Deprecate unused OpenLlama architecture (#24922)

* Resolve typo in check_repo.py

* Specify encoding when opening modeling files

* Deprecate the OpenLlama architecture

* Add disclaimer pointing to Llama

I'm open to different wordings here

* Match the capitalisation of LLaMA

docs/source/en/model_doc/open-llama.md

@@ -16,6 +16,21 @@ rendered properly in your Markdown viewer.
 
 # Open-Llama
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, so we won't accept any new PRs changing its code.
+
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.31.0.
+You can do so by running the following command: `pip install -U transformers==4.31.0`.
+
+</Tip>
+
+<Tip warning={true}>
+
+This model differs from the [OpenLLaMA models](https://huggingface.co/models?search=openllama) on the Hugging Face Hub, which primarily use the [LLaMA](llama) architecture.
+
+</Tip>
+
 ## Overview
 
 The Open-Llama model was proposed in [Open-Llama project](https://github.com/s-JoL/Open-Llama) by community developer s-JoL.

src/transformers/__init__.py

@@ -278,6 +278,7 @@ _import_structure = {
         "MCTCTProcessor",
     ],
     "models.deprecated.mmbt": ["MMBTConfig"],
+    "models.deprecated.open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
     "models.deprecated.retribert": [
         "RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "RetriBertConfig",
@@ -445,7 +446,6 @@ _import_structure = {
         "NystromformerConfig",
     ],
     "models.oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig", "OneFormerProcessor"],
-    "models.open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
     "models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"],
     "models.opt": ["OPTConfig"],
     "models.owlvit": [
@@ -1536,6 +1536,9 @@ else:
         ]
     )
     _import_structure["models.deprecated.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"])
+    _import_structure["models.deprecated.open_llama"].extend(
+        ["OpenLlamaForCausalLM", "OpenLlamaForSequenceClassification", "OpenLlamaModel", "OpenLlamaPreTrainedModel"]
+    )
     _import_structure["models.deprecated.retribert"].extend(
         ["RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RetriBertModel", "RetriBertPreTrainedModel"]
     )
@@ -2300,9 +2303,6 @@ else:
             "OneFormerPreTrainedModel",
         ]
     )
-    _import_structure["models.open_llama"].extend(
-        ["OpenLlamaForCausalLM", "OpenLlamaForSequenceClassification", "OpenLlamaModel", "OpenLlamaPreTrainedModel"]
-    )
     _import_structure["models.openai"].extend(
         [
             "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -4239,6 +4239,7 @@ if TYPE_CHECKING:
         MCTCTProcessor,
     )
     from .models.deprecated.mmbt import MMBTConfig
+    from .models.deprecated.open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
     from .models.deprecated.retribert import (
         RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         RetriBertConfig,
@@ -4390,7 +4391,6 @@ if TYPE_CHECKING:
     from .models.nllb_moe import NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP, NllbMoeConfig
     from .models.nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
     from .models.oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig, OneFormerProcessor
-    from .models.open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
     from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer
     from .models.opt import OPTConfig
     from .models.owlvit import (
@@ -5334,6 +5334,12 @@ if TYPE_CHECKING:
             MCTCTPreTrainedModel,
         )
         from .models.deprecated.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
+        from .models.deprecated.open_llama import (
+            OpenLlamaForCausalLM,
+            OpenLlamaForSequenceClassification,
+            OpenLlamaModel,
+            OpenLlamaPreTrainedModel,
+        )
         from .models.deprecated.retribert import (
             RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             RetriBertModel,
@@ -5954,12 +5960,6 @@ if TYPE_CHECKING:
             OneFormerModel,
             OneFormerPreTrainedModel,
         )
-        from .models.open_llama import (
-            OpenLlamaForCausalLM,
-            OpenLlamaForSequenceClassification,
-            OpenLlamaModel,
-            OpenLlamaPreTrainedModel,
-        )
         from .models.openai import (
             OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
             OpenAIGPTDoubleHeadsModel,

src/transformers/models/__init__.py

@@ -145,7 +145,6 @@ from . import (
     nllb_moe,
     nystromformer,
     oneformer,
-    open_llama,
     openai,
     opt,
     owlvit,

src/transformers/models/auto/configuration_auto.py

@@ -652,6 +652,7 @@ DEPRECATED_MODELS = [
     "bort",
     "mctct",
     "mmbt",
+    "open_llama",
     "retribert",
     "tapex",
     "trajectory_transformer",

src/transformers/models/open_llama/__init__.py → src/transformers/models/deprecated/open_llama/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ....utils import (
     OptionalDependencyNotAvailable,
     _LazyModule,
     is_sentencepiece_available,

src/transformers/models/open_llama/configuration_open_llama.py → src/transformers/models/deprecated/open_llama/configuration_open_llama.py

@@ -19,8 +19,8 @@
 # limitations under the License.
 """ Open-Llama model configuration"""
 
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
+from ....configuration_utils import PretrainedConfig
+from ....utils import logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/open_llama/modeling_open_llama.py → src/transformers/models/deprecated/open_llama/modeling_open_llama.py

@@ -26,10 +26,10 @@ import torch.utils.checkpoint
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
-from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
-from ...modeling_utils import PreTrainedModel
-from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from ....activations import ACT2FN
+from ....modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from ....modeling_utils import PreTrainedModel
+from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
 from .configuration_open_llama import OpenLlamaConfig
 
 

src/transformers/utils/dummy_pt_objects.py

@@ -2396,6 +2396,34 @@ class ModalEmbeddings(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class OpenLlamaForCausalLM(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class OpenLlamaForSequenceClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class OpenLlamaModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class OpenLlamaPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
@@ -5461,34 +5489,6 @@ class OneFormerPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
-class OpenLlamaForCausalLM(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-class OpenLlamaForSequenceClassification(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-class OpenLlamaModel(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-class OpenLlamaPreTrainedModel(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
 OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/models/open_llama/test_modeling_open_llama.py

-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. 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.
-""" Testing suite for the PyTorch Open-Llama model. """
-
-
-import unittest
-
-from parameterized import parameterized
-
-from transformers import OpenLlamaConfig, is_torch_available, set_seed
-from transformers.testing_utils import require_torch, torch_device
-
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
-from ...test_pipeline_mixin import PipelineTesterMixin
-
-
-if is_torch_available():
-    import torch
-
-    from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
-
-
-class OpenLlamaModelTester:
-    def __init__(
-        self,
-        parent,
-        batch_size=13,
-        seq_length=7,
-        is_training=True,
-        use_input_mask=True,
-        use_token_type_ids=False,
-        use_labels=True,
-        vocab_size=99,
-        hidden_size=32,
-        num_hidden_layers=5,
-        num_attention_heads=4,
-        intermediate_size=37,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=16,
-        type_sequence_label_size=2,
-        initializer_range=0.02,
-        num_labels=3,
-        num_choices=4,
-        scope=None,
-    ):
-        self.parent = parent
-        self.batch_size = batch_size
-        self.seq_length = seq_length
-        self.is_training = is_training
-        self.use_input_mask = use_input_mask
-        self.use_token_type_ids = use_token_type_ids
-        self.use_labels = use_labels
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.intermediate_size = intermediate_size
-        self.hidden_act = hidden_act
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_size = type_vocab_size
-        self.type_sequence_label_size = type_sequence_label_size
-        self.initializer_range = initializer_range
-        self.num_labels = num_labels
-        self.num_choices = num_choices
-        self.scope = scope
-
-    def prepare_config_and_inputs(self):
-        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-
-        input_mask = None
-        if self.use_input_mask:
-            input_mask = random_attention_mask([self.batch_size, self.seq_length])
-
-        token_type_ids = None
-        if self.use_token_type_ids:
-            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
-
-        sequence_labels = None
-        token_labels = None
-        choice_labels = None
-        if self.use_labels:
-            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
-            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
-            choice_labels = ids_tensor([self.batch_size], self.num_choices)
-
-        config = self.get_config()
-
-        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
-    def get_config(self):
-        return OpenLlamaConfig(
-            vocab_size=self.vocab_size,
-            hidden_size=self.hidden_size,
-            num_hidden_layers=self.num_hidden_layers,
-            num_attention_heads=self.num_attention_heads,
-            intermediate_size=self.intermediate_size,
-            hidden_act=self.hidden_act,
-            hidden_dropout_prob=self.hidden_dropout_prob,
-            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
-            max_position_embeddings=self.max_position_embeddings,
-            type_vocab_size=self.type_vocab_size,
-            is_decoder=False,
-            initializer_range=self.initializer_range,
-            use_stable_embedding=False,
-        )
-
-    def create_and_check_model(
-        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-    ):
-        model = OpenLlamaModel(config=config)
-        model.to(torch_device)
-        model.eval()
-        result = model(input_ids, attention_mask=input_mask)
-        result = model(input_ids)
-        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
-
-    def create_and_check_model_as_decoder(
-        self,
-        config,
-        input_ids,
-        token_type_ids,
-        input_mask,
-        sequence_labels,
-        token_labels,
-        choice_labels,
-        encoder_hidden_states,
-        encoder_attention_mask,
-    ):
-        config.add_cross_attention = True
-        model = OpenLlamaModel(config)
-        model.to(torch_device)
-        model.eval()
-        result = model(
-            input_ids,
-            attention_mask=input_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-        )
-        result = model(
-            input_ids,
-            attention_mask=input_mask,
-            encoder_hidden_states=encoder_hidden_states,
-        )
-        result = model(input_ids, attention_mask=input_mask)
-        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
-
-    def create_and_check_for_causal_lm(
-        self,
-        config,
-        input_ids,
-        token_type_ids,
-        input_mask,
-        sequence_labels,
-        token_labels,
-        choice_labels,
-        encoder_hidden_states,
-        encoder_attention_mask,
-    ):
-        model = OpenLlamaForCausalLM(config=config)
-        model.to(torch_device)
-        model.eval()
-        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
-
-    def create_and_check_decoder_model_past_large_inputs(
-        self,
-        config,
-        input_ids,
-        token_type_ids,
-        input_mask,
-        sequence_labels,
-        token_labels,
-        choice_labels,
-        encoder_hidden_states,
-        encoder_attention_mask,
-    ):
-        config.is_decoder = True
-        config.add_cross_attention = True
-        model = OpenLlamaForCausalLM(config=config)
-        model.to(torch_device)
-        model.eval()
-
-        # first forward pass
-        outputs = model(
-            input_ids,
-            attention_mask=input_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            use_cache=True,
-        )
-        past_key_values = outputs.past_key_values
-
-        # create hypothetical multiple next token and extent to next_input_ids
-        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
-        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
-
-        # append to next input_ids and
-        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
-        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
-
-        output_from_no_past = model(
-            next_input_ids,
-            attention_mask=next_attention_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            output_hidden_states=True,
-        )["hidden_states"][0]
-        output_from_past = model(
-            next_tokens,
-            attention_mask=next_attention_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            output_hidden_states=True,
-        )["hidden_states"][0]
-
-        # select random slice
-        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
-        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
-        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
-
-        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
-
-        # test that outputs are equal for slice
-        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
-
-    def prepare_config_and_inputs_for_common(self):
-        config_and_inputs = self.prepare_config_and_inputs()
-        (
-            config,
-            input_ids,
-            token_type_ids,
-            input_mask,
-            sequence_labels,
-            token_labels,
-            choice_labels,
-        ) = config_and_inputs
-        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
-        return config, inputs_dict
-
-
-@require_torch
-class OpenLlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (
-        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
-    )
-    all_generative_model_classes = (OpenLlamaForCausalLM,) if is_torch_available() else ()
-    pipeline_model_mapping = (
-        {
-            "feature-extraction": OpenLlamaModel,
-            "text-classification": OpenLlamaForSequenceClassification,
-            "text-generation": OpenLlamaForCausalLM,
-            "zero-shot": OpenLlamaForSequenceClassification,
-        }
-        if is_torch_available()
-        else {}
-    )
-    test_headmasking = False
-    test_pruning = False
-
-    def setUp(self):
-        self.model_tester = OpenLlamaModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=OpenLlamaConfig, hidden_size=37)
-
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
-    def test_model_various_embeddings(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        for type in ["absolute", "relative_key", "relative_key_query"]:
-            config_and_inputs[0].position_embedding_type = type
-            self.model_tester.create_and_check_model(*config_and_inputs)
-
-    def test_open_llama_sequence_classification_model(self):
-        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        config.num_labels = 3
-        input_ids = input_dict["input_ids"]
-        attention_mask = input_ids.ne(1).to(torch_device)
-        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
-        model = OpenLlamaForSequenceClassification(config)
-        model.to(torch_device)
-        model.eval()
-        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
-        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
-
-    def test_open_llama_sequence_classification_model_for_single_label(self):
-        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        config.num_labels = 3
-        config.problem_type = "single_label_classification"
-        input_ids = input_dict["input_ids"]
-        attention_mask = input_ids.ne(1).to(torch_device)
-        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
-        model = OpenLlamaForSequenceClassification(config)
-        model.to(torch_device)
-        model.eval()
-        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
-        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
-
-    def test_open_llama_sequence_classification_model_for_multi_label(self):
-        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        config.num_labels = 3
-        config.problem_type = "multi_label_classification"
-        input_ids = input_dict["input_ids"]
-        attention_mask = input_ids.ne(1).to(torch_device)
-        sequence_labels = ids_tensor(
-            [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
-        ).to(torch.float)
-        model = OpenLlamaForSequenceClassification(config)
-        model.to(torch_device)
-        model.eval()
-        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
-        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
-
-    @unittest.skip("Open-Llama buffers include complex numbers, which breaks this test")
-    def test_save_load_fast_init_from_base(self):
-        pass
-
-    @parameterized.expand([("linear",), ("dynamic",)])
-    def test_model_rope_scaling(self, scaling_type):
-        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
-        short_input = ids_tensor([1, 10], config.vocab_size)
-        long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
-
-        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
-        original_model = OpenLlamaModel(config)
-        original_model.to(torch_device)
-        original_model.eval()
-        original_short_output = original_model(short_input).last_hidden_state
-        original_long_output = original_model(long_input).last_hidden_state
-
-        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
-        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
-        scaled_model = OpenLlamaModel(config)
-        scaled_model.to(torch_device)
-        scaled_model.eval()
-        scaled_short_output = scaled_model(short_input).last_hidden_state
-        scaled_long_output = scaled_model(long_input).last_hidden_state
-
-        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
-        # maximum sequence length, so the outputs for the short input should match.
-        if scaling_type == "dynamic":
-            self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
-        else:
-            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
-
-        # The output should be different for long inputs
-        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

utils/check_config_attributes.py

@@ -238,7 +238,7 @@ def check_config_attributes_being_used(config_class):
     modeling_sources = []
     for path in modeling_paths:
         if os.path.isfile(path):
-            with open(path) as fp:
+            with open(path, encoding="utf8") as fp:
                 modeling_sources.append(fp.read())
 
     unused_attributes = []

utils/check_repo.py

@@ -1093,7 +1093,7 @@ def check_deprecated_constant_is_up_to_date():
     if len(missing_models) != 0:
         missing_models = ", ".join(missing_models)
         message.append(
-            "The following models are in the deprecated folder, make sur to add them to `DEPRECATED_MODELS` in "
+            "The following models are in the deprecated folder, make sure to add them to `DEPRECATED_MODELS` in "
             f"`models/auto/configuration_auto.py`: {missing_models}."
         )