Update tests and name skips a bit

docs/source/en/index.mdx

@@ -325,7 +325,7 @@ Flax), PyTorch, and/or TensorFlow.
 |      Funnel Transformer       |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |              GIT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             GLPN              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            GPT Neo            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|            GPT Neo            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           GPT NeoX            |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
 |       GPT NeoX Japanese       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             GPT-J             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |

src/transformers/__init__.py

@@ -3181,6 +3181,17 @@ else:
             "TFGPT2PreTrainedModel",
         ]
     )
+    _import_structure["models.gpt_neo"].extend(
+        [
+            "TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFGPTNeoForCausalLM",
+            "TFGPTNeoForQuestionAnswering",
+            "TFGPTNeoForSequenceClassification",
+            "TFGPTNeoForTokenClassification",
+            "TFGPTNeoModel",
+            "TFGPTNeoPreTrainedModel",
+        ]
+    )
     _import_structure["models.gptj"].extend(
         [
             "TFGPTJForCausalLM",
@@ -6466,6 +6477,15 @@ if TYPE_CHECKING:
             TFGPT2Model,
             TFGPT2PreTrainedModel,
         )
+        from .models.gpt_neo import (
+            TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFGPTNeoForCausalLM,
+            TFGPTNeoForQuestionAnswering,
+            TFGPTNeoForSequenceClassification,
+            TFGPTNeoForTokenClassification,
+            TFGPTNeoModel,
+            TFGPTNeoPreTrainedModel,
+        )
         from .models.gptj import (
             TFGPTJForCausalLM,
             TFGPTJForQuestionAnswering,

src/transformers/modeling_tf_utils.py

@@ -41,6 +41,7 @@ from .generation import GenerationConfig, TFGenerationMixin
 from .tf_utils import shape_list
 from .utils import (
     DUMMY_INPUTS,
+    MULTIPLE_CHOICE_DUMMY_INPUTS,
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
     TF2_WEIGHTS_INDEX_NAME,
@@ -1117,9 +1118,29 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         Returns:
             `Dict[str, tf.Tensor]`: The dummy inputs.
         """
-        return {
-            "input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32),
-        }
+        dummy_inputs = {}
+
+        serving_sig = self.get_serving_input_signature()
+        if self.main_input_name == "input_ids" and serving_sig[0]["input_ids"].shape.rank == 2:
+            dummy_inputs["input_ids"] = tf.constant(DUMMY_INPUTS, dtype=tf.int32)
+        elif self.main_input_name == "input_ids" and serving_sig[0]["input_ids"].shape.rank == 3:
+            dummy_inputs["input_ids"] = tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32)
+        elif self.main_input_name == "pixel_values":
+            image_shape = serving_sig[0]["pixel_values"].shape.as_list()
+            if image_shape[0] is None:
+                image_shape[0] = 3  # matches DUMMY_INPUTS
+            if None in image_shape[1:]:
+                raise NotImplementedError(
+                    f"Could not fully infer input tensor shape; dummy inputs or serving sig must be defined manually for {self.__class__.__name__}"
+                )
+            rng = np.random.default_rng(42)
+            VISION_DUMMY_INPUTS = rng.random(image_shape).astype(np.float32)
+            dummy_inputs["pixel_values"] = tf.constant(VISION_DUMMY_INPUTS, dtype=tf.float32)
+        else:
+            raise NotImplementedError(
+                f"Could not fully infer input shapes, dummy inputs must be defined manually for {self.__class__.__name__}"
+            )
+        return dummy_inputs
 
     @property
     def framework(self) -> str:
@@ -1140,6 +1161,8 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         self.config = config
         self.name_or_path = config.name_or_path
         self.generation_config = GenerationConfig.from_model_config(config) if self.can_generate() else None
+        if not hasattr(self, "serving"):  # Don't overwrite existing serving signatures
+            self.serving = tf.function(self.eager_serving, input_signature=self.get_serving_input_signature())
         # Set the serving spec quickly to ensure that Keras doesn't use the specific dummy input shapes as the spec
         self._set_save_spec(self.serving.input_signature[0])
 
@@ -1182,12 +1205,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
 
     def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
         """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
-        if head_mask.shape.rank == 1:
+        if len(head_mask.shape) == 1:
             head_mask = head_mask[None, None, :, None, None]
             head_mask = tf.repeat(head_mask, repeats=num_hidden_layers, axis=0)
-        elif head_mask.shape.rank == 2:
+        elif len(head_mask.shape) == 2:
             head_mask = head_mask[:, None, :, None, None]
-        assert head_mask.shape.rank == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
+        assert len(head_mask.shape) == 5, f"head_mask.dim != 5, instead {len(head_mask.shape)}"
         head_mask = tf.cast(head_mask, tf.float32)  # switch to float if need + fp16 compatibility
         return head_mask
 
@@ -1204,36 +1227,50 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
 
         return self.serving_output(output)
 
-    @tf.function(
-        input_signature=[
-            {
-                "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
-                "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
-                "token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
-            }
-        ]
-    )
-    def serving(self, inputs):
-        """
-        Method used for serving the model.
-
-        Args:
-            inputs (`Dict[str, tf.Tensor]`):
-                The input of the saved model as a dictionary of tensors.
-        """
-        output = self.call(inputs)
-
-        return self.serving_output(output)
+    def get_serving_input_signature(self) -> List[Dict[str, tf.TensorSpec]]:
+        model_inputs = list(dict(inspect.signature(self.call).parameters).keys())
+        sig = {}
+        if self.__class__.__name__.endswith("ForMultipleChoice"):
+            text_dims = 3
+        else:
+            text_dims = 2
+        if "input_ids" in model_inputs:
+            for input_name in ("input_ids", "attention_mask", "token_type_ids"):
+                if input_name in model_inputs:
+                    sig[input_name] = tf.TensorSpec([None] * text_dims, tf.int32, name=input_name)
+        if "pixel_values" in model_inputs:
+            pixel_values_shape = [None, None, None, None]
+            if hasattr(self.config, "vision_config"):
+                vision_config = self.config.vision_config
+            else:
+                vision_config = self.config
+            if hasattr(vision_config, "num_channels"):
+                pixel_values_shape[1] = vision_config.num_channels
+            if hasattr(vision_config, "image_size"):
+                pixel_values_shape[2] = pixel_values_shape[3] = vision_config.image_size
+            sig["pixel_values"] = tf.TensorSpec(pixel_values_shape, tf.float32, name="pixel_values")
+        return [sig]
 
     def serving_output(self, output):
         """
-        Prepare the output of the saved model. Each model must implement this function.
-
-        Args:
-            output ([`TFBaseModelOutput`]):
-                The output returned by the model.
+        Prepare the output of the saved model. Can be overridden if specific serving modifications are required.
         """
-        raise NotImplementedError
+        config_variables = {
+            "hidden_states": "output_hidden_states",
+            "attentions": "output_attentions",
+            "past_key_values": "use_cache",
+        }
+        if isinstance(output, ModelOutput):
+            for key, config_var in config_variables.items():
+                if key in output:
+                    if not getattr(self.config, config_var, False):
+                        output[key] = None
+                    elif output[key] is not None:
+                        try:
+                            output[key] = tf.convert_to_tensor(output[key])
+                        except ValueError:
+                            pass  # Layers may not have the same dimensions
+        return output
 
     def can_generate(self) -> bool:
         """

src/transformers/models/auto/modeling_tf_auto.py

@@ -54,6 +54,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("gpt-sw3", "TFGPT2Model"),
         ("gpt2", "TFGPT2Model"),
         ("gptj", "TFGPTJModel"),
+        ("gpt_neo", "TFGPTNeoModel"),
         ("groupvit", "TFGroupViTModel"),
         ("hubert", "TFHubertModel"),
         ("layoutlm", "TFLayoutLMModel"),
@@ -173,6 +174,7 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
         ("gpt-sw3", "TFGPT2LMHeadModel"),
         ("gpt2", "TFGPT2LMHeadModel"),
         ("gptj", "TFGPTJForCausalLM"),
+        ("gpt_neo", "TFGPTNeoForCausalLM"),
         ("openai-gpt", "TFOpenAIGPTLMHeadModel"),
         ("opt", "TFOPTForCausalLM"),
         ("rembert", "TFRemBertForCausalLM"),
@@ -306,6 +308,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("gpt-sw3", "TFGPT2ForSequenceClassification"),
         ("gpt2", "TFGPT2ForSequenceClassification"),
         ("gptj", "TFGPTJForSequenceClassification"),
+        ("gpt_neo", "TFGPTNeoForSequenceClassification"),
         ("layoutlm", "TFLayoutLMForSequenceClassification"),
         ("layoutlmv3", "TFLayoutLMv3ForSequenceClassification"),
         ("longformer", "TFLongformerForSequenceClassification"),
@@ -338,6 +341,7 @@ TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("flaubert", "TFFlaubertForQuestionAnsweringSimple"),
         ("funnel", "TFFunnelForQuestionAnswering"),
         ("gptj", "TFGPTJForQuestionAnswering"),
+        ("gpt_neo", "TFGPTNeoForQuestionAnswering"),
         ("layoutlmv3", "TFLayoutLMv3ForQuestionAnswering"),
         ("longformer", "TFLongformerForQuestionAnswering"),
         ("mobilebert", "TFMobileBertForQuestionAnswering"),
@@ -381,6 +385,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("esm", "TFEsmForTokenClassification"),
         ("flaubert", "TFFlaubertForTokenClassification"),
         ("funnel", "TFFunnelForTokenClassification"),
+        ("gpt_neo", "TFGPTNeoForTokenClassification"),
         ("layoutlm", "TFLayoutLMForTokenClassification"),
         ("layoutlmv3", "TFLayoutLMv3ForTokenClassification"),
         ("longformer", "TFLongformerForTokenClassification"),

src/transformers/models/gpt_neo/__init__.py

@@ -13,7 +13,13 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -37,6 +43,23 @@ else:
         "load_tf_weights_in_gpt_neo",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_gpt_neo"] = [
+        "TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFGPTNeoForCausalLM",
+        "TFGPTNeoForQuestionAnswering",
+        "TFGPTNeoForSequenceClassification",
+        "TFGPTNeoForTokenClassification",
+        "TFGPTNeoModel",
+        "TFGPTNeoPreTrainedModel",
+    ]
+
+
 try:
     if not is_flax_available():
         raise OptionalDependencyNotAvailable()
@@ -70,6 +93,22 @@ if TYPE_CHECKING:
             load_tf_weights_in_gpt_neo,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_gpt_neo import (
+            TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFGPTNeoForCausalLM,
+            TFGPTNeoForQuestionAnswering,
+            TFGPTNeoForSequenceClassification,
+            TFGPTNeoForTokenClassification,
+            TFGPTNeoModel,
+            TFGPTNeoPreTrainedModel,
+        )
+
     try:
         if not is_flax_available():
             raise OptionalDependencyNotAvailable()

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -892,7 +892,6 @@ class GPTNeoForSequenceClassification(GPTNeoPreTrainedModel):
                     f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                     "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                 )
-
         pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
 
         loss = None

src/transformers/models/gpt_neo/modeling_tf_gpt_neo.py

@@ -0,0 +1,959 @@
+# coding=utf-8
+# Copyright 2021 The Eleuther AI and 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.
+""" PyTorch GPT Neo model."""
+
+# TODO Implement weight tying, and see if there's a generic solution (because this will come up in user PRs too)
+from typing import Optional, Tuple, Union
+
+import tensorflow as tf
+
+from ...activations_tf import ACT2FN
+from ...modeling_tf_outputs import (
+    TFBaseModelOutputWithPast,
+    TFBaseModelOutputWithPastAndCrossAttentions,
+    TFCausalLMOutputWithCrossAttentions,
+    TFCausalLMOutputWithPast,
+    TFQuestionAnsweringModelOutput,
+    TFSequenceClassifierOutputWithPast,
+    TFTokenClassifierOutput,
+)
+from ...modeling_tf_utils import TFPreTrainedModel, unpack_inputs, TFCausalLanguageModelingLoss, TFQuestionAnsweringLoss, TFTokenClassificationLoss, TFSequenceClassificationLoss
+from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
+from ...tf_utils import shape_list
+from .configuration_gpt_neo import GPTNeoConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "GPTNeoConfig"
+
+TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "EleutherAI/gpt-neo-1.3B",
+    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
+]
+
+_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neo-1.3B"
+
+class TFGPTNeoSelfAttention(tf.keras.layers.Layer):
+    def __init__(self, config, attention_type, **kwargs):
+        super().__init__(**kwargs)
+
+        max_positions = config.max_position_embeddings
+        bias = tf.linalg.band_part(tf.ones((max_positions, max_positions), dtype=tf.bool), -1, 0)
+        bias = tf.reshape(bias, (1, 1, max_positions, max_positions))
+
+        if attention_type == "local":
+            bias = tf.math.logical_xor(bias, tf.linalg.band_part(bias, -config.window_size, 0))
+
+        self.bias = tf.constant(bias)
+        self.masked_bias = tf.constant(-1e9)
+
+        self.attn_dropout = tf.keras.layers.Dropout(float(config.attention_dropout))
+        self.resid_dropout = tf.keras.layers.Dropout(float(config.resid_dropout))
+
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads})."
+            )
+
+        self.k_proj = tf.keras.layers.Dense(self.embed_dim, use_bias=False, name="k_proj")
+        self.v_proj = tf.keras.layers.Dense(self.embed_dim, use_bias=False, name="v_proj")
+        self.q_proj = tf.keras.layers.Dense(self.embed_dim, use_bias=False, name="q_proj")
+        self.out_proj = tf.keras.layers.Dense(self.embed_dim, use_bias=True, name="out_proj")
+
+    def _split_heads(self, tensor, num_heads, attn_head_size):
+        new_shape = tensor.shape[:-1] + (num_heads, attn_head_size)
+        tensor = tf.reshape(tensor, new_shape)
+        return tf.transpose(tensor, perm=[0, 2, 1, 3])
+
+    def _merge_heads(self, tensor, num_heads, attn_head_size):
+        tensor = tf.transpose(tensor, perm=[0, 2, 1, 3])
+        new_shape = tensor.shape[:-2] + (num_heads * attn_head_size,)
+        return tf.reshape(tensor, new_shape)
+
+    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
+        query = tf.cast(query, tf.float32)
+        key = tf.cast(key, tf.float32)
+
+        attn_weights = tf.matmul(query, key, transpose_b=True)
+
+        query_length, key_length = query.shape[-2], key.shape[-2]
+        causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
+        mask_value = tf.float32.min
+        mask_value = tf.constant(mask_value, dtype=attn_weights.dtype)
+        attn_weights = tf.where(causal_mask, attn_weights, mask_value)
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = tf.nn.softmax(attn_weights, axis=-1)
+        attn_weights = tf.cast(attn_weights, value.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        if head_mask is not None:
+            attn_weights = attn_weights * head_mask
+
+        attn_output = tf.matmul(attn_weights, value)
+
+        return attn_output, attn_weights
+
+    def call(
+        self,
+        hidden_states,
+        attention_mask=None,
+        layer_past=None,
+        head_mask=None,
+        use_cache=False,
+        output_attentions=False,
+        training=None,
+    ):
+        query = self.q_proj(hidden_states)
+        key = self.k_proj(hidden_states)
+        value = self.v_proj(hidden_states)
+
+        query = self._split_heads(query, self.num_heads, self.head_dim)
+        key = self._split_heads(key, self.num_heads, self.head_dim)
+        value = self._split_heads(value, self.num_heads, self.head_dim)
+
+        if layer_past is not None:
+            past_key = layer_past[0]
+            past_value = layer_past[1]
+            key = tf.concat((past_key, key), axis=-2)
+            value = tf.concat((past_value, value), axis=-2)
+
+        if use_cache is True:
+            present = (key, value)
+        else:
+            present = None
+
+        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
+
+        attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
+        attn_output = self.out_proj(attn_output)
+        attn_output = self.resid_dropout(attn_output, training=training)
+
+        outputs = (attn_output, present)
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs  # a, present, (attentions)
+
+class TFGPTNeoAttention(tf.keras.layers.Layer):
+    def __init__(self, config, layer_id=0, **kwargs):
+        super().__init__(**kwargs)
+        self.layer_id = layer_id
+        self.attention_layers = config.attention_layers
+        self.attention_type = self.attention_layers[layer_id]
+
+        if self.attention_type in ["global", "local"]:
+            self.attention = TFGPTNeoSelfAttention(config, self.attention_type, name="attention")
+        else:
+            raise NotImplementedError(
+                "Only attn layer types 'global' and 'local' exist, but got `config.attention_layers`: "
+                f"{config.attention_layers}. Select attn layer types from ['global', 'local'] only."
+            )
+
+    def call(
+        self,
+        hidden_states,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        return self.attention(
+            hidden_states,
+            attention_mask=attention_mask,
+            layer_past=layer_past,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+
+
+class TFGPTNeoMLP(tf.keras.layers.Layer):
+    def __init__(self, intermediate_size, config, **kwargs):  # in MLP: intermediate_size= 4 * hidden_size
+        super().__init__(**kwargs)
+        embed_dim = config.hidden_size
+        self.c_fc = tf.keras.layers.Dense(intermediate_size, name="c_fc")
+        self.c_proj = tf.keras.layers.Dense(embed_dim, name="c_proj")
+        self.act = ACT2FN[config.activation_function]
+        self.dropout = tf.keras.layers.Dropout(float(config.resid_dropout))
+
+    def call(self, hidden_states):
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.c_proj(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class TFGPTNeoBlock(tf.keras.layers.Layer):
+    def __init__(self, config, layer_id, **kwargs):
+        super().__init__(**kwargs)
+        hidden_size = config.hidden_size
+        inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size
+        self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
+        self.attn = TFGPTNeoAttention(config, layer_id, name="attn")
+        self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2")
+        self.mlp = TFGPTNeoMLP(inner_dim, config, name="mlp")
+
+    def call(
+        self,
+        hidden_states,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        use_cache=False,
+        output_attentions=False,
+        training=None,
+    ):
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_outputs = self.attn(
+            hidden_states,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            training=training,
+        )
+        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
+        outputs = attn_outputs[1:]
+        # residual connection
+        hidden_states = attn_output + residual
+
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        feed_forward_hidden_states = self.mlp(hidden_states, training=training)
+        # residual connection
+        hidden_states = residual + feed_forward_hidden_states
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+
+        return outputs  # hidden_states, present, (attentions, cross_attentions)
+
+
+class TFGPTNeoPreTrainedModel(TFPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = GPTNeoConfig
+    base_model_prefix = "transformer"
+    _no_split_modules = ["TFGPTNeoBlock"]
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+
+GPT_NEO_START_DOCSTRING = r"""
+
+    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a TensorFlow [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model)
+    subclass. Use it as a regular TensorFlow Model and refer to the TensorFlow documentation for all matter related to
+    general usage and behavior.
+
+    Parameters:
+        config ([`GPTNeoConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+GPT_NEO_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`tf.Tensor` of shape `(batch_size, input_ids_length)`):
+            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
+            `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
+            sequence tokens in the vocabulary.
+
+            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
+            `input_ids`.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.num_layers`):
+            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
+            `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
+            their past given to this model should not be passed as `input_ids` as they have already been computed.
+        attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        token_type_ids (`tf.Tensor` of shape `(batch_size, input_ids_length)`, *optional*):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+            1]`:
+
+            - 0 corresponds to a *sentence A* token,
+            - 1 corresponds to a *sentence B* token.
+
+            [What are token type IDs?](../glossary#token-type-ids)
+        position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+
+            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
+            `past_key_values`).
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+@add_start_docstrings(
+    "The bare GPT Neo Model transformer outputting raw hidden-states without any specific head on top.",
+    GPT_NEO_START_DOCSTRING,
+)
+class TFGPTNeoModel(TFGPTNeoPreTrainedModel):
+    def __init__(self, config, **kwargs):
+        super().__init__(config, **kwargs)
+
+        self.embed_dim = config.hidden_size
+        self.wte = tf.keras.layers.Embedding(config.vocab_size, self.embed_dim, name="wte")
+        self.wpe = tf.keras.layers.Embedding(config.max_position_embeddings, self.embed_dim, name="wpe")
+        self.drop = tf.keras.layers.Dropout(float(config.embed_dropout))
+        self.h = [TFGPTNeoBlock(config, layer_id=i, name=f"h_._{i}") for i in range(config.num_layers)]
+        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f")
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFBaseModelOutputWithPastAndCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: Optional[tf.Tensor] = None,
+        past_key_values: Optional[Tuple[tf.Tensor]] = None,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = None,
+    ) -> Union[Tuple[tf.Tensor], TFBaseModelOutputWithPastAndCrossAttentions]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = shape_list(input_ids)
+            input_ids = tf.reshape(input_ids, (-1, input_shape[-1]))
+            batch_size = input_shape[0]
+        elif inputs_embeds is not None:
+            input_shape = shape_list(inputs_embeds)[:-1]
+            batch_size = input_shape[0]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if token_type_ids is not None:
+            token_type_ids = tf.reshape(token_type_ids, (-1, input_shape[-1]))
+        if position_ids is not None:
+            position_ids = tf.reshape(position_ids, (-1, input_shape[-1]))
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = [None] * len(self.h)
+        else:
+            past_length = past_key_values[0][0].shape[-2]
+
+        if position_ids is None:
+            position_ids = tf.range(past_length, input_shape[-1] + past_length, dtype=tf.int32)
+            position_ids = tf.expand_dims(position_ids, 0)
+            position_ids = tf.reshape(position_ids, (-1, input_shape[-1]))
+
+        # Attention mask.
+        if attention_mask is not None:
+            if batch_size <= 0:
+                raise ValueError("batch_size has to be defined and > 0")
+            attention_mask = tf.reshape(attention_mask, (batch_size, -1))
+            # We create a 3D attention mask from a 2D tensor mask.
+            # Sizes are [batch_size, 1, 1, to_seq_length]
+            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+            # this attention mask is more simple than the triangular masking of causal attention
+            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+            attention_mask = attention_mask[:, None, None, :]
+
+            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+            # masked positions, this operation will create a tensor which is 0.0 for
+            # positions we want to attend and the dtype's smallest value for masked positions.
+            # Since we are adding it to the raw scores before the softmax, this is
+            # effectively the same as removing these entirely.
+            attention_mask = tf.cast(attention_mask, dtype=self.dtype)  # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * tf.float32.min
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x num_heads x N x N
+        # head_mask has shape n_layer x batch x num_heads x N x N
+        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
+
+        if inputs_embeds is None:
+            tf.debugging.assert_less(
+                input_ids,
+                tf.cast(self.config.vocab_size, dtype=input_ids.dtype),
+                message=(
+                    "input_ids must be smaller than the embedding layer's input dimension (got"
+                    f" {tf.math.reduce_max(input_ids)} >= {self.config.vocab_size})"
+                ),
+            )
+            inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+
+        if token_type_ids is not None:
+            token_type_embeds = self.wte(token_type_ids)
+            hidden_states = hidden_states + token_type_embeds
+
+        hidden_states = self.drop(hidden_states, training=training)
+
+        output_shape = input_shape + [hidden_states.shape[-1]]
+
+        presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            outputs = block(
+                hidden_states,
+                layer_past=layer_past,
+                attention_mask=attention_mask,
+                head_mask=head_mask[i],
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                training=training,
+            )
+
+            hidden_states = outputs[0]
+            if use_cache is True:
+                presents = presents + (outputs[1],)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
+
+        hidden_states = self.ln_f(hidden_states)
+
+        hidden_states = tf.reshape(hidden_states, output_shape)
+        # Addlast hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
+
+        return TFBaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The GPT Neo Model transformer with a language modeling head on top (linear layer with weights tied to the input
+    embeddings).
+    """,
+    GPT_NEO_START_DOCSTRING,
+)
+class TFGPTNeoForCausalLM(TFGPTNeoPreTrainedModel, TFCausalLanguageModelingLoss):
+    _keys_to_ignore_on_load_missing = [
+        r"h\.\d+\.attn\.masked_bias",
+        r"lm_head.weight",
+        r"h\.\d+\.attn\.attention\.bias",
+    ]
+    _keys_to_ignore_on_save = [r"lm_head.weight"]
+    _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]
+
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.transformer = TFGPTNeoModel(config, name="transformer")
+        self.lm_head = tf.keras.layers.Dense(config.vocab_size, use_bias=False, name="lm_head")
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1:]
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = tf.math.cumsum(tf.cast(attention_mask, tf.int32), axis=-1) - 1
+            position_ids = tf.where(attention_mask == 0, 1, position_ids)
+            if past_key_values:
+                position_ids = position_ids[:, -1:]
+        else:
+            position_ids = None
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFCausalLMOutputWithCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: Optional[tf.Tensor] = None,
+        past_key_values: Optional[Tuple[tf.Tensor]] = None,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        labels: Optional[tf.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs,
+    ) -> Union[Tuple[tf.Tensor], TFCausalLMOutputWithCrossAttentions]:
+        r"""
+        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+        hidden_states = transformer_outputs[0]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :]
+            shift_labels = labels[..., 1:]
+            loss = self.hf_compute_loss(shift_labels, shift_logits)
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFCausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(
+        past_key_values: Tuple[Tuple[tf.Tensor]], beam_idx: tf.Tensor
+    ) -> Tuple[Tuple[tf.Tensor]]:
+        """
+        This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
+        [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
+        beam_idx at every generation step.
+        """
+        return tuple(
+            tuple(tf.gather(past_state, beam_idx) for past_state in layer_past)
+            for layer_past in past_key_values
+        )
+
+
+@add_start_docstrings(
+    """
+    The GPTNeo Model transformer with a sequence classification head on top (linear layer).
+
+    [`TFGPTNeoForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-1) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    GPT_NEO_START_DOCSTRING,
+)
+class TFGPTNeoForSequenceClassification(TFGPTNeoPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head.weight"]
+
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
+        self.num_labels = config.num_labels
+        self.transformer = TFGPTNeoModel(config, name="transformer")
+        self.score = tf.keras.layers.Dense(self.num_labels, use_bias=False, name="score")
+
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFSequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: Optional[tf.Tensor] = None,
+        past_key_values: Optional[Tuple[tf.Tensor]] = None,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        labels: Optional[tf.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[tf.Tensor], TFSequenceClassifierOutputWithPast]:
+        r"""
+        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size, sequence_length = shape_list(input_ids)[:2]
+        else:
+            batch_size, sequence_length = shape_list(inputs_embeds)[:2]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = tf.fill(dims=(batch_size,), value=-1)
+        else:
+            if input_ids is not None:
+                sequence_lengths = tf.math.count_nonzero(input_ids != self.config.pad_token_id, axis=1, dtype=tf.int32) - 1
+            else:
+                sequence_lengths = tf.fill(dims=(batch_size,), value=-1)
+                logger.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+
+        pooled_logits = tf.gather_nd(logits, tf.stack([tf.range(batch_size), sequence_lengths], axis=1))
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == tf.int64 or labels.dtype == tf.int32):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = tf.keras.losses.MeanSquaredError()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits, labels)
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+                loss = loss_fct(labels, pooled_logits)
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = tf.keras.losses.BinaryCrossentropy(from_logits=True)
+                loss = loss_fct(labels, pooled_logits)
+            if loss.shape.rank == 0:
+                loss = tf.expand_dims(loss, 0)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFSequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    GPT Neo model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    GPT_NEO_START_DOCSTRING,
+)
+class TFGPTNeoForTokenClassification(TFGPTNeoPreTrainedModel, TFTokenClassificationLoss):
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
+        self.num_labels = config.num_labels
+
+        self.transformer = TFGPTNeoModel(config, name="transformer")
+        self.dropout = tf.keras.layers.Dropout(config.classifier_dropout, name="dropout")
+        self.classifier = tf.keras.layers.Dense(config.num_labels, name="classifier")
+
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint="EleutherAI/gpt-neo-125m",
+        output_type=TFTokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        expected_loss=0.25,
+    )
+    def call(
+        self,
+        input_ids: Optional[tf.Tensor] = None,
+        past_key_values: Optional[Tuple[Tuple[tf.Tensor]]] = None,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        labels: Optional[tf.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = None,
+    ) -> Union[Tuple, TFTokenClassifierOutput]:
+        r"""
+        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        hidden_states = transformer_outputs[0]
+        hidden_states = self.dropout(hidden_states, training=training)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.hf_compute_loss(labels, logits)
+
+        if not return_dict:
+            output = (logits,) + transformer_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFTokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The GPT-Neo Model transformer with a span classification head on top for extractive question-answering tasks like
+    SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    GPT_NEO_START_DOCSTRING,
+)
+class TFGPTNeoForQuestionAnswering(TFGPTNeoPreTrainedModel, TFQuestionAnsweringLoss):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias", r"lm_head.weight"]
+
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
+        self.num_labels = config.num_labels
+        self.transformer = TFGPTNeoModel(config, name="transformer")
+        self.qa_outputs = tf.keras.layers.Dense(2, name="qa_outputs")
+
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFQuestionAnsweringModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+        real_checkpoint=_CHECKPOINT_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: Optional[tf.Tensor] = None,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        start_positions: Optional[tf.Tensor] = None,
+        end_positions: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple, TFQuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = tf.split(logits, 2, axis=-1)
+        start_logits = tf.squeeze(start_logits, axis=-1)
+        end_logits = tf.squeeze(end_logits, axis=-1)
+
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            loss = self.hf_compute_loss({"start_position": start_positions, "end_position": end_positions}, (start_logits, end_logits))
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFQuestionAnsweringModelOutput(
+            loss=loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/utils/dummy_tf_objects.py

@@ -1366,6 +1366,51 @@ class TFGPT2PreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFGPTNeoForCausalLM(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGPTNeoForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGPTNeoForSequenceClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGPTNeoForTokenClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGPTNeoModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGPTNeoPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 class TFGPTJForCausalLM(metaclass=DummyObject):
     _backends = ["tf"]
 

tests/models/gpt_neo/test_modeling_tf_gpt_neo.py

@@ -0,0 +1,602 @@
+# coding=utf-8
+# Copyright 2021 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 GPT Neo model. """
+
+
+import unittest
+
+from transformers import GPTNeoConfig, is_tf_available
+from transformers.testing_utils import require_tf, slow
+from transformers.utils import cached_property
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import (
+        TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
+        GPT2Tokenizer,
+        TFGPTNeoForCausalLM,
+        TFGPTNeoForQuestionAnswering,
+        TFGPTNeoForSequenceClassification,
+        TFGPTNeoForTokenClassification,
+        TFGPTNeoModel,
+    )
+
+import numpy as np
+
+
+class GPTNeoModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=14,
+        seq_length=7,
+        is_training=True,
+        use_token_type_ids=True,
+        use_input_mask=True,
+        use_labels=True,
+        use_mc_token_ids=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=4,
+        attention_types=[[["global", "local"], 2]],
+        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,
+        window_size=7,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_token_type_ids = use_token_type_ids
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.use_mc_token_ids = use_mc_token_ids
+        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.window_size = window_size
+        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.bos_token_id = vocab_size - 1
+        self.eos_token_id = vocab_size - 1
+        self.pad_token_id = vocab_size - 1
+        self.attention_types = attention_types
+
+    def get_large_model_config(self):
+        return GPTNeoConfig.from_pretrained("gpt-neo-125M")
+
+    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)
+
+        mc_token_ids = None
+        if self.use_mc_token_ids:
+            mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
+
+        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()
+
+        head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
+
+        return (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        )
+
+    def get_config(self):
+        return GPTNeoConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_hidden_layers,
+            num_heads=self.num_attention_heads,
+            max_position_embeddings=self.max_position_embeddings,
+            use_cache=True,
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+            window_size=self.window_size,
+            attention_types=self.attention_types,
+        )
+
+    def get_pipeline_config(self):
+        config = self.get_config()
+        config.vocab_size = 300
+        return config
+
+    def prepare_config_and_inputs_for_decoder(self):
+        (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = self.prepare_config_and_inputs()
+
+        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
+        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        return (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
+    def create_and_check_gpt_neo_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = TFGPTNeoModel(config=config)
+
+        result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
+        result = model(input_ids, token_type_ids=token_type_ids)
+        result = model(input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        # past_key_values is not implemented
+        # self.parent.assertEqual(len(result.past_key_values), config.n_layer)
+
+    def create_and_check_gpt_neo_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = TFGPTNeoModel(config=config)
+
+        # first forward pass
+        outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
+        outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
+        outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
+
+        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
+        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
+
+        output, past = outputs.to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+        next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
+
+        # append to next input_ids and token_type_ids
+        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
+        next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
+
+        output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
+        output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
+            "last_hidden_state"
+        ]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).numpy().item()
+        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
+        output_from_past_slice = output_from_past[:, 0, random_slice_idx]
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_gpt_neo_model_attention_mask_past(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, *args
+    ):
+        model = TFGPTNeoModel(config=config)
+
+        # create attention mask
+        attn_mask = np.ones(input_ids.shape, dtype=np.int32)
+        half_seq_length = self.seq_length // 2
+        attn_mask[:, half_seq_length:] = 0
+        attn_mask = tf.convert_to_tensor(attn_mask)
+
+        # first forward pass
+        output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
+
+        # change a random masked slice from input_ids
+        random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy().item() + 1
+        random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).numpy().squeeze(-1)
+        input_ids = input_ids.numpy()
+        input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
+        input_ids = tf.convert_to_tensor(input_ids)
+
+        # append to next input_ids and attn_mask
+        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
+        attn_mask = tf.concat(
+            [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
+            axis=1,
+        )
+
+        # get two different outputs
+        output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
+        output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).numpy().item()
+        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
+        output_from_past_slice = output_from_past[:, 0, random_slice_idx]
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_gpt_neo_model_past_large_inputs(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, *args
+    ):
+        model = TFGPTNeoModel(config=config)
+
+        # first forward pass
+        outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)
+
+        output, past = outputs.to_tuple()
+
+        # create hypothetical next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+        next_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size)
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and token_type_ids
+        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
+        next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
+        next_attention_mask = tf.concat([input_mask, next_mask], axis=-1)
+
+        output_from_no_past = model(
+            next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
+        )["last_hidden_state"]
+        output_from_past = model(
+            next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
+        )["last_hidden_state"]
+        self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).numpy().item()
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
+        output_from_past_slice = output_from_past[:, :, random_slice_idx]
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = TFGPTNeoForCausalLM(config)
+
+        result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_gpt_neo_for_question_answering(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        model = TFGPTNeoForQuestionAnswering(config)
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.start_logits.shape, [self.batch_size, self.seq_length])
+        self.parent.assertEqual(result.end_logits.shape, [self.batch_size, self.seq_length])
+
+    def create_and_check_gpt_neo_for_sequence_classification(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        model = TFGPTNeoForSequenceClassification(config)
+        model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_gpt_neo_for_token_classification(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        model = TFGPTNeoForTokenClassification(config)
+        model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.logits.shape, [self.batch_size, self.seq_length, self.num_labels])
+
+    def create_and_check_forward_and_backwards(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
+        model = TFGPTNeoForCausalLM(config)
+
+        result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+
+        (
+            config,
+            input_ids,
+            input_mask,
+            head_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "head_mask": head_mask,
+        }
+
+        return config, inputs_dict
+
+
+@require_tf
+class TFGPTNeoModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            TFGPTNeoModel,
+            TFGPTNeoForCausalLM,
+            TFGPTNeoForQuestionAnswering,
+            TFGPTNeoForSequenceClassification,
+            TFGPTNeoForTokenClassification,
+        )
+        if is_tf_available()
+        else ()
+    )
+    all_generative_model_classes = (TFGPTNeoForCausalLM,) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": TFGPTNeoModel,
+            "question-answering": TFGPTNeoForQuestionAnswering,
+            "text-classification": TFGPTNeoForSequenceClassification,
+            "token-classification": TFGPTNeoForTokenClassification,
+            "text-generation": TFGPTNeoForCausalLM,
+            "zero-shot": TFGPTNeoForSequenceClassification,
+        }
+        if is_tf_available()
+        else {}
+    )
+    test_onnx = False
+    test_missing_keys = False
+    test_pruning = False
+    test_model_parallel = False
+    test_head_masking = False
+    test_resize_token_embeddings = False
+
+    # special case for DoubleHeads model
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = GPTNeoModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=GPTNeoConfig, n_embd=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_gpt_neo_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_model(*config_and_inputs)
+
+    def test_gpt_neo_model_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_model_past(*config_and_inputs)
+
+    def test_gpt_neo_model_att_mask_past(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_model_attention_mask_past(*config_and_inputs)
+
+    def test_gpt_neo_model_past_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_model_past_large_inputs(*config_and_inputs)
+
+    def test_gpt_neo_lm_head_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
+
+    def test_gpt_neo_question_answering_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_for_question_answering(*config_and_inputs)
+
+    def test_gpt_neo_sequence_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_for_sequence_classification(*config_and_inputs)
+
+    def test_gpt_neo_token_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_for_token_classification(*config_and_inputs)
+
+    def test_model_common_attributes(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
+
+            if model_class in self.all_generative_model_classes:
+                x = model.get_output_embeddings()
+                assert isinstance(x, tf.keras.layers.Layer)
+                name = model.get_bias()
+                assert name is None
+            else:
+                x = model.get_output_embeddings()
+                assert x is None
+                name = model.get_bias()
+                assert name is None
+
+    def _get_hidden_states(self):
+        return tf.constant(
+            [
+                [
+                    [0.4983, -0.7584, -1.6944, 0.5440],
+                    [2.6918, 0.4206, 0.4176, 0.2055],
+                    [-0.0071, -0.0405, -1.4920, -0.3630],
+                    [1.0492, 0.1599, -1.7648, 0.2419],
+                    [-1.8348, 2.0514, -0.1946, 0.3203],
+                    [0.7672, -1.1600, -1.7118, -0.9056],
+                    [0.2986, 0.5372, 0.7729, -0.1927],
+                    [0.0285, 0.2629, -1.1156, -1.1992],
+                ]
+            ],
+            dtype=tf.float32,
+        )
+
+    def test_local_attn_probs(self):
+        model = TFGPTNeoModel.from_pretrained("valhalla/gpt-neo-random-tiny")
+        layer = model.h[1].attn.attention
+        hidden_states = self._get_hidden_states()
+        hidden_states = tf.concat([hidden_states, hidden_states - 0.5], axis=2)
+
+        batch_size, seq_length, _ = hidden_states.shape
+        mask_tokens = 2
+        attention_mask = tf.ones((batch_size, seq_length), dtype=tf.int32)
+        attention_mask[:, -mask_tokens:] = 0  # dont attend last mask_tokens
+
+        attention_mask = tf.reshape(attention_mask, (batch_size, -1))
+        attention_mask = attention_mask[:, None, None, :]
+        attention_mask = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0
+
+        attn_probs = layer(hidden_states, attention_mask=attention_mask, output_attentions=True)[-1]
+
+        # the last 2 tokens are masked, and should have 0 attn_probs
+        self.assertTrue(tf.reduce_all(attn_probs[:, :, -mask_tokens:, -mask_tokens:] == 0))
+
+        # in local attention each token can only attend to the previous window_size tokens (including itself)
+        # here window_size is 4, so a token at index 5 can only attend to indices [2, 3, 4, 5]
+        # and the attn_probs should be 0 for token [0, 1]
+        self.assertTrue(tf.reduce_all(attn_probs[:, :, 5, 2:6] != 0))
+        self.assertTrue(tf.reduce_all(attn_probs[:, :, 5, :2] == 0))
+
+
+@require_tf
+class TFGPTNeoModelLanguageGenerationTest(unittest.TestCase):
+    @cached_property
+    def model(self):
+        return TFGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
+
+    @cached_property
+    def tokenizer(self):
+        return GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
+
+    @slow
+    def test_lm_generate_gpt_neo(self):
+        model = self.model
+        input_ids = tf.constant([[464, 3290]], dtype=tf.int32)  # The dog
+        # fmt: off
+        # The dog-eared copy of the book, which is a collection of essays by the late author,
+        expected_output_ids = [464, 3290, 12, 3380, 4866, 286, 262, 1492, 11, 543, 318, 257, 4947, 286, 27126, 416, 262, 2739, 1772, 11]
+        # fmt: on
+        output_ids = model.generate(input_ids, do_sample=False)
+        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
+
+    @slow
+    def test_gpt_neo_sample(self):
+        model = self.model
+        tokenizer = self.tokenizer
+
+        tf.random.set_seed(0)
+        tokenized = tokenizer("Today is a nice day and", return_tensors="tf", return_token_type_ids=True)
+        input_ids = tokenized.input_ids
+        output_ids = model.generate(input_ids, do_sample=True)
+        output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+        EXPECTED_OUTPUT_STR = "Today is a nice day and if you don’t get the memo here is what you can"
+        self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
+
+    @slow
+    def test_batch_generation(self):
+        model = self.model
+        tokenizer = self.tokenizer
+
+        tokenizer.padding_side = "left"
+
+        # Define PAD Token = EOS Token = 50256
+        tokenizer.pad_token = tokenizer.eos_token
+        model.config.pad_token_id = model.config.eos_token_id
+
+        # use different length sentences to test batching
+        sentences = [
+            "Hello, my dog is a little",
+            "Today, I am",
+        ]
+
+        inputs = tokenizer(sentences, return_tensors="tf", padding=True)
+        input_ids = inputs["input_ids"]
+
+        outputs = model.generate(
+            input_ids=input_ids,
+            attention_mask=inputs["attention_mask"],
+        )
+
+        inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids
+        output_non_padded = model.generate(input_ids=inputs_non_padded)
+
+        num_paddings = inputs_non_padded.shape[-1] - tf.reduce_sum(inputs["attention_mask"][-1]).numpy().item()
+        inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids
+        output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
+
+        batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
+        padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
+
+        expected_output_sentence = [
+            "Hello, my dog is a little bit of a kitty. She is a very sweet and loving",
+            "Today, I am going to talk about the best way to get a job in the",
+        ]
+        self.assertListEqual(expected_output_sentence, batch_out_sentence)
+        self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGPTNeoModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)