Merge pull request #63 from tedhtchang/Enable-pylint

Enable pylint in the github workflow

Author: anhuong

.github/workflows/format.yml

@@ -35,4 +35,6 @@ jobs:
           python -m pip install -r setup_requirements.txt
       - name: Check Formatting
         run: tox -e fmt
+      - name: Run pylint
+        run: tox -e lint
 

.pylintrc

@@ -443,7 +443,9 @@ disable=raw-checker-failed,
         attribute-defined-outside-init,
         abstract-method,
         pointless-statement,
-        wrong-import-order
+        wrong-import-order,
+        duplicate-code,
+        unbalanced-tuple-unpacking
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

build/launch_training.py

@@ -48,7 +48,7 @@ def get_highest_checkpoint(dir_path):
     for curr_dir in os.listdir(dir_path):
         if curr_dir.startswith("checkpoint"):
             if checkpoint_dir:
-                curr_dir_num = int(checkpoint_dir.split("-")[-1])
+                curr_dir_num = int(checkpoint_dir.rsplit("-", maxsplit=1)[-1])
                 new_dir_num = int(curr_dir.split("-")[-1])
                 if new_dir_num > curr_dir_num:
                     checkpoint_dir = curr_dir
@@ -87,13 +87,13 @@ def main():
         ) = parser.parse_json_file(json_path, allow_extra_keys=True)
 
         contents = ""
-        with open(json_path, "r") as f:
+        with open(json_path, "r", encoding="utf-8") as f:
             contents = json.load(f)
         peft_method_parsed = contents.get("peft_method")
-        logging.debug(f"Input params parsed: {contents}")
+        logging.debug("Input params parsed: %s", contents)
     elif json_env_var:
         job_config_dict = txt_to_obj(json_env_var)
-        logging.debug(f"Input params parsed: {job_config_dict}")
+        logging.debug("Input params parsed: %s", job_config_dict)
 
         (
             model_args,
@@ -106,7 +106,8 @@ def main():
         peft_method_parsed = job_config_dict.get("peft_method")
     else:
         raise ValueError(
-            "Must set environment variable 'SFT_TRAINER_CONFIG_JSON_PATH' or 'SFT_TRAINER_CONFIG_JSON_ENV_VAR'."
+            "Must set environment variable 'SFT_TRAINER_CONFIG_JSON_PATH' \
+        or 'SFT_TRAINER_CONFIG_JSON_ENV_VAR'."
         )
 
     tune_config = None
@@ -118,7 +119,12 @@ def main():
         tune_config = prompt_tuning_config
 
     logging.debug(
-        f"Parameters used to launch training: model_args {model_args}, data_args {data_args}, training_args {training_args}, tune_config {tune_config}"
+        "Parameters used to launch training: \
+    model_args %s, data_args %s, training_args %s, tune_config %s",
+        model_args,
+        data_args,
+        training_args,
+        tune_config,
     )
 
     original_output_dir = training_args.output_dir
@@ -138,7 +144,9 @@ def main():
             )
 
             logging.info(
-                f"Merging lora tuned checkpoint {lora_checkpoint_dir} with base model into output path: {export_path}"
+                "Merging lora tuned checkpoint %s with base model into output path: %s",
+                lora_checkpoint_dir,
+                export_path,
             )
 
             create_merged_model(
@@ -151,7 +159,9 @@ def main():
             # copy last checkpoint into mounted output dir
             pt_checkpoint_dir = get_highest_checkpoint(training_args.output_dir)
             logging.info(
-                f"Copying last checkpoint {pt_checkpoint_dir} into output dir {original_output_dir}"
+                "Copying last checkpoint %s into output dir %s",
+                pt_checkpoint_dir,
+                original_output_dir,
             )
             shutil.copytree(
                 os.path.join(training_args.output_dir, pt_checkpoint_dir),

scripts/run_inference.py

@@ -91,11 +91,11 @@ def _apply_config_changes(self, overrides: dict) -> dict:
         # If we have no overrides, this context manager is a noop; no need to do anything
         if not overrides:
             return {}
-        with open(self.config_path, "r") as config_file:
+        with open(self.config_path, "r", encoding="utf-8") as config_file:
             adapter_config = json.load(config_file)
         overridden_values = self._get_old_config_values(adapter_config, overrides)
         adapter_config = {**adapter_config, **overrides}
-        with open(self.config_path, "w") as config_file:
+        with open(self.config_path, "w", encoding="utf-8") as config_file:
             json.dump(adapter_config, config_file, indent=4)
         return overridden_values
 
@@ -227,7 +227,8 @@ def main():
     )
     parser.add_argument(
         "--base_model_name_or_path",
-        help="Override for base model to be used for non-merged models [default: value in model adapter_config.json]",
+        help="Override for base model to be used for non-merged models \
+            [default: value in model adapter_config.json]",
         default=None,
     )
     parser.add_argument(
@@ -257,7 +258,7 @@ def main():
     if args.text:
         texts = [args.text]
     else:
-        with open(args.text_file, "r") as text_file:
+        with open(args.text_file, "r", encoding="utf-8") as text_file:
             texts = [line.strip() for line in text_file.readlines()]
 
     # TODO: we should add batch inference support
@@ -270,7 +271,7 @@ def main():
     ]
 
     # Export the results to a file
-    with open(args.out_file, "w") as out_file:
+    with open(args.out_file, "w", encoding="utf-8") as out_file:
         json.dump(results, out_file, sort_keys=True, indent=4)
 
     print(f"Exported results to: {args.out_file}")

tox.ini

@@ -17,5 +17,6 @@ allowlist_externals = ./scripts/fmt.sh
 [testenv:lint]
 description = lint with pylint
 deps = pylint>=2.16.2,<=3.1.0
-commands = pylint tuning scripts/*.py
+        -r requirements.txt
+commands = pylint tuning scripts/*.py build/*.py
 allowlist_externals = pylint

tuning/config/configs.py

@@ -14,7 +14,7 @@
 
 # Standard
 from dataclasses import dataclass, field
-from typing import Dict, Optional, Union
+from typing import Optional, Union
 
 # Third Party
 import torch
@@ -64,7 +64,8 @@ class TrainingArguments(transformers.TrainingArguments):
     model_max_length: int = field(
         default=DEFAULT_CONTEXT_LENGTH,
         metadata={
-            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
+            "help": "Maximum sequence length. Sequences will be right padded \
+            (and possibly truncated)."
         },
     )
     packing: bool = field(

tuning/config/peft_config.py

@@ -24,8 +24,10 @@ class LoraConfig:
     target_modules: List[str] = field(
         default_factory=lambda: ["q_proj", "v_proj"],
         metadata={
-            "help": "The names of the modules to apply LORA to. LORA selects modules which either completely match or "
-            'end with one of the strings. If the value is ["all-linear"], then LORA selects all linear and Conv1D '
+            "help": "The names of the modules to apply LORA to. LORA selects modules which either \
+            completely match or "
+            'end with one of the strings. If the value is ["all-linear"], \
+            then LORA selects all linear and Conv1D '
             "modules except for the output layer."
         },
     )

tuning/data/tokenizer_data_utils.py

@@ -13,19 +13,11 @@
 # limitations under the License.
 
 # Standard
-from typing import Dict, Sequence
-import copy
-import json
-import logging
+from typing import Dict
 
 # Third Party
-from torch.utils.data import Dataset
-import torch
 import transformers
 
-# Local
-from tuning.config import configs
-
 
 def tokenizer_and_embedding_resize(
     special_tokens_dict: Dict,

tuning/sft_trainer.py

@@ -17,6 +17,7 @@
 from typing import Optional, Union
 import json
 import os
+import sys
 
 # Third Party
 from peft.utils.other import fsdp_auto_wrap_policy
@@ -94,15 +95,15 @@ def _track_loss(self, loss_key, log_file, logs, state):
             return
 
         # append the current log to the jsonl file
-        with open(log_file, "a") as f:
+        with open(log_file, "a", encoding="utf-8") as f:
             f.write(f"{json.dumps(log_obj, sort_keys=True)}\n")
 
 
 def train(
     model_args: configs.ModelArguments,
     data_args: configs.DataArguments,
     train_args: configs.TrainingArguments,
-    peft_config: Optional[
+    peft_config: Optional[  # pylint: disable=redefined-outer-name
         Union[peft_config.LoraConfig, peft_config.PromptTuningConfig]
     ] = None,
 ):
@@ -154,9 +155,7 @@ def train(
     )
 
     # TODO: understand if we need to hardcode these here or just use defaults in model
-    if isinstance(tokenizer, LlamaTokenizer) or isinstance(
-        tokenizer, LlamaTokenizerFast
-    ):
+    if isinstance(tokenizer, (LlamaTokenizer, LlamaTokenizerFast)):
         tokenizer.add_special_tokens(
             {
                 "bos_token": "<s>",
@@ -165,33 +164,36 @@ def train(
                 "pad_token": "<pad>",
             }
         )
-    elif isinstance(tokenizer, GPTNeoXTokenizerFast) or isinstance(
-        tokenizer, GPT2Tokenizer
-    ):
+    elif isinstance(tokenizer, (GPT2Tokenizer, GPTNeoXTokenizerFast)):
         tokenizer.add_special_tokens(
             {
                 "pad_token": "<pad>",
             }
         )
 
-    """TODO: near term - how response template ids are parsed out needs to be cleaned.
-       The [2:] here applies if response template has \n prefix, it is needed to strip \n, otherwise template is not found.
-       We will create issue to clean this out after we discuss data formats and collators we will support
-    """
+    # TODO: near term - how response template ids are parsed out needs to be cleaned.
+    # The [2:] here applies if response template has \n prefix, it is needed to strip \n,
+    # otherwise template is not found. We will create issue to clean this out after we discuss
+    # data formats and collators we will support.
     response_template_ids = tokenizer.encode(
         data_args.response_template, add_special_tokens=False
     )[2:]
-    # TODO: This is actually max_seq_length and not model_max_length. we should not override model_max_length
-    # as in current main. We need to change name of this parameter we expose to users.
+    # TODO: This is actually max_seq_length and not model_max_length. we should not override
+    # model_max_length as in current main. We need to change name of this parameter we expose
+    # to users.
     model_max_length = min(train_args.model_max_length, tokenizer.model_max_length)
-    logger.info(f"Model max length {model_max_length}")
+    logger.info("Model max length %s, model_max_length")
     if train_args.model_max_length > tokenizer.model_max_length:
         logger.warning(
-            f"model_max_length {train_args.model_max_length} exceeds tokenizer.model_max_length {tokenizer.model_max_length}, using tokenizer.model_max_length {tokenizer.model_max_length}"
+            "model_max_length %s exceeds tokenizer.model_max_length \
+            %s, using tokenizer.model_max_length %s",
+            train_args.model_max_length,
+            tokenizer.model_max_length,
+            tokenizer.model_max_length,
         )
 
     # TODO: we need to change this, perhaps follow what open instruct does?
-    special_tokens_dict = dict()
+    special_tokens_dict = {}
     if tokenizer.pad_token is None:
         logger.warning("PAD token set to default, missing in tokenizer")
         special_tokens_dict["pad_token"] = configs.DEFAULT_PAD_TOKEN
@@ -219,19 +221,21 @@ def train(
     if data_args.validation_data_path:
         data_files["validation"] = data_args.validation_data_path
 
-    format_dataset = lambda example: {
+    format_dataset = lambda example: {  # pylint: disable=unnecessary-lambda-assignment
         f"{data_args.dataset_text_field}": example[f"{data_args.dataset_text_field}"]
         + tokenizer.eos_token
     }
 
     json_dataset = datasets.load_dataset("json", data_files=data_files)
     formatted_train_dataset = json_dataset["train"].map(format_dataset)
-    logger.info(f"Training dataset length is {len(formatted_train_dataset)}")
+    logger.info("Training dataset length is %s", len(formatted_train_dataset))
 
     formatted_validation_dataset = None
     if data_args.validation_data_path:
         formatted_validation_dataset = json_dataset["validation"].map(format_dataset)
-        logger.info(f"Validation dataset length is {len(formatted_validation_dataset)}")
+        logger.info(
+            "Validation dataset length is %s", len(formatted_validation_dataset)
+        )
 
     aim_callback = get_aimstack_callback()
     file_logger_callback = FileLoggingCallback(logger)
@@ -248,13 +252,13 @@ def train(
             logger.error(
                 "Error, response template is None, needs to be set for training"
             )
-            exit(-1)
+            sys.exit(-1)
 
         if data_args.dataset_text_field is None:
             logger.error(
                 "Error, dataset_text_field is None, needs to be set for training"
             )
-            exit(-1)
+            sys.exit(-1)
 
         data_collator = DataCollatorForCompletionOnlyLM(
             response_template_ids,
@@ -284,7 +288,7 @@ def train(
     trainer.train()
 
 
-def main(**kwargs):
+def main(**kwargs):  # pylint: disable=unused-argument
     parser = transformers.HfArgumentParser(
         dataclass_types=(
             configs.ModelArguments,

tuning/utils/merge_model_utils.py

@@ -14,7 +14,6 @@
 
 # Standard
 from typing import Union
-import argparse
 import json
 import os
 
@@ -41,7 +40,7 @@ def create_merged_model(
     References:
     - https://github.com/huggingface/peft/issues/1040
     - https://github.com/huggingface/peft/issues/280#issuecomment-1500805831
-    - https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.add_weighted_adapter
+    - https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.add_weighted_adapter # pylint: disable=line-too-long
 
     Args:
         checkpoint_model: Union[str, list[str]]
@@ -96,7 +95,7 @@ def fetch_base_model_from_checkpoint(checkpoint_model: str) -> str:
     if not os.path.isfile(adapter_config):
         raise FileNotFoundError("Unable to locate adapter config to infer base model!")
 
-    with open(adapter_config, "r") as cfg:
+    with open(adapter_config, "r", encoding="utf-8") as cfg:
         adapter_dict = json.load(cfg)
     if "base_model_name_or_path" not in adapter_dict:
         raise KeyError(