yarl==1.8.2
+    - zipp==3.14.0
+prefix: C:\Users\DELL\anaconda3\envs\llmatic

LLMatic-main/evaluations.py

@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 1, 1, bias=False)
+        self.conv2 = nn.Conv2d(3, 1, 1, bias=False)
+        self.fc1 = nn.Linear(1024, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        return x
+
+
+
+net = Net()
+def is_trainable(net):
+    zeros = torch.zeros((1, 3, 32, 32))  # input tensor of shape (batch_size, channels, height, width)
+
+    # Check that we can pass a dummy input through the network without errors.
+    try:
+        output = net(zeros)
+    except Exception as e:
+        return False
+    
+    # Network output shape must match number of classes in CIFAR-10.
+    if output.shape != (1, 10):
+        return False
+
+    return True
+
+def main():
+    print(is_trainable(net))
+
+if __name__ == '__main__':
+    main()

LLMatic-main/initial_net.py

@@ -0,0 +1,13 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 1, 1)
+        self.fc1 = nn.Linear(1024, 10)
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        return x

LLMatic-main/llmatic.py

@@ -0,0 +1,566 @@
+#| This file has a major part from the pymap_elites framework.
+#| Copyright 2019, INRIA
+#| Main contributor(s):
+#| Jean-Baptiste Mouret, [email protected]
+#| Eloise Dalin , [email protected]
+#| Pierre Desreumaux , [email protected]
+#|
+#|
+#| **Main paper**: Mouret JB, Clune J. Illuminating search spaces by
+#| mapping elites. arXiv preprint arXiv:1504.04909. 2015 Apr 20.
+#|
+#| This software is governed by the CeCILL license under French law
+#| and abiding by the rules of distribution of free software.  You
+#| can use, modify and/ or redistribute the software under the terms
+#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
+#| following URL "http://www.cecill.info".
+#|
+#| As a counterpart to the access to the source code and rights to
+#| copy, modify and redistribute granted by the license, users are
+#| provided only with a limited warranty and the software's author,
+#| the holder of the economic rights, and the successive licensors
+#| have only limited liability.
+#|
+#| In this respect, the user's attention is drawn to the risks
+#| associated with loading, using, modifying and/or developing or
+#| reproducing the software by the user in light of its specific
+#| status of free software, that may mean that it is complicated to
+#| manipulate, and that also therefore means that it is reserved for
+#| developers and experienced professionals having in-depth computer
+#| knowledge. Users are therefore encouraged to load and test the
+#| software's suitability as regards their requirements in conditions
+#| enabling the security of their systems and/or data to be ensured
+#| and, more generally, to use and operate it in the same conditions
+#| as regards security.
+#|
+#| The fact that you are presently reading this means that you have
+#| had knowledge of the CeCILL license and that you accept its terms.
+
+import math
+import numpy as np
+import multiprocessing
+
+# from scipy.spatial import cKDTree : TODO -- faster?
+from sklearn.neighbors import KDTree
+
+from map_elites import common as cm
+
+from flopth import flopth
+
+from datasets import get_datasets
+from utils import read_python_file, extract_code, get_net_name, extract_code_section, get_class, csv_writer, get_max_curiosity, get_max_fitness, count_unique_components, count_parameters, get_network_width_depth_ratio
+from utils import Net
+from mutation_models import codegen_mutate, codex_mutate, replace_word_mutation
+from evaluations import is_trainable
+from train import train_net_on_cpu, train_net_on_gpu, forward_pass_on_cpu,forward_pass_on_gpu, detect_layers, transfer_weights
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+import numpy as np
+import os
+import random
+import hydra
+import ray
+from fvcore.nn import FlopCountAnalysis
+
+from conf.config import Config
+
+
+
+
+def __add_to_archive(s, centroid, archive, kdt, type_ind):
+    niche_index = kdt.query([centroid], k=1)[1][0][0]
+    niche = kdt.data[niche_index]
+    n = cm.make_hashable(niche)
+    s.centroid = n
+    if n in archive:
+        if type_ind == "network": 
+            if s.fitness < archive[n].fitness:
+                archive[n] = s
+                return True
+        elif type_ind == "prompt":
+            if s.fitness > archive[n].fitness:
+                archive[n] = s
+                return True
+        return False
+    else:
+        archive[n] = s
+        return True
+
+
+
+def to_specie(net,fit,desc,net_path):
+    return cm.Species(net, desc, fit, net_path)
+
+# map-elites algorithm (CVT variant)
+
+@hydra.main(version_base="1.3.0", config_path="conf", config_name="config")    
+def main(cfg: Config):
+    """CVT MAP-Elites
+       Vassiliades V, Chatzilygeroudis K, Mouret JB. Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm. IEEE Transactions on Evolutionary Computation. 2017 Aug 3;22(4):623-30.
+
+       Format of the logfile: evals archive_size max mean median 5%_percentile, 95%_percentile
+
+    """
+    if cfg.DEVICE == "cuda":
+        ray.init(num_gpus=cfg.NUM_GPUS_TOTAL) 
+    else:
+        ray.init()
+    mutation_fn = codex_mutate if cfg.MUTATION == "codex" else codegen_mutate
+
+    prompts = ['"""Add a layer to improve the above network"""',
+               '"""Delete a layer to improve the above network"""',
+               '"""improve the above network"""',
+               '"""improve the above network by reducing the size drastically"""',
+               '"""improve the above network by increasing the size drastically"""',
+               # Specific prompts below
+               '"""Add fully connected layer to improve the above network"""',
+               '"""Add convolutional layer to improve the above network"""',
+               '"""Add pooling layer to improve the above network"""',
+               '"""Add residual connection to improve the above network"""',
+               '"""Add multiple residual connections to improve the above network"""',
+               '"""Add dropout layer to improve the above network"""',
+               '"""Add normalization layer to improve the above network"""',
+               '"""Add recurrent layer to improve the above network"""',
+                ]
+    probabilities = [1.0 / len(prompts)] * len(prompts)
+
+    prompt_score = {}
+    for prompt in prompts:
+        prompt_score[prompt] = 0
+    prompt_score["None"] = 0
+
+    prompt_to_int = {}
+    int_to_prompt = {}
+    for i,prompt in enumerate(prompts):
+        prompt_to_int[prompt] = i
+        int_to_prompt[i] = prompt
+    
+    if cfg.START_FROM_CHECKPOINT:
+        best_net = read_python_file("initial_net.py")
+        init_gen = 4
+        print(f"Starting from the following network:\n{best_net}")
+    else:
+        init_net = read_python_file(os.path.normpath(f"{cfg.SAVE_DIR}/initial_net.py"))
+        init_gen = 0
+        print(f"Starting from the following network:\n{init_net}")
+    
+    
+    # create the CVT
+    params = cm.default_params
+    c = cm.cvt(cfg.N_NICHES,cfg.DIM_MAP,
+              params['cvt_samples'], params['cvt_use_cache'])
+    kdt = KDTree(c, leaf_size=30, metric='euclidean')
+    cm.__write_centroids(c)
+
+    prompt_archive = {} # init archive (empty)
+    nets_archive = {}
+    n_evals = 0 # number of evaluations since the beginning
+    b_evals = 0 # number evaluation since the last dump
+
+    curios_net_path = os.path.normpath(f"{cfg.SAVE_DIR}/initial_net.py")
+
+    c_net_str = read_python_file(curios_net_path)
+    Net = get_class(curios_net_path)
+    curios_net = Net()
+    curios_prompt = prompts[0]
+    # main loop
+
+    temperature_start = 0.6
+    prev_best_score = np.inf
+    prev_best_net_path = None
+    prev_best_prompt = None
+
+    exp_name = f"gen-nets_{cfg.MUTATION}_networks-{cfg.NUM_NETS}_temp-{cfg.TEMPERATURE}_net-training-epochs-{cfg.NET_TRAINING_EPOCHS}_niches-{cfg.N_NICHES}_infer-and-flops-as-bd"
+    path_nets = f"{cfg.SAVE_DIR}/logs/{exp_name}"
+    os.makedirs(path_nets,exist_ok=True)
+    log_file = open(os.path.normpath(f"{path_nets}/cvt.dat"),"w")
+    out_file = os.path.normpath(f"{path_nets}/exp_results.csv")        
+    csv_writer(["generations", "best_loss", "used_prompt"],out_file)
+            
+
+    for gen_i in range(init_gen, cfg.GENERATIONS):
+
+        generated_nets = []
+        
+        # random initialization
+        (print(f"[Generation: {gen_i}]"))
+
+        if (len(nets_archive.keys()) < cfg.RANDOM_INIT_NETS) and (len(prompt_archive.keys()) < (int(cfg.RANDOM_INIT_NETS*2))):#params['random_init'] * n_niches:
+            
+            
+            for _i in range(cfg.ROLL_OUTS):
+                selected_prompts = []
+                generated_net_results = []
+                selected_prompts = random.choices(prompts, weights=probabilities,k=cfg.INIT_NUM_NETS)
+                for i in range(0, cfg.INIT_NUM_NETS):
+                    print(f"Selected prompt for generations: {selected_prompts[i]}")
+
+                for prompt in selected_prompts:
+                    generated_net_results.append(mutation_fn.remote(cfg=cfg, prompt=init_net + "\n" + prompt, temperature=temperature_start))
+
+
+                for i,generated_net in enumerate(generated_net_results):
+                    generated_nets.append((ray.get(generated_net),selected_prompts[i],temperature_start))
+                
+        else:  # variation/selection loop
+            
+            evo_operator = random.choices(["mutation","crossover"], weights=[0.85,0.15])[0]
+            
+            print(f"Performing {evo_operator}")
+            for _i in range(cfg.ROLL_OUTS):
+                generated_net_results = []
+                if evo_operator == "mutation":
+            
+                    if len(nets_archive.keys()) < 3:
+                        n_nets = 1
+                        selection = 0
+                    else:
+                        n_nets = 3
+                        selection = random.randint(0, 2)
+
+                    curios_nets = []
+                    curios_prompts = []
+                    curios_temps = []
+                    curios_net_paths = []
+
+                    n_curios_nets = get_max_fitness(nets_archive.values(),n_nets)
+                    for n in n_curios_nets:
+                        curios_nets.append(n.x)
+                        curios_net_paths.append(n.net_path)
+
+                    n_prompts_temps = get_max_curiosity(prompt_archive.values(),n_nets)
+                    for pt in n_prompts_temps:
+                        curios_prompts.append(pt.desc[0])
+                        curios_temps.append(pt.desc[1])
+
+                    curios_net = curios_nets[selection]
+                    curios_temp = curios_temps[selection]
+                    curios_net_path = curios_net_paths[selection]
+
+                    curios_temp_ray = []
+                    curios_prompt_ray = []
+
+
+                    for i in range(cfg.NUM_NETS):
+                        # Temperature mutation
+
+                        if i == 1:
+                            curios_temp = curios_temps[selection]
+                        else:
+                            curios_temp += random.uniform(-0.1,0.1)
+                            if curios_temp > 1.0:
+                                curios_temp = 1.0
+                            elif curios_temp < 0.1:
+                                curios_temp = 0.1
+
+                        curios_prompt = curios_prompts[selection]
+
+                        curios_temp_ray.append(curios_temp)
+                        curios_prompt_ray.append(curios_prompt)
+
+                        print(f"prompt in mutation is {curios_prompt}")
+
+                    for i in range(cfg.NUM_NETS):
+                        generated_net_results.append(mutation_fn.remote(cfg=cfg, prompt=read_python_file(curios_net_path) + "\n" + int_to_prompt[int(curios_prompt_ray[i])], temperature = curios_temp_ray[i]))
+
+                    for i,generated_net in enumerate(generated_net_results):
+                        generated_nets.append((ray.get(generated_net),int_to_prompt[int(curios_prompt_ray[i])],curios_temp_ray[i]))
+
+                elif evo_operator == "crossover":
+
+                    if len(nets_archive.keys()) < 1:
+                        print("No crossover performed")
+                    else:
+                        curios_nets_ray = []
+                        curios_temps_ray = []
+
+                        curios_nets = []
+                        curios_prompts = []
+                        curios_temps = []
+                        curios_net_paths = []
+
+
+                        if len(nets_archive.keys()) < 2:
+                            selection = 0
+                            n_nets = 1
+                        else:
+                            selection = random.randint(1, len(nets_archive.keys())-1)# change it to 1
+                            n_nets = 2
+
+                        n_curios_nets = get_max_fitness(nets_archive.values(),n_nets)
+                        for n in n_curios_nets:
+                            curios_nets.append(n.x)
+                            curios_net_paths.append(n.net_path)
+
+                        n_prompts_temps = get_max_curiosity(prompt_archive.values(),n_nets)
+                        for pt in n_prompts_temps:
+                            curios_prompts.append(pt.desc[0])
+                            curios_temps.append(pt.desc[1])
+
+
+
+
+                        for i in range(0, cfg.NUM_NETS):
+
+                            curios_net_str = read_python_file(curios_net_paths[0])
+                            curios_net = curios_nets[0]
+                            
+                            curios_temp = curios_temps[0]
+                            curios_prompt = curios_prompts[0]
+
+
+                            curios_nets_ray.append(read_python_file(curios_net_paths[selection]))
+
+
+                        crossover_prompt = '"""Combine the above two neural networks and create a third neural network class that also inherits from nn.Module"""'
+
+                        for curios_2nd_net in curios_nets_ray:
+                            generated_net_results.append(mutation_fn.remote(cfg=cfg, prompt=curios_net_str + "\n" + curios_2nd_net + "\n" + crossover_prompt, temperature = curios_temp))
+
+                        for i,generated_net in enumerate(generated_net_results):
+                            generated_nets.append((ray.get(generated_net),int_to_prompt[int(curios_prompt)],curios_temp))
+
+ 
+                           
+        net_class_name = None
+        net_paths=[]
+        training_prompts=[]
+        training_nets=[]
+
+        for i, k in enumerate(generated_nets):
+            
+            invalid_net = False
+            generation, prompt, temperature = k
+
+            
+            net_path = os.path.normpath(f"{path_nets}/network_{gen_i}_{i}_{prompt[3:-3]}_{temperature}.py")
+            
+
+            if cfg.MUTATION == "codex":
+                    extract_code_section(generation['choices'][0]["text"], prompt, file_path=net_path)
+            elif cfg.MUTATION.startswith("codegen"):
+                extract_code_section(generation, prompt, file_path=net_path)  
+            main_net_focus = read_python_file(net_path)
+            print(f"Net in focus:\n {main_net_focus}")
+
+            if not invalid_net:
+                try:
+                    Net = get_class(net_path)             
+                    net = Net()
+                except Exception as e:
+                    net = curios_net
+                    net_path = curios_net_path
+                    if isinstance(curios_prompt,str):
+                        prompt = curios_prompt
+                    elif isinstance(curios_prompt,float) or isinstance(curios_prompt,int):
+                        prompt = int_to_prompt[int(curios_prompt)]
+                    
+                #breakpoint()
+                try:
+                    is_t = is_trainable(net)
+                except Exception:
+                    is_t = False
+                if is_t:
+                    net_paths.append(net_path)
+                    training_prompts.append(prompt)
+                    training_nets.append(net)
+                else:
+                    invalid_net = True
+
+                    
+
+            if invalid_net:
+                print(f"The network at {net_path} is not trainable")
+        
+
+        if training_nets == []:
+            continue
+    
+        if (len(nets_archive.keys()) < cfg.RANDOM_INIT_NETS) and (len(prompt_archive.keys()) < (int(cfg.RANDOM_INIT_NETS*2))):
+
+            inter_results = []
+            losses = []
+            layers_c_net = detect_layers(curios_net)
+            if cfg.DEVICE == "cpu":
+                
+                for net in training_nets:
+                    for layer in layers_c_net:
+                        try:
+                            net = transfer_weights(curios_net,net,layer)
+                            print(f"Weights transferred successfully")
+                        except Exception:
+                            print("Weights can not transfer")
+                for net in training_nets:
+                    inter_results.append(forward_pass_on_gpu.remote(net))
+            elif cfg.DEVICE == "cuda":
+                    for net in training_nets:
+                        for layer in layers_c_net:
+                            try:
+                                net = transfer_weights(curios_net,net,layer)
+                                print(f"Weights transferred successfully")
+                            except Exception:
+                                print("Weights can not transfer")
+                    for net in training_nets:
+                        inter_results.append(forward_pass_on_gpu.remote(net))
+            else:
+                raise ValueError(f"{cfg.DEVICE} is not a valid device")
+            fitness = []
+            for i,result in enumerate(inter_results):
+                try:
+                    res = ray.get(result)
+                    fitness.append([res[0],training_prompts[i],temperature,net_paths[i],res[1]])
+
+                    if fitness[i][0] <= prev_best_score:
+                        prev_best_net_path = net_paths[i]
+                        prev_best_prompt = training_prompts[i]
+                        prev_best_score = fitness[i][0]
+                        temperature += 0.05
+
+                    else:
+                        temperature -= 0.05
+                    if temperature > 1.0:
+                        temperature = 1.0
+                    elif temperature < 0.1:
+                        temperature = 0.1
+
+                        fitness[i][2] = temperature
+                except Exception:
+                    print("not trainable due to fitness 1")
+        
+        else:
+
+            if cfg.RAY:
+                inter_results = []
+                losses = []
+                layers_c_net = detect_layers(curios_net)
+                if cfg.DEVICE == "cpu":
+                    for net in training_nets:
+                        for layer in layers_c_net:
+                            try:
+                                net = transfer_weights(curios_net,net,layer)
+                                print(f"Weights transferred successfully")
+                            except Exception:
+                                print("Weights can not transfer")
+                    for net in training_nets:
+                        inter_results.append(train_net_on_cpu.remote(net,cfg.NET_TRAINING_EPOCHS))
+
+                elif cfg.DEVICE == "both":
+                    for i, net in enumerate(training_nets):
+                        if i % 2 == 0:
+                            inter_results.append(train_net_on_cpu.remote(net,cfg.NET_TRAINING_EPOCHS))
+                        else:
+                            inter_results.append(train_net_on_gpu.remote(net,cfg.NET_TRAINING_EPOCHS))
+
+                elif cfg.DEVICE == "cuda":
+                    for net in training_nets:
+                        for layer in layers_c_net:
+                            try:
+                                net = transfer_weights(curios_net,net,layer)
+                                print(f"Weights transferred successfully")
+                            except Exception:
+                                print("Weights can not transfer")
+                    for net in training_nets:
+                        inter_results.append(train_net_on_gpu.remote(net,cfg.NET_TRAINING_EPOCHS))
+
+                else:
+                    raise ValueError(f"{cfg.DEVICE} is not a valid device")
+                fitness = []
+                for i,result in enumerate(inter_results):
+                    try:
+                        fitness.append([ray.get(result),training_prompts[i],temperature,net_paths[i],0.0])
+
+                        if fitness[i][0] <= prev_best_score:
+                            prev_best_net_path = net_paths[i]
+                            prev_best_prompt = training_prompts[i]
+                            prev_best_score = fitness[i][0]
+                            temperature += 0.05
+                        else:
+                            temperature -= 0.05
+
+                        if temperature > 1.0:
+                            temperature = 1.0
+                        elif temperature < 0.1:
+                            temperature = 0.1
+
+                        fitness[i][2] = temperature
+                    except Exception:
+                        print("not trainable due to fitness 2")
+                
+                try:
+                    infer_results = []
+                    for net in training_nets:
+                        infer_results.append(forward_pass_on_gpu.remote(net))
+                    
+                    
+                    for i,result in enumerate(infer_results):
+                        try:
+                            res = ray.get(result)
+                            fitness[i][4] = res[1]
+                        except Exception:
+                            print("not trainable due to inference speed 1")
+
+                    
+                except Exception:
+                    print("not trainable due to inference speed 2")
+                        
+        dummy_inputs = torch.zeros((1, 3, 32, 32))
+        net_beh_list = []
+        prompt_beh_list = []
+        for loss_x,prompt_x,temp_x,net_p,infer_speed in fitness:
+            _nn = read_python_file(net_p) #Avoiding sytax errors
+            Net = get_class(net_p)             
+            net = Net()
+            try:
+                flps = FlopCountAnalysis(net, dummy_inputs)
+                flops = flps.total()
+                depth_width_ratio = get_network_width_depth_ratio(net)
+
+            except Exception:
+                try:
+                    flps = FlopCountAnalysis(curios_net, dummy_inputs)
+                    flops = flps.total()
+                    depth_width_ratio = get_network_width_depth_ratio(curios_net)
+                except Exception:
+                    flops = 0
+                    depth_width_ratio = 1
+
+            print(f"flops, infer_speed, depth_width_ratio:  {flops}, {infer_speed}, {depth_width_ratio}")
+            s_net = to_specie(net,loss_x,np.array([depth_width_ratio, flops]),net_p)
+            net_added = __add_to_archive(s_net, s_net.desc, nets_archive, kdt, type_ind = "network")
+
+            if net_added:
+                prompt_fit = 1.0 
+            else:
+                prompt_fit = 0.0
+
+            s_prompt = to_specie(net,prompt_fit,np.array([prompt_to_int[prompt_x], temp_x]),net_p)
+            prompt_added = __add_to_archive(s_prompt, s_prompt.desc, prompt_archive, kdt, type_ind = "prompt")
+            if not net_added:
+                s_prompt.curiosity = s_prompt.curiosity - 0.5
+            elif net_added:
+                s_prompt.curiosity = s_prompt.curiosity + 1.0 
+        
+        if gen_i % 1 == 0:
+            
+            cm.__save_archive(nets_archive, gen_i,name="net")
+            cm.__save_archive(prompt_archive, gen_i,name="prompt")
+        # write log
+        if log_file != None:
+            fit_list = np.array([x.fitness for x in nets_archive.values()])
+            log_file.write("{} {} {} {} {} {} {}\n".format(gen_i, len(nets_archive.keys()), len(prompt_archive.keys()),
+                    fit_list.min(), np.mean(fit_list), np.median(fit_list),
+                    np.percentile(fit_list, 5), np.percentile(fit_list, 95)))
+            log_file.flush()
+    cm.__save_archive(nets_archive, gen_i,name="net")
+    cm.__save_archive(prompt_archive, gen_i,name="prompt")
+    return nets_archive,prompt_archive
+
+if __name__ == "__main__":
+    main()
+

LLMatic-main/logs/gen-nets_codegen-6B-mono_networks-10_temp-0.0_net-training-epochs-50_niches-100_infer-and-flops-as-bd/exp_results.csv

@@ -0,0 +1,4 @@
+generations,best_loss,used_prompt
+generations,best_loss,used_prompt
+generations,best_loss,used_prompt
+generations,best_loss,used_prompt

LLMatic-main/map_elites/LICENSE

@@ -0,0 +1,518 @@
+
+  CeCILL FREE SOFTWARE LICENSE AGREEMENT
+
+Version 2.1 dated 2013-06-21
+
+
+    Notice
+
+This Agreement is a Free Software license agreement that is the result
+of discussions between its authors in order to ensure compliance with
+the two main principles guiding its drafting:
+
+  * firstly, compliance with the principles governing the distribution
+    of Free Software: access to source code, broad rights granted to users,
+  * secondly, the election of a governing law, French law, with which it
+    is conformant, both as regards the law of torts and intellectual
+    property law, and the protection that it offers to both authors and
+    holders of the economic rights over software.
+
+The authors of the CeCILL (for Ce[a] C[nrs] I[nria] L[ogiciel] L[ibre]) 
+license are: 
+
+Commissariat � l'�nergie atomique et aux �nergies alternatives - CEA, a
+public scientific, technical and industrial research establishment,
+having its principal place of business at 25 rue Leblanc, immeuble Le
+Ponant D, 75015 Paris, France.
+
+Centre National de la Recherche Scientifique - CNRS, a public scientific
+and technological establishment, having its principal place of business
+at 3 rue Michel-Ange, 75794 Paris cedex 16, France.
+
+Institut National de Recherche en Informatique et en Automatique -
+Inria, a public scientific and technological establishment, having its
+principal place of business at Domaine de Voluceau, Rocquencourt, BP
+105, 78153 Le Chesnay cedex, France.
+
+
+    Preamble
+
+The purpose of this Free Software license agreement is to grant users
+the right to modify and redistribute the software governed by this
+license within the framework of an open source distribution model.
+
+The exercising of this right is conditional upon certain obligations for
+users so as to preserve this status for all subsequent redistributions.
+
+In consideration of access to the source code and the rights to copy,
+modify and redistribute granted by the license, users are provided only
+with a limited warranty and the software's author, the holder of the
+economic rights, and the successive licensors only have limited liability.
+
+In this respect, the risks associated with loading, using, modifying
+and/or developing or reproducing the software by the user are brought to
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+security. This Agreement may be freely reproduced and published,
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+
+This Agreement may apply to any or all software for which the holder of
+the economic rights decides to submit the use thereof to its provisions.
+
+Frequently asked questions can be found on the official website of the
+CeCILL licenses family (http://www.cecill.info/index.en.html) for any 
+necessary clarification.
+
+
+    Article 1 - DEFINITIONS
+
+For the purpose of this Agreement, when the following expressions
+commence with a capital letter, they shall have the following meaning:
+
+Agreement: means this license agreement, and its possible subsequent
+versions and annexes.
+
+Software: means the software in its Object Code and/or Source Code form
+and, where applicable, its documentation, "as is" when the Licensee
+accepts the Agreement.
+
+Initial Software: means the Software in its Source Code and possibly its
+Object Code form and, where applicable, its documentation, "as is" when
+it is first distributed under the terms and conditions of the Agreement.
+
+Modified Software: means the Software modified by at least one
+Contribution.
+
+Source Code: means all the Software's instructions and program lines to
+which access is required so as to modify the Software.
+
+Object Code: means the binary files originating from the compilation of
+the Source Code.
+
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+Software.
+
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+
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+distributes the Software under the Agreement.
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+enables supplementary functions or services in addition to those offered
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+
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+that they both execute in the same address space.
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+subsequent version, as published by the Free Software Foundation Inc.
+
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+subsequent version, as published by the European Commission.
+
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+
+These expressions may be used both in singular and plural form.
+
+
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+
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+Software as set forth in Article 5 <#scope> hereinafter for the whole
+term of the protection granted by the rights over said Software.
+
+
+    Article 3 - ACCEPTANCE
+
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+conditions of this Agreement upon the occurrence of the first of the
+following events:
+
+  * (i) loading the Software by any or all means, notably, by
+    downloading from a remote server, or by loading from a physical medium;
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+    hereunder.
+
+3.2 One copy of the Agreement, containing a notice relating to the
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+fact that its use is restricted to experienced users has been provided
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+
+
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+
+
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+
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+the Licensee as set forth in Article 3.1 <#accepting>.
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+
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+
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+
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+
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+
+ 1. permanent or temporary reproduction of all or part of the Software
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+
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+
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+
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+
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+
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+
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+ 2. a notice relating to the limitation of both the Licensor's warranty
+    and liability as set forth in Articles 8 and 9,
+
+and, in the event that only the object code of the Modified Software is
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+    from the distribution of the Modified Software, it being understood
+    that the additional acquisition cost of the source code shall not
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+
+
+        5.3.3 DISTRIBUTION OF EXTERNAL MODULES
+
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+conditions of this Agreement do not apply to said External Module, that
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+
+        5.3.4 COMPATIBILITY WITH OTHER LICENSES
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+of the versions of the GNU GPL, GNU Affero GPL and/or EUPL in the
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+
+
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+
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+
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+
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+
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+jurisdiction, by the more diligent Party.

LLMatic-main/map_elites/common.py

@@ -0,0 +1,231 @@
+#! /usr/bin/env python
+#| This file is a part of the pymap_elites framework.
+#| Copyright 2019, INRIA
+#| Main contributor(s):
+#| Jean-Baptiste Mouret, [email protected]
+#| Eloise Dalin , [email protected]
+#| Pierre Desreumaux , [email protected]
+#|
+#|
+#| **Main paper**: Mouret JB, Clune J. Illuminating search spaces by
+#| mapping elites. arXiv preprint arXiv:1504.04909. 2015 Apr 20.
+#|
+#| This software is governed by the CeCILL license under French law
+#| and abiding by the rules of distribution of free software.  You
+#| can use, modify and/ or redistribute the software under the terms
+#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
+#| following URL "http://www.cecill.info".
+#|
+#| As a counterpart to the access to the source code and rights to
+#| copy, modify and redistribute granted by the license, users are
+#| provided only with a limited warranty and the software's author,
+#| the holder of the economic rights, and the successive licensors
+#| have only limited liability.
+#|
+#| In this respect, the user's attention is drawn to the risks
+#| associated with loading, using, modifying and/or developing or
+#| reproducing the software by the user in light of its specific
+#| status of free software, that may mean that it is complicated to
+#| manipulate, and that also therefore means that it is reserved for
+#| developers and experienced professionals having in-depth computer
+#| knowledge. Users are therefore encouraged to load and test the
+#| software's suitability as regards their requirements in conditions
+#| enabling the security of their systems and/or data to be ensured
+#| and, more generally, to use and operate it in the same conditions
+#| as regards security.
+#|
+#| The fact that you are presently reading this means that you have
+#| had knowledge of the CeCILL license and that you accept its terms.
+#
+
+import math
+import numpy as np
+import multiprocessing
+from pathlib import Path
+import sys
+import random
+from collections import defaultdict
+from sklearn.cluster import KMeans
+
+default_params = \
+    {
+        # more of this -> higher-quality CVT
+        "cvt_samples": 25000,
+        # we evaluate in batches to paralleliez
+        "batch_size": 100,
+        # proportion of niches to be filled before starting
+        "random_init": 0.1,
+        # batch for random initialization
+        "random_init_batch": 100,
+        # when to write results (one generation = one batch)
+        "dump_period": 10000,
+        # do we use several cores?
+        "parallel": True,
+        # do we cache the result of CVT and reuse?
+        "cvt_use_cache": True,
+        # min/max of parameters
+        "min": 0,
+        "max": 1,
+        # only useful if you use the 'iso_dd' variation operator
+        "iso_sigma": 0.01,
+        "line_sigma": 0.2
+    }
+
+class Species:
+    def __init__(self, x, desc, fitness, net_path, centroid=None):
+        self.x = x
+        self.desc = desc
+        self.fitness = fitness
+        self.centroid = centroid
+        self.curiosity = 0
+        self.net_path = net_path
+
+
+def polynomial_mutation(x):
+    '''
+    Cf Deb 2001, p 124 ; param: eta_m
+    '''
+    y = x.copy()
+    eta_m = 5.0;
+    r = np.random.random(size=len(x))
+    for i in range(0, len(x)):
+        if r[i] < 0.5:
+            delta_i = math.pow(2.0 * r[i], 1.0 / (eta_m + 1.0)) - 1.0
+        else:
+            delta_i = 1 - math.pow(2.0 * (1.0 - r[i]), 1.0 / (eta_m + 1.0))
+        y[i] += delta_i
+    return y
+
+def sbx(x, y, params):
+    '''
+    SBX (cf Deb 2001, p 113) Simulated Binary Crossover
+
+    A large value ef eta gives a higher probablitity for
+    creating a `near-parent' solutions and a small value allows
+    distant solutions to be selected as offspring.
+    '''
+    eta = 10.0
+    xl = params['min']
+    xu = params['max']
+    z = x.copy()
+    r1 = np.random.random(size=len(x))
+    r2 = np.random.random(size=len(x))
+
+    for i in range(0, len(x)):
+        if abs(x[i] - y[i]) > 1e-15:
+            x1 = min(x[i], y[i])
+            x2 = max(x[i], y[i])
+
+            beta = 1.0 + (2.0 * (x1 - xl) / (x2 - x1))
+            alpha = 2.0 - beta ** -(eta + 1)
+            rand = r1[i]
+            if rand <= 1.0 / alpha:
+                beta_q = (rand * alpha) ** (1.0 / (eta + 1))
+            else:
+                beta_q = (1.0 / (2.0 - rand * alpha)) ** (1.0 / (eta + 1))
+
+            c1 = 0.5 * (x1 + x2 - beta_q * (x2 - x1))
+
+            beta = 1.0 + (2.0 * (xu - x2) / (x2 - x1))
+            alpha = 2.0 - beta ** -(eta + 1)
+            if rand <= 1.0 / alpha:
+                beta_q = (rand * alpha) ** (1.0 / (eta + 1))
+            else:
+                beta_q = (1.0 / (2.0 - rand * alpha)) ** (1.0 / (eta + 1))
+            c2 = 0.5 * (x1 + x2 + beta_q * (x2 - x1))
+
+            c1 = min(max(c1, xl), xu)
+            c2 = min(max(c2, xl), xu)
+
+            if r2[i] <= 0.5:
+                z[i] = c2
+            else:
+                z[i] = c1
+    return z
+
+
+def iso_dd(x, y, params):
+    '''
+    Iso+Line
+    Ref:
+    Vassiliades V, Mouret JB. Discovering the elite hypervolume by leveraging interspecies correlation.
+    GECCO 2018
+    '''
+    assert(x.shape == y.shape)
+    p_max = np.array(params["max"])
+    p_min = np.array(params["min"])
+    a = np.random.normal(0, params['iso_sigma'], size=len(x))
+    b = np.random.normal(0, params['line_sigma'])
+    norm = np.linalg.norm(x - y)
+    z = x.copy() + a + b * (x - y)
+    return np.clip(z, p_min, p_max)
+
+
+def variation(x, z, params):
+    assert(x.shape == z.shape)
+    y = sbx(x, z, params)
+    return y
+
+def __centroids_filename(k, dim):
+    return 'centroids_' + str(k) + '_' + str(dim) + '.dat'
+
+
+def __write_centroids(centroids):
+    k = centroids.shape[0]
+    dim = centroids.shape[1]
+    filename = __centroids_filename(k, dim)
+    with open(filename, 'w') as f:
+        for p in centroids:
+            for item in p:
+                f.write(str(item) + ' ')
+            f.write('\n')
+
+
+def cvt(k, dim, samples, cvt_use_cache=True):
+    # check if we have cached values
+    fname = __centroids_filename(k, dim)
+    if cvt_use_cache:
+        if Path(fname).is_file():
+            print("WARNING: using cached CVT:", fname)
+            return np.loadtxt(fname)
+    # otherwise, compute cvt
+    print("Computing CVT (this can take a while...):", fname)
+
+    x = np.random.rand(samples, dim)
+    k_means = KMeans(init='k-means++', n_clusters=k,
+                     n_init=1, verbose=1)#,algorithm="full")
+    k_means.fit(x)
+    __write_centroids(k_means.cluster_centers_)
+
+    return k_means.cluster_centers_
+
+
+def make_hashable(array):
+    return tuple(map(float, array))
+
+
+def parallel_eval(evaluate_function, to_evaluate, pool, params):
+    if params['parallel'] == True:
+        s_list = pool.map(evaluate_function, to_evaluate)
+    else:
+        s_list = map(evaluate_function, to_evaluate)
+    return list(s_list)
+
+# format: fitness, centroid, desc, genome \n
+# fitness, centroid, desc and x are vectors
+def __save_archive(archive, gen,name="net"):
+    def write_array(a, f):
+        for i in a:
+            f.write(str(i) + " ")
+    filename = name+'archive_' + str(gen) + '.dat'
+    with open(filename, 'w') as f:
+        for k in archive.values():
+            f.write(k.net_path + " ")
+            f.write(str(k.fitness) + ' ')
+            write_array(k.centroid, f)
+            f.write(str(k.curiosity) + " ")
+            f.write(str(k.desc[0]) + " ")
+            f.write(str(k.desc[1]) + " ")
+            
+            #write_array(k.x, f)
+            f.write("\n")

LLMatic-main/map_elites/cvt.py

@@ -0,0 +1,142 @@
+#! /usr/bin/env python
+#| This file is a part of the pymap_elites framework.
+#| Copyright 2019, INRIA
+#| Main contributor(s):
+#| Jean-Baptiste Mouret, [email protected]
+#| Eloise Dalin , [email protected]
+#| Pierre Desreumaux , [email protected]
+#|
+#|
+#| **Main paper**: Mouret JB, Clune J. Illuminating search spaces by
+#| mapping elites. arXiv preprint arXiv:1504.04909. 2015 Apr 20.
+#|
+#| This software is governed by the CeCILL license under French law
+#| and abiding by the rules of distribution of free software.  You
+#| can use, modify and/ or redistribute the software under the terms
+#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
+#| following URL "http://www.cecill.info".
+#|
+#| As a counterpart to the access to the source code and rights to
+#| copy, modify and redistribute granted by the license, users are
+#| provided only with a limited warranty and the software's author,
+#| the holder of the economic rights, and the successive licensors
+#| have only limited liability.
+#|
+#| In this respect, the user's attention is drawn to the risks
+#| associated with loading, using, modifying and/or developing or
+#| reproducing the software by the user in light of its specific
+#| status of free software, that may mean that it is complicated to
+#| manipulate, and that also therefore means that it is reserved for
+#| developers and experienced professionals having in-depth computer
+#| knowledge. Users are therefore encouraged to load and test the
+#| software's suitability as regards their requirements in conditions
+#| enabling the security of their systems and/or data to be ensured
+#| and, more generally, to use and operate it in the same conditions
+#| as regards security.
+#|
+#| The fact that you are presently reading this means that you have
+#| had knowledge of the CeCILL license and that you accept its terms.
+
+import math
+import numpy as np
+import multiprocessing
+
+# from scipy.spatial import cKDTree : TODO -- faster?
+from sklearn.neighbors import KDTree
+
+from map_elites import common as cm
+
+
+
+def __add_to_archive(s, centroid, archive, kdt):
+    niche_index = kdt.query([centroid], k=1)[1][0][0]
+    niche = kdt.data[niche_index]
+    n = cm.make_hashable(niche)
+    s.centroid = n
+    if n in archive:
+        if s.fitness > archive[n].fitness:
+            archive[n] = s
+            return 1
+        return 0
+    else:
+        archive[n] = s
+        return 1
+
+
+# evaluate a single vector (x) with a function f and return a species
+# t = vector, function
+def __evaluate(t):
+    z, f = t  # evaluate z with function f
+    fit, desc = f(z)
+    return cm.Species(z, desc, fit)
+
+# map-elites algorithm (CVT variant)
+def compute(dim_map, dim_x, f,
+            n_niches=1000,
+            max_evals=1e5,
+            params=cm.default_params,
+            log_file=None,
+            variation_operator=cm.variation):
+    """CVT MAP-Elites
+       Vassiliades V, Chatzilygeroudis K, Mouret JB. Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm. IEEE Transactions on Evolutionary Computation. 2017 Aug 3;22(4):623-30.
+
+       Format of the logfile: evals archive_size max mean median 5%_percentile, 95%_percentile
+
+    """
+    # setup the parallel processing pool
+    num_cores = multiprocessing.cpu_count()
+    pool = multiprocessing.Pool(num_cores)
+
+    # create the CVT
+    c = cm.cvt(n_niches, dim_map,
+              params['cvt_samples'], params['cvt_use_cache'])
+    kdt = KDTree(c, leaf_size=30, metric='euclidean')
+    cm.__write_centroids(c)
+
+    archive = {} # init archive (empty)
+    n_evals = 0 # number of evaluations since the beginning
+    b_evals = 0 # number evaluation since the last dump
+
+    # main loop
+    while (n_evals < max_evals):
+        to_evaluate = []
+        # random initialization
+        if len(archive) <= params['random_init'] * n_niches:
+            for i in range(0, params['random_init_batch']):
+                x = np.random.uniform(low=params['min'], high=params['max'], size=dim_x)
+                to_evaluate += [(x, f)]
+        else:  # variation/selection loop
+            keys = list(archive.keys())
+            # we select all the parents at the same time because randint is slow
+            rand1 = np.random.randint(len(keys), size=params['batch_size'])
+            rand2 = np.random.randint(len(keys), size=params['batch_size'])
+            for n in range(0, params['batch_size']):
+                # parent selection
+                x = archive[keys[rand1[n]]]
+                y = archive[keys[rand2[n]]]
+                # copy & add variation
+                z = variation_operator(x.x, y.x, params)
+                to_evaluate += [(z, f)]
+        # evaluation of the fitness for to_evaluate
+        s_list = cm.parallel_eval(__evaluate, to_evaluate, pool, params)
+        # natural selection
+        for s in s_list:
+            __add_to_archive(s, s.desc, archive, kdt)
+        # count evals
+        n_evals += len(to_evaluate)
+        b_evals += len(to_evaluate)
+
+        # write archive
+        if b_evals >= params['dump_period'] and params['dump_period'] != -1:
+            print("[{}/{}]".format(n_evals, int(max_evals)), end=" ", flush=True)
+            cm.__save_archive(archive, n_evals)
+            b_evals = 0
+        # write log
+        if log_file != None:
+            fit_list = np.array([x.fitness for x in archive.values()])
+            log_file.write("{} {} {} {} {} {} {}\n".format(n_evals, len(archive.keys()),
+                    fit_list.max(), np.mean(fit_list), np.median(fit_list),
+                    np.percentile(fit_list, 5), np.percentile(fit_list, 95)))
+            log_file.flush()
+    cm.__save_archive(archive, n_evals)
+    return archive

LLMatic-main/map_elites/multitask.py

@@ -0,0 +1,242 @@
+#! /usr/bin/env python
+#| This file is a part of the pymap_elites framework.
+#| Copyright 2019, INRIA
+#| Main contributor(s):
+#| Jean-Baptiste Mouret, [email protected]
+#| Eloise Dalin , [email protected]
+#| Pierre Desreumaux , [email protected]
+#|
+#|
+#| **Main paper**: Mouret JB, Clune J. Illuminating search spaces by
+#| mapping elites. arXiv preprint arXiv:1504.04909. 2015 Apr 20.
+#|
+#| This software is governed by the CeCILL license under French law
+#| and abiding by the rules of distribution of free software.  You
+#| can use, modify and/ or redistribute the software under the terms
+#| of the CeCILL license as circulated by CEA, CNRS and INRIA at the
+#| following URL "http://www.cecill.info".
+#|
+#| As a counterpart to the access to the source code and rights to
+#| copy, modify and redistribute granted by the license, users are
+#| provided only with a limited warranty and the software's author,
+#| the holder of the economic rights, and the successive licensors
+#| have only limited liability.
+#|
+#| In this respect, the user's attention is drawn to the risks
+#| associated with loading, using, modifying and/or developing or
+#| reproducing the software by the user in light of its specific
+#| status of free software, that may mean that it is complicated to
+#| manipulate, and that also therefore means that it is reserved for
+#| developers and experienced professionals having in-depth computer
+#| knowledge. Users are therefore encouraged to load and test the
+#| software's suitability as regards their requirements in conditions
+#| enabling the security of their systems and/or data to be ensured
+#| and, more generally, to use and operate it in the same conditions
+#| as regards security.
+#|
+#| The fact that you are presently reading this means that you have
+#| had knowledge of the CeCILL license and that you accept its terms.
+#
+# from scipy.spatial import cKDTree : TODO
+
+import math
+import numpy as np
+import multiprocessing
+from pathlib import Path
+import sys
+import random
+from collections import defaultdict
+from sklearn.neighbors import KDTree
+from scipy.spatial import distance
+
+from map_elites import common as cm
+
+
+def add_to_archive(s, archive):
+    centroid = cm.make_hashable(s.centroid)
+    if centroid in archive:
+        if s.fitness > archive[centroid].fitness:
+            archive[centroid] = s
+            return 1
+        return 0
+    else:
+        archive[centroid] = s
+        return 1
+
+# evaluate a single vector (z) with a function f and return a species
+# t = vector, function
+def __evaluate(t):
+    z, f, task, centroid, _ = t
+    fit = f(z, task)
+    return cm.Species(z, task, fit, centroid)
+
+# bandit opt for optimizing tournament size
+# probability matching / Adaptive pursuit Thierens GECCO 2005
+# UCB: schoenauer / Sebag
+# TODO : params for values, and params for window
+def bandit(successes, n_niches):
+    n = 0
+    for v in successes.values():
+        n += len(v)
+    v = [1, 10, 50, 100, 500]#, 1000]
+    if len(successes.keys()) < len(v):
+        return random.choice(v)
+    ucb = []
+    for k in v:
+        x = [i[0] for i in successes[k]]
+        mean = sum(x) / float(len(x)) # 100 = batch size??
+        n_a = len(x)
+        ucb += [mean +  math.sqrt(2 * math.log(n) / n_a)]
+    a = np.argmax(ucb)
+    t_size = v[a]
+    return t_size
+
+# select the niche according to
+def select_niche(x, z, f, centroids, tasks, t_size, params, use_distance=False):
+    to_evaluate = []
+    if not use_distance:
+        # No distance: evaluate on a random niche
+        niche = np.random.randint(len(tasks))
+        to_evaluate += [(z, f, tasks[niche], centroids[niche, :], params)]
+    else:
+        # we select the parent (a single one), then we select the niche
+        # with a tournament based on the task distance
+        # the size of the tournament depends on the bandit algorithm
+        niches_centroids = []
+        niches_tasks = [] # TODO : use a kd-tree
+        rand = np.random.randint(centroids.shape[0], size=t_size)
+        for p in range(0, t_size):
+            n = rand[p]
+            niches_centroids += [centroids[n, :]]
+            niches_tasks += [tasks[n]]
+        cd = distance.cdist(niches_centroids, [x.centroid], 'euclidean')
+        cd_min = np.argmin(cd)
+        to_evaluate += [(z, f, niches_tasks[cd_min], niches_centroids[cd_min], params)]
+    return to_evaluate
+
+
+def compute(dim_map=-1,
+            dim_x=-1,
+            f=None,
+            max_evals=1e5,
+            centroids=[],
+            tasks=[],
+            variation_operator=cm.variation,
+            params=cm.default_params,
+            log_file=None):
+    """Multi-task MAP-Elites
+    - if there is no centroid : random assignation of niches
+    - if there is no task: use the centroids as tasks
+    - if there is a centroid list: use the centroids to compute distances
+    when using the distance, use the bandit to select the tournament size (cf paper):
+
+    Format of the logfile: evals archive_size max mean 5%_percentile, 95%_percentile
+
+    Reference:
+    Mouret and Maguire (2020). Quality Diversity for Multitask Optimization
+    Proceedings of ACM GECCO.
+    """
+    print(params)
+    assert(f != None)
+    assert(dim_x != -1)
+    # handle the arguments
+    use_distance = False
+    if tasks != [] and centroids != []:
+        use_distance = True
+    elif tasks == [] and centroids != []:
+        # if no task, we use the centroids as tasks
+        tasks = centroids
+        use_distance = True
+    elif tasks != [] and centroids == []:
+        # if no centroid, we create indices so that we can index the archive by centroid
+        centroids = np.arange(0, len(tasks)).reshape(len(tasks), 1)
+        use_distance = False
+    else:
+        raise ValueError('Multi-task MAP-Elites: you need to specify a list of task, a list of centroids, or both')
+    print("Multitask-MAP-Elites:: using distance =>", use_distance)
+
+    assert(len(tasks) == len(centroids))
+    n_tasks = len(tasks)
+
+    # init archive (empty)
+    archive = {}
+
+    init_count = 0
+
+    # init multiprocessing
+    num_cores = multiprocessing.cpu_count()
+    pool = multiprocessing.Pool(num_cores)
+
+    # main loop
+    n_evals = 0 # number of evaluations
+    b_evals = 0 # number evaluation since the last dump
+    t_size = 1  # size of the tournament (if using distance) [will be selected by the bandit]
+    successes = defaultdict(list) # count the successes
+    while (n_evals < max_evals):
+        to_evaluate = []
+        to_evaluate_centroid = []
+        if len(archive) <= params['random_init'] * n_tasks:
+            # initialize the map with random individuals
+            for i in range(0, params['random_init_batch']):
+                # create a random individual
+                x = np.random.uniform(low=params['min'], high=params['max'], size=dim_x)
+                # we take a random task
+                n = np.random.randint(0, n_tasks)
+                to_evaluate += [(x, f, tasks[n], centroids[n], params)]
+            s_list = cm.parallel_eval(__evaluate, to_evaluate, pool, params)
+            n_evals += len(to_evaluate)
+            b_evals += len(to_evaluate)
+            for i in range(0, len(list(s_list))):
+                add_to_archive(s_list[i], archive)
+        else:
+            # main variation/selection loop
+            keys = list(archive.keys())
+            # we do all the randint together because randint is slow
+            rand1 = np.random.randint(len(keys), size=params['batch_size'])
+            rand2 = np.random.randint(len(keys), size=params['batch_size'])
+            for n in range(0, params['batch_size']):
+                # parent selection
+                x = archive[keys[rand1[n]]]
+                y = archive[keys[rand2[n]]]
+                # copy & add variation
+                z = variation_operator(x.x, y.x, params)
+                # different modes for multi-task (to select the niche)
+                to_evaluate += select_niche(x, z, f, centroids, tasks, t_size, params, use_distance)
+            # parallel evaluation of the fitness
+            s_list = cm.parallel_eval(__evaluate, to_evaluate, pool, params)
+            n_evals += len(to_evaluate)
+            b_evals += len(to_evaluate)
+            # natural selection
+            suc = 0
+            for i in range(0, len(list(s_list))):
+                suc += add_to_archive(s_list[i], archive)
+            if use_distance:
+                successes[t_size] += [(suc, n_evals)]
+        if use_distance: # call the bandit to optimize t_size
+            t_size = bandit(successes, n_tasks)
+
+        # write archive
+        if params['dump_period'] != -1 and b_evals > params['dump_period']:
+            cm.__save_archive(archive, n_evals)
+            b_evals = 0
+            n_e = [len(v) for v in successes.values()]
+            print(n_evals, n_e)
+            np.savetxt('t_size.dat', np.array(n_e))
+        if log_file != None:
+            fit_list = np.array([x.fitness for x in archive.values()])
+            log_file.write("{} {} {} {} {} {} {}\n".format(n_evals, len(archive.keys()), fit_list.max(), np.mean(fit_list), np.median(fit_list), np.percentile(fit_list, 5), np.percentile(fit_list, 95)))
+            log_file.flush()
+    cm.__save_archive(archive, n_evals)
+    return archive
+
+
+# a small test
+if __name__ == "__main__":
+    def rastrigin(xx):
+        x = xx * 10.0 - 5.0
+        f = 10 * x.shape[0]
+        for i in range(0, x.shape[0]):
+            f += x[i] * x[i] - 10 * math.cos(2 * math.pi * x[i])
+        return -f, np.array([xx[0], xx[1]])
+    # CVT-based version
+    my_map = compute(dim_map=2, dim_x = 10, n_niches=1500, f=rastrigin)

LLMatic-main/mutation_models.py

@@ -0,0 +1,73 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, CodeGenForCausalLM
+#import openai
+import os
+import ray
+
+from conf.config import Config
+
+cfg = Config()
+
+
+def codex_mutate(cfg: Config, prompt, model="code-davinci-002",temperature=0.5):
+    
+    os.environ['OPENAI_API_KEY'] = ""# ENETR OPENAI API KEY HERE
+    openai.api_key = os.getenv("OPENAI_API_KEY")
+    return openai.Completion.create(
+                                    model=model,
+                                    prompt=prompt,
+                                    max_tokens=300,
+                                    temperature=temperature,
+                                    ) 
+
+
+#['codegen-350M-multi', 'codegen-2B-multi', 'codegen-6B-multi', 'codegen-16B-multi', 'codegen-350M-mono', 'codegen-2B-mono', 'codegen-6B-mono', 'codegen-16B-mono']
+#@ray.remote(num_cpus=cfg.NUM_CPUS)  # FIXME: Number of parallel processes should be configured globally.
+@ray.remote(num_gpus=cfg.NUM_GPUS)
+def codegen_mutate(cfg: Config, prompt, temperature):
+    diff = False
+
+    if diff:
+        # model = "/mnt/lustre/users/mnasir/NAS-LLM/diff-codegen-6B"
+        model = os.path.join(cfg.SAVE_DIR, "diff-codegen-6B")
+        tokenizer = AutoTokenizer.from_pretrained(model)#.to(cfg.DEVICE)
+        tokenizer.padding_side = 'left'
+        tokenizer.pad_token = tokenizer.eos_token
+        model = CodeGenForCausalLM.from_pretrained(model).to(cfg.DEVICE)
+        inputs = tokenizer(prompt, return_tensors='pt', padding=True)#.to(device)
+        #model.config.use_cache = True
+        sample = model.generate(**inputs, temperature=0.5, max_length=len(inputs[0]) + 300)
+        
+        return tokenizer.decode(sample[0][len(inputs[0]):])
+    else:
+        model = f'Salesforce/{cfg.MUTATION}'
+        # model = "/mnt/lustre/users/mnasir/NAS-LLM/codegen-6B"
+        #model = "/mnt/lustre/users/mnasir/NAS-LLM/diff-codegen-6B"
+        #model = os.path.join(cfg.SAVE_DIR, "codegen-6B")
+
+        # TODO: Should be doing something like this to download the model automatically.
+        # model = os.path.join(cfg.SAVE_DIR, cfg.MUTATION)
+        # if not os.path.exists(model):
+        #     # TODO: `Salesforce` part should not be hardcoded / should be configurable so that we can download models 
+        #     # from other sources.
+        #     model = f'Salesforce/{cfg.MUTATION}'
+
+        tokenizer = AutoTokenizer.from_pretrained(model)
+        model = AutoModelForCausalLM.from_pretrained(model).to(cfg.DEVICE)
+
+        # TODO: Above lines may have downloaded a fresh model if it was not already present. Now, copy the model file 
+        # to the desired location if necessary.
+
+        inputs = tokenizer(prompt, return_tensors="pt").to(cfg.DEVICE)
+        sample = model.generate(**inputs, max_length=350 + len(inputs[0]),temperature=temperature,num_beams=1,do_sample=True)
+        return tokenizer.decode(sample[0][len(inputs[0]):], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"])
+
+
+def replace_word_mutation(sentence):
+    if "Add" in sentence:
+        return sentence.replace("Add", "Delete")
+    elif "Delete" in sentence:
+        return sentence.replace("Delete", "Add")
+    else:
+        return sentence
+

LLMatic-main/outputs/2024-09-03/22-14-50/.hydra/config.yaml

@@ -0,0 +1,19 @@
+RANDOM_INIT_NETS: 10
+INIT_NUM_NETS: 10
+ROLL_OUTS: 4
+NUM_NETS: 10
+START_FROM_CHECKPOINT: false
+RANDOM_NETWORKS: false
+MUTATION: codegen-6B-mono
+GENERATIONS: 200
+NET_TRAINING_EPOCHS: 50
+TEMPERATURE: 0.0
+DEVICE: cuda
+NUM_PROCESSES: 15
+RAY: true
+NUM_CPUS: 15
+NUM_GPUS: 1
+NUM_GPUS_TOTAL: 4
+DIM_MAP: 2
+N_NICHES: 100
+SAVE_DIR: ./

LLMatic-main/outputs/2024-09-03/22-14-50/.hydra/hydra.yaml

@@ -0,0 +1,182 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+    timeout_min: 2880
+    cpus_per_task: 1
+    gpus_per_node: 1
+    tasks_per_node: 1
+    mem_gb: 30
+    nodes: 1
+    name: ${hydra.job.name}
+    stderr_to_stdout: false
+    _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.SlurmLauncher
+    partition: null
+    qos: null
+    comment: null
+    constraint: null
+    exclude: null
+    gres: null
+    cpus_per_gpu: null
+    gpus_per_task: null
+    mem_per_gpu: null
+    mem_per_cpu: null
+    account: null
+    signal_delay_s: 120
+    max_num_timeout: 0
+    additional_parameters: {}
+    array_parallelism: 256
+    setup: null
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params:
+      NUM_NETS: 10, 20
+      NET_TRAINING_EPOCHS: 10, 30
+      TEMPERATURE: 0.3, 0.5, 0.7
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task: []
+  job:
+    name: llmatic
+    chdir: null
+    override_dirname: ''
+    id: ???
+    num: ???
+    config_name: config
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.1
+    version_base: '1.3'
+    cwd: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\conf
+      schema: file
+      provider: main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\outputs\2024-09-03\22-14-50
+    choices:
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: submitit_slurm
+      hydra/output: default
+  verbose: false

LLMatic-main/outputs/2024-09-03/22-14-50/.hydra/overrides.yaml

@@ -0,0 +1 @@
+[]

LLMatic-main/outputs/2024-09-03/23-04-26/.hydra/config.yaml

@@ -0,0 +1,19 @@
+RANDOM_INIT_NETS: 10
+INIT_NUM_NETS: 10
+ROLL_OUTS: 4
+NUM_NETS: 10
+START_FROM_CHECKPOINT: false
+RANDOM_NETWORKS: false
+MUTATION: codegen-6B-mono
+GENERATIONS: 200
+NET_TRAINING_EPOCHS: 50
+TEMPERATURE: 0.0
+DEVICE: cuda
+NUM_PROCESSES: 15
+RAY: true
+NUM_CPUS: 15
+NUM_GPUS: 1
+NUM_GPUS_TOTAL: 4
+DIM_MAP: 2
+N_NICHES: 100
+SAVE_DIR: ./

LLMatic-main/outputs/2024-09-03/23-04-26/.hydra/hydra.yaml

@@ -0,0 +1,182 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+    timeout_min: 2880
+    cpus_per_task: 1
+    gpus_per_node: 1
+    tasks_per_node: 1
+    mem_gb: 30
+    nodes: 1
+    name: ${hydra.job.name}
+    stderr_to_stdout: false
+    _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.SlurmLauncher
+    partition: null
+    qos: null
+    comment: null
+    constraint: null
+    exclude: null
+    gres: null
+    cpus_per_gpu: null
+    gpus_per_task: null
+    mem_per_gpu: null
+    mem_per_cpu: null
+    account: null
+    signal_delay_s: 120
+    max_num_timeout: 0
+    additional_parameters: {}
+    array_parallelism: 256
+    setup: null
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params:
+      NUM_NETS: 10, 20
+      NET_TRAINING_EPOCHS: 10, 30
+      TEMPERATURE: 0.3, 0.5, 0.7
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task: []
+  job:
+    name: llmatic
+    chdir: null
+    override_dirname: ''
+    id: ???
+    num: ???
+    config_name: config
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.1
+    version_base: '1.3'
+    cwd: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\conf
+      schema: file
+      provider: main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\outputs\2024-09-03\23-04-26
+    choices:
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: submitit_slurm
+      hydra/output: default
+  verbose: false

LLMatic-main/outputs/2024-09-03/23-04-26/.hydra/overrides.yaml

@@ -0,0 +1 @@
+[]

LLMatic-main/outputs/2024-09-06/05-03-40/.hydra/config.yaml

@@ -0,0 +1,19 @@
+RANDOM_INIT_NETS: 10
+INIT_NUM_NETS: 10
+ROLL_OUTS: 4
+NUM_NETS: 10
+START_FROM_CHECKPOINT: false
+RANDOM_NETWORKS: false
+MUTATION: codegen-6B-mono
+GENERATIONS: 200
+NET_TRAINING_EPOCHS: 50
+TEMPERATURE: 0.0
+DEVICE: cuda
+NUM_PROCESSES: 15
+RAY: true
+NUM_CPUS: 15
+NUM_GPUS: 1
+NUM_GPUS_TOTAL: 4
+DIM_MAP: 2
+N_NICHES: 100
+SAVE_DIR: ./

LLMatic-main/outputs/2024-09-06/05-03-40/.hydra/hydra.yaml

@@ -0,0 +1,182 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+    timeout_min: 2880
+    cpus_per_task: 1
+    gpus_per_node: 1
+    tasks_per_node: 1
+    mem_gb: 30
+    nodes: 1
+    name: ${hydra.job.name}
+    stderr_to_stdout: false
+    _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.SlurmLauncher
+    partition: null
+    qos: null
+    comment: null
+    constraint: null
+    exclude: null
+    gres: null
+    cpus_per_gpu: null
+    gpus_per_task: null
+    mem_per_gpu: null
+    mem_per_cpu: null
+    account: null
+    signal_delay_s: 120
+    max_num_timeout: 0
+    additional_parameters: {}
+    array_parallelism: 256
+    setup: null
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params:
+      NUM_NETS: 10, 20
+      NET_TRAINING_EPOCHS: 10, 30
+      TEMPERATURE: 0.3, 0.5, 0.7
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task: []
+  job:
+    name: llmatic
+    chdir: null
+    override_dirname: ''
+    id: ???
+    num: ???
+    config_name: config
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.1
+    version_base: '1.3'
+    cwd: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\conf
+      schema: file
+      provider: main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\outputs\2024-09-06\05-03-40
+    choices:
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: submitit_slurm
+      hydra/output: default
+  verbose: false

LLMatic-main/outputs/2024-09-06/05-03-40/.hydra/overrides.yaml

@@ -0,0 +1 @@
+[]

LLMatic-main/outputs/2024-09-06/06-51-07/.hydra/config.yaml

@@ -0,0 +1,19 @@
+RANDOM_INIT_NETS: 10
+INIT_NUM_NETS: 10
+ROLL_OUTS: 4
+NUM_NETS: 10
+START_FROM_CHECKPOINT: false
+RANDOM_NETWORKS: false
+MUTATION: codegen-6B-mono
+GENERATIONS: 200
+NET_TRAINING_EPOCHS: 50
+TEMPERATURE: 0.0
+DEVICE: cuda
+NUM_PROCESSES: 15
+RAY: true
+NUM_CPUS: 15
+NUM_GPUS: 1
+NUM_GPUS_TOTAL: 4
+DIM_MAP: 2
+N_NICHES: 100
+SAVE_DIR: ./

LLMatic-main/outputs/2024-09-06/06-51-07/.hydra/hydra.yaml

@@ -0,0 +1,182 @@
+hydra:
+  run:
+    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
+  sweep:
+    dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+    timeout_min: 2880
+    cpus_per_task: 1
+    gpus_per_node: 1
+    tasks_per_node: 1
+    mem_gb: 30
+    nodes: 1
+    name: ${hydra.job.name}
+    stderr_to_stdout: false
+    _target_: hydra_plugins.hydra_submitit_launcher.submitit_launcher.SlurmLauncher
+    partition: null
+    qos: null
+    comment: null
+    constraint: null
+    exclude: null
+    gres: null
+    cpus_per_gpu: null
+    gpus_per_task: null
+    mem_per_gpu: null
+    mem_per_cpu: null
+    account: null
+    signal_delay_s: 120
+    max_num_timeout: 0
+    additional_parameters: {}
+    array_parallelism: 256
+    setup: null
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params:
+      NUM_NETS: 10, 20
+      NET_TRAINING_EPOCHS: 10, 30
+      TEMPERATURE: 0.3, 0.5, 0.7
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][HYDRA] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    loggers:
+      logging_example:
+        level: DEBUG
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: simple
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.mode=RUN
+    task: []
+  job:
+    name: llmatic
+    chdir: null
+    override_dirname: ''
+    id: ???
+    num: ???
+    config_name: config
+    env_set: {}
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.1
+    version_base: '1.3'
+    cwd: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\conf
+      schema: file
+      provider: main
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: C:\Users\kjkj0\PycharmProjects\help-research-student\LLMatic-main\outputs\2024-09-06\06-51-07
+    choices:
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: default
+      hydra/hydra_logging: default
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: submitit_slurm
+      hydra/output: default
+  verbose: false