reformat black

src/petals/__init__.py

@@ -20,7 +20,7 @@
 __version__ = "2.3.0.dev2"
 
 
-#if not os.getenv("PETALS_IGNORE_DEPENDENCY_VERSION"):
+# if not os.getenv("PETALS_IGNORE_DEPENDENCY_VERSION"):
 #    assert (
 #        version.parse("4.38.2") <= version.parse(transformers.__version__) < version.parse("4.39.0")
 #    ), "Please install a proper transformers version: pip install transformers>=4.37.1,<4.39.0"

src/petals/client/ptune.py

@@ -51,7 +51,7 @@ def get_prompt(self, batch_size):
                 batch_size,
                 self.pre_seq_len,
                 self.config.num_hidden_layers,
-                self.config.hidden_size
+                self.config.hidden_size,
                 # TODO: should be num_hidden_layers - 1
             )
             intermediate_prompts = intermediate_prompts.permute([2, 0, 1, 3])

src/petals/client/remote_forward_backward.py

@@ -1,6 +1,7 @@
 """
 Utility functions that call RPC forward or backward on a single remote server
 """
+
 import asyncio
 from typing import Iterable, List, Optional, Sequence, Tuple
 

src/petals/client/routing/spending_policy.py

@@ -3,6 +3,7 @@
 The intent is to let Petals participants earn points by helping others while idle (e.g. at night), then use these
  points to run their own compute experiments faster. See Section 4 of https://arxiv.org/abs/2209.01188 for discussion.
 """
+
 from abc import ABC, abstractmethod
 
 

src/petals/client/sequential_autograd.py

@@ -1,6 +1,7 @@
 """
 A PyTorch autograd function that runs forward/backward on a sequence of remote servers in a fault-tolerant manner
 """
+
 import asyncio
 import itertools
 from collections import deque

src/petals/models/bloom/block.py

@@ -3,6 +3,7 @@
 Based on https://github.com/huggingface/transformers/commit/ca2a55e9dfb245527b5e1c954fec6ffbb7aef07b
 See commit history for authorship.
 """
+
 from typing import Optional, Tuple
 
 import torch

src/petals/models/falcon/block.py

@@ -3,6 +3,7 @@
 Based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/falcon/modeling_falcon.py
 See commit history for authorship.
 """
+
 import math
 from functools import partial
 from typing import Optional, Tuple

src/petals/models/llama/block.py

@@ -3,6 +3,7 @@
 Based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 See commit history for authorship.
 """
+
 import math
 from typing import Optional, Tuple
 

src/petals/server/block_functions.py

@@ -1,6 +1,7 @@
 """
 This module implements server-side computations on served blocks: forward, backward and inference; used by handler
 """
+
 from __future__ import annotations
 
 from typing import Any, AsyncIterator, Dict, Optional, Sequence, Tuple, Union

src/petals/server/from_pretrained.py

@@ -6,6 +6,7 @@
  - fetch the weights over IPoAC, using a fleet of trained pigeons ( http://www.faqs.org/rfcs/rfc1149.html )
 
 """
+
 import json
 import time
 from contextlib import suppress

src/petals/server/memory_cache.py

@@ -4,6 +4,7 @@
 For now, the only purpose of this code is to ensure that allocated memory will be deleted properly.
 
 """
+
 import asyncio
 import contextlib
 import ctypes

src/petals/utils/auto_config.py

@@ -41,7 +41,7 @@ def from_pretrained(cls, model_name_or_path: Union[str, os.PathLike, None], *arg
             kwargs["use_auth_token"] = True
 
         kwargs["trust_remote_code"] = True
-#trust_remote_code=True
+        # trust_remote_code=True
 
         config = AutoConfig.from_pretrained(model_name_or_path, *args, **kwargs)
         if config.model_type not in _CLASS_MAPPING:

src/petals/utils/convert_block.py

@@ -1,6 +1,7 @@
 """
 Tools for converting transformer blocks, applying quantization and/or tensor parallelism
 """
+
 import re
 from enum import Enum
 from typing import Optional, Sequence

src/petals/utils/dht.py

@@ -1,6 +1,7 @@
 """
 Utilities for declaring and retrieving active model layers using a shared DHT.
 """
+
 from __future__ import annotations
 
 import math