examples/seq2seq/__init__.py mutates sys.path (#7194)

examples/seq2seq/__init__.py

+import os
+import sys
+
+
+sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))

examples/seq2seq/distillation.py

@@ -10,37 +10,21 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
+from finetune import SummarizationModule, TranslationModule
+from finetune import main as ft_main
+from initialization_utils import copy_layers, init_student
 from lightning_base import generic_train
 from transformers import AutoModelForSeq2SeqLM, MBartTokenizer, T5Config, T5ForConditionalGeneration
 from transformers.modeling_bart import shift_tokens_right
-
-
-try:
-    from .finetune import SummarizationModule, TranslationModule
-    from .finetune import main as ft_main
-    from .initialization_utils import copy_layers, init_student
-    from .utils import (
-        any_requires_grad,
-        assert_all_frozen,
-        calculate_bleu,
-        freeze_params,
-        label_smoothed_nll_loss,
-        pickle_load,
-        use_task_specific_params,
-    )
-except ImportError:
-    from finetune import SummarizationModule, TranslationModule
-    from finetune import main as ft_main
-    from initialization_utils import copy_layers, init_student
-    from utils import (
-        any_requires_grad,
-        assert_all_frozen,
-        calculate_bleu,
-        freeze_params,
-        label_smoothed_nll_loss,
-        pickle_load,
-        use_task_specific_params,
-    )
+from utils import (
+    any_requires_grad,
+    assert_all_frozen,
+    calculate_bleu,
+    freeze_params,
+    label_smoothed_nll_loss,
+    pickle_load,
+    use_task_specific_params,
+)
 
 
 class BartSummarizationDistiller(SummarizationModule):

examples/seq2seq/finetune.py

@@ -12,50 +12,29 @@ import pytorch_lightning as pl
 import torch
 from torch.utils.data import DataLoader
 
+from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
 from lightning_base import BaseTransformer, add_generic_args, generic_train
 from transformers import MBartTokenizer, T5ForConditionalGeneration
 from transformers.modeling_bart import shift_tokens_right
+from utils import (
+    ROUGE_KEYS,
+    LegacySeq2SeqDataset,
+    Seq2SeqDataset,
+    assert_all_frozen,
+    calculate_bleu,
+    calculate_rouge,
+    flatten_list,
+    freeze_params,
+    get_git_info,
+    label_smoothed_nll_loss,
+    lmap,
+    pickle_save,
+    save_git_info,
+    save_json,
+    use_task_specific_params,
+)
 
 
-try:
-    from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
-    from .utils import (
-        ROUGE_KEYS,
-        LegacySeq2SeqDataset,
-        Seq2SeqDataset,
-        assert_all_frozen,
-        calculate_bleu,
-        calculate_rouge,
-        flatten_list,
-        freeze_params,
-        get_git_info,
-        label_smoothed_nll_loss,
-        lmap,
-        pickle_save,
-        save_git_info,
-        save_json,
-        use_task_specific_params,
-    )
-except ImportError:
-    from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
-    from utils import (
-        ROUGE_KEYS,
-        LegacySeq2SeqDataset,
-        Seq2SeqDataset,
-        assert_all_frozen,
-        calculate_bleu,
-        calculate_rouge,
-        flatten_list,
-        freeze_params,
-        get_git_info,
-        label_smoothed_nll_loss,
-        lmap,
-        pickle_save,
-        save_git_info,
-        save_json,
-        use_task_specific_params,
-    )
-
 logger = logging.getLogger(__name__)
 
 

examples/seq2seq/run_distributed_eval.py

@@ -11,35 +11,21 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from utils import (
+    Seq2SeqDataset,
+    calculate_bleu,
+    calculate_rouge,
+    lmap,
+    load_json,
+    parse_numeric_n_bool_cl_kwargs,
+    save_json,
+    use_task_specific_params,
+    write_txt_file,
+)
 
 
 logger = getLogger(__name__)
 
-try:
-    from .utils import (
-        Seq2SeqDataset,
-        calculate_bleu,
-        calculate_rouge,
-        lmap,
-        load_json,
-        parse_numeric_n_bool_cl_kwargs,
-        save_json,
-        use_task_specific_params,
-        write_txt_file,
-    )
-except ImportError:
-    from utils import (
-        Seq2SeqDataset,
-        calculate_bleu,
-        calculate_rouge,
-        lmap,
-        load_json,
-        parse_numeric_n_bool_cl_kwargs,
-        save_json,
-        use_task_specific_params,
-        write_txt_file,
-    )
-
 
 def eval_data_dir(
     data_dir,

examples/seq2seq/run_eval.py

@@ -11,14 +11,11 @@ import torch
 from tqdm import tqdm
 
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from utils import calculate_bleu, calculate_rouge, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
 
 
 logger = getLogger(__name__)
 
-try:
-    from .utils import calculate_bleu, calculate_rouge, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
-except ImportError:
-    from utils import calculate_bleu, calculate_rouge, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
 
 DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 

examples/seq2seq/run_eval_search.py

@@ -4,11 +4,7 @@ import operator
 import sys
 from collections import OrderedDict
 
-
-try:
-    from .run_eval import datetime_now, run_generate
-except ImportError:
-    from run_eval import datetime_now, run_generate
+from run_eval import datetime_now, run_generate
 
 
 # A table of supported tasks and the list of scores in the order of importance to be sorted by.

examples/seq2seq/test_bash_script.py

@@ -10,13 +10,12 @@ import pytorch_lightning as pl
 import timeout_decorator
 import torch
 
+from distillation import BartSummarizationDistiller, distill_main
+from finetune import SummarizationModule, main
+from test_seq2seq_examples import CUDA_AVAILABLE, MBART_TINY
 from transformers import BartForConditionalGeneration, MarianMTModel
 from transformers.testing_utils import slow
-
-from .distillation import BartSummarizationDistiller, distill_main
-from .finetune import SummarizationModule, main
-from .test_seq2seq_examples import CUDA_AVAILABLE, MBART_TINY
-from .utils import load_json
+from utils import load_json
 
 
 MODEL_NAME = MBART_TINY

examples/seq2seq/test_seq2seq_examples.py

@@ -12,16 +12,15 @@ import pytorch_lightning as pl
 import torch
 
 import lightning_base
+from convert_pl_checkpoint_to_hf import convert_pl_to_hf
+from distillation import distill_main, evaluate_checkpoint
+from finetune import SummarizationModule, main
+from run_eval import generate_summaries_or_translations, run_generate
+from run_eval_search import run_search
 from transformers import AutoConfig, AutoModelForSeq2SeqLM
 from transformers.hf_api import HfApi
 from transformers.testing_utils import CaptureStderr, CaptureStdout, require_multigpu, require_torch_and_cuda, slow
-
-from .convert_pl_checkpoint_to_hf import convert_pl_to_hf
-from .distillation import distill_main, evaluate_checkpoint
-from .finetune import SummarizationModule, main
-from .run_eval import generate_summaries_or_translations, run_generate
-from .run_eval_search import run_search
-from .utils import label_smoothed_nll_loss, lmap, load_json
+from utils import label_smoothed_nll_loss, lmap, load_json
 
 
 logging.basicConfig(level=logging.DEBUG)