reformat black

src/petals/__init__.py

@@ -20,7 +20,7 @@ from petals.utils.logging import initialize_logs as _initialize_logs
 __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 @@ class PTuneMixin:
                 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 @@ An interface for exchanging internal "BLOOM points" for higher priority compute
 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 @@ Bloom intermediate layer
 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 @@ Falcon intermediate layer
 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 @@ LLaMA intermediate layer
 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 @@ If necessary, one can rewrite this to implement a different behavior, such as:
  - 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 @@ A pytorch memory cache that can be allocated by ConnectionHandler (on cpu) and u
 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 @@ class _AutoDistributedBase:
             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