Fix subfolder && add subfolder tests

paddlenlp/transformers/auto/configuration.py

@@ -159,7 +159,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: str, *model_args, **kwar
             config = AutoConfig.from_pretrained("bert-base-uncased")
             config.save_pretrained('./bert-base-uncased')
         """
-        subfolder = kwargs.pop("subfolder", "")
+        subfolder = kwargs.get("subfolder", "")
         if subfolder is None:
             subfolder = ""
         from_aistudio = kwargs.pop("from_aistudio", False)

paddlenlp/transformers/auto/tokenizer.py

@@ -281,7 +281,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                 all_tokenizer_names.append(name)
         # From local dir path
         if os.path.isdir(pretrained_model_name_or_path):
-            config_file = os.path.join(pretrained_model_name_or_path, cls.tokenizer_config_file)
+            config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.tokenizer_config_file)
             if os.path.exists(config_file):
                 tokenizer_class = cls._get_tokenizer_class_from_config(
                     pretrained_model_name_or_path, config_file, use_fast

paddlenlp/transformers/conversion_utils.py

@@ -1012,7 +1012,6 @@ def convert(cls, weight_file: str, config: PretrainedConfig, cache_dir: str) ->
         """
         # FIXME(wj-Mcat): add compatibility with downstream models
         name_mappings = cls._get_name_mappings(config)
-
         if weight_file.endswith(".index.json"):
             if ".safetensors." in weight_file:
                 files = [file for file in os.listdir(os.path.dirname(weight_file)) if file.startswith("model-")]

paddlenlp/transformers/tokenizer_utils_base.py

@@ -1559,7 +1559,9 @@ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
         for k, v in resolved_vocab_files.items():
             if v is not None and os.path.isfile(v):
                 tokenizer_config_file_dir_list.add(os.path.dirname(v))
-        assert len(tokenizer_config_file_dir_list) == 1, "All tokenizer files should be in the same directory."
+        tokenizer_config_file_dir_list = list(tokenizer_config_file_dir_list)
+        # TODO: check this
+        assert len(tokenizer_config_file_dir_list) > 0, "All tokenizer files should be in the same directory."
         # Prepare tokenizer initialization kwargs
         # Did we saved some inputs and kwargs to reload ?
         has_tokenizer_file = resolved_vocab_files.get("tokenizer_file", None) is not None

paddlenlp/transformers/utils.py

@@ -612,6 +612,7 @@ def get_checkpoint_shard_files(
     cache_dir=None,
     subfolder="",
     from_aistudio=False,
+    from_hf_hub=False,
 ):
     """
     For a given model:
@@ -666,6 +667,13 @@ def get_checkpoint_shard_files(
                     subfolder=subfolder,
                     cache_dir=cache_dir,
                 )
+            elif from_hf_hub:
+                cached_filename = hf_hub_download(
+                    repo_id=pretrained_model_name_or_path,
+                    filename=shard_filename,
+                    subfolder=subfolder,
+                    cache_dir=cache_dir,
+                )
             else:
                 cached_filename = paddlenlp_hub_download(
                     pretrained_model_name_or_path,

tests/transformers/load_subfolder/test_config.py

@@ -0,0 +1,87 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from paddlenlp.transformers import AutoConfig, BertConfig, CLIPConfig, T5Config
+from paddlenlp.utils.log import logger
+
+
+class ConfigLoadTester(unittest.TestCase):
+    def test_bert_config_load(self):
+        logger.info("Download Bert Config from PaddleNLP BOS")
+        bert_config = BertConfig.from_pretrained("bert-base-uncased", from_hf_hub=False)
+        bert_config = AutoConfig.from_pretrained("bert-base-uncased", from_hf_hub=False)
+
+        logger.info("Download config from local")
+        bert_config.save_pretrained("./paddlenlp-test-config/bert-base-uncased")
+        bert_config = BertConfig.from_pretrained("./paddlenlp-test-config/bert-base-uncased")
+        bert_config = AutoConfig.from_pretrained("./paddlenlp-test-config/bert-base-uncased")
+        logger.info("Download config from local with subfolder")
+        bert_config = BertConfig.from_pretrained("./paddlenlp-test-config", subfolder="bert-base-uncased")
+        bert_config = AutoConfig.from_pretrained("./paddlenlp-test-config", subfolder="bert-base-uncased")
+
+        logger.info("Download Bert Config from PaddleNLP BOS with subfolder")
+        bert_config = BertConfig.from_pretrained(
+            "baicai/paddlenlp-test-model", subfolder="bert-base-uncased", from_hf_hub=False
+        )
+        bert_config = AutoConfig.from_pretrained(
+            "baicai/paddlenlp-test-model", subfolder="bert-base-uncased", from_hf_hub=False
+        )
+
+        logger.info("Download Bert Config from aistudio")
+        bert_config = BertConfig.from_pretrained("aistudio/bert-base-uncased", from_aistudio=True)
+        bert_config = AutoConfig.from_pretrained("aistudio/bert-base-uncased", from_aistudio=True)
+
+    def test_clip_config_load(self):
+        logger.info("Download CLIP Config from PaddleNLP BOS")
+        clip_config = CLIPConfig.from_pretrained("openai/clip-vit-base-patch32", from_hf_hub=False)
+        clip_config = AutoConfig.from_pretrained("openai/clip-vit-base-patch32", from_hf_hub=False)
+
+        logger.info("Download CLIP Config from local")
+        clip_config.save_pretrained("./paddlenlp-test-config/clip-vit-base-patch32")
+        clip_config = CLIPConfig.from_pretrained("./paddlenlp-test-config/clip-vit-base-patch32")
+        clip_config = AutoConfig.from_pretrained("./paddlenlp-test-config/clip-vit-base-patch32")
+        logger.info("Download CLIP Config from local with subfolder")
+        clip_config = CLIPConfig.from_pretrained("./paddlenlp-test-config", subfolder="clip-vit-base-patch32")
+        clip_config = AutoConfig.from_pretrained("./paddlenlp-test-config", subfolder="clip-vit-base-patch32")
+
+        logger.info("Download CLIP Config from PaddleNLP BOS with subfolder")
+        clip_config = CLIPConfig.from_pretrained(
+            "baicai/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_hf_hub=False
+        )
+        clip_config = AutoConfig.from_pretrained(
+            "baicai/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_hf_hub=False
+        )
+
+        logger.info("Download CLIP Config from aistudio")
+        clip_config = CLIPConfig.from_pretrained("aistudio/clip-vit-base-patch32", from_aistudio=True)
+        clip_config = AutoConfig.from_pretrained("aistudio/clip-vit-base-patch32", from_aistudio=True)
+
+    def test_t5_config_load(self):
+        logger.info("Download T5 Config from PaddleNLP BOS")
+        t5_config = T5Config.from_pretrained("t5-small", from_hf_hub=False)
+        t5_config = AutoConfig.from_pretrained("t5-small", from_hf_hub=False)
+
+        logger.info("Download T5 Config from PaddleNLP BOS with subfolder")
+        t5_config = T5Config.from_pretrained("baicai/paddlenlp-test-model", subfolder="t5-small", from_hf_hub=False)
+        t5_config = AutoConfig.from_pretrained("baicai/paddlenlp-test-model", subfolder="t5-small", from_hf_hub=False)
+        logger.info("Download T5 Config from local")
+        t5_config.save_pretrained("./paddlenlp-test-config/t5-small")
+        t5_config = T5Config.from_pretrained("./paddlenlp-test-config/t5-small")
+        t5_config = AutoConfig.from_pretrained("./paddlenlp-test-config/t5-small")
+
+        logger.info("Download T5 Config from aistudio")
+        t5_config = T5Config.from_pretrained("aistudio/t5-small", from_aistudio=True)
+        t5_config = AutoConfig.from_pretrained("aistudio/t5-small", from_aistudio=True)

tests/transformers/load_subfolder/test_image_processor.py

@@ -0,0 +1,57 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from paddlenlp.transformers import AutoImageProcessor, CLIPImageProcessor
+from paddlenlp.utils.log import logger
+
+
+class ImageProcessorLoadTester(unittest.TestCase):
+    def test_clip_load(self):
+        logger.info("Download model from PaddleNLP BOS")
+        clip_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32", from_hf_hub=False)
+        clip_processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32", from_hf_hub=False)
+
+        logger.info("Download model from local")
+        clip_processor.save_pretrained("./paddlenlp-test-model/clip-vit-base-patch32")
+        clip_processor = CLIPImageProcessor.from_pretrained("./paddlenlp-test-model/clip-vit-base-patch32")
+        clip_processor = AutoImageProcessor.from_pretrained("./paddlenlp-test-model/clip-vit-base-patch32")
+        logger.info("Download model from PaddleNLP BOS with subfolder")
+        clip_processor = CLIPImageProcessor.from_pretrained(
+            "./paddlenlp-test-model/", subfolder="clip-vit-base-patch32"
+        )
+        clip_processor = AutoImageProcessor.from_pretrained(
+            "./paddlenlp-test-model/", subfolder="clip-vit-base-patch32"
+        )
+
+        logger.info("Download model from PaddleNLP BOS with subfolder")
+        clip_processor = CLIPImageProcessor.from_pretrained(
+            "baicai/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_hf_hub=False
+        )
+        clip_processor = AutoImageProcessor.from_pretrained(
+            "baicai/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_hf_hub=False
+        )
+
+        logger.info("Download model from aistudio")
+        clip_processor = CLIPImageProcessor.from_pretrained("aistudio/clip-vit-base-patch32", from_aistudio=True)
+        clip_processor = AutoImageProcessor.from_pretrained("aistudio/clip-vit-base-patch32", from_aistudio=True)
+
+        logger.info("Download model from aistudio with subfolder")
+        clip_processor = CLIPImageProcessor.from_pretrained(
+            "aistudio/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_aistudio=True
+        )
+        clip_processor = AutoImageProcessor.from_pretrained(
+            "aistudio/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_aistudio=True
+        )