[Fnet P0]add PretrainedConfig and unit test

paddlenlp/transformers/__init__.py

@@ -105,6 +105,7 @@
 from .ernie_m.tokenizer import *
 from .fnet.modeling import *
 from .fnet.tokenizer import *
+from .fnet.configuration import *
 from .funnel.modeling import *
 from .funnel.tokenizer import *
 from .funnel.configuration import *

paddlenlp/transformers/fnet/configuration.py

@@ -0,0 +1,142 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" fnet model configuration"""
+from __future__ import annotations
+
+from paddlenlp.transformers.configuration_utils import PretrainedConfig
+
+__all__ = [
+    "FNET_PRETRAINED_INIT_CONFIGURATION",
+    "FNET_PRETRAINED_RESOURCE_FILES_MAP",
+    "FnetConfig",
+]
+
+FNET_PRETRAINED_INIT_CONFIGURATION = {
+    "fnet-base": {
+        "vocab_size": 32000,
+        "hidden_size": 768,
+        "num_hidden_layers": 12,
+        "intermediate_size": 3072,
+        "hidden_act": "gelu_new",
+        "hidden_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 4,
+        "initializer_range": 0.02,
+        "layer_norm_eps": 1e-12,
+        "pad_token_id": 3,
+        "bos_token_id": 1,
+        "eos_token_id": 2,
+    },
+    "fnet-large": {
+        "vocab_size": 32000,
+        "hidden_size": 1024,
+        "num_hidden_layers": 24,
+        "intermediate_size": 4096,
+        "hidden_act": "gelu_new",
+        "hidden_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 4,
+        "initializer_range": 0.02,
+        "layer_norm_eps": 1e-12,
+        "pad_token_id": 3,
+        "bos_token_id": 1,
+        "eos_token_id": 2,
+    },
+}
+FNET_PRETRAINED_RESOURCE_FILES_MAP = {
+    "model_state": {
+        "fnet-base": "https://bj.bcebos.com/paddlenlp/models/transformers/fnet/fnet-base/model_state.pdparams",
+        "fnet-large": "https://bj.bcebos.com/paddlenlp/models/transformers/fnet/fnet-large/model_state.pdparams",
+    }
+}
+
+
+class FnetConfig(PretrainedConfig):
+    r"""
+    Args:
+        vocab_size (int, optional):
+            Vocabulary size of `inputs_ids` in `FNetModel`. Also is the vocab size of token embedding matrix.
+            Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `FNetModel`.
+            Defaults to `32000`.
+        hidden_size (int, optional):
+            Dimensionality of the encoder layer and pooler layer. Defaults to `768`.
+        num_hidden_layers (int, optional):
+            Number of hidden layers in the Transformer encoder. Defaults to `12`.
+        intermediate_size (int, optional):
+            Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors
+            to ff layers are firstly projected from `hidden_size` to `intermediate_size`,
+            and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`.
+            Defaults to `3072`.
+        hidden_act (str, optional):
+            The non-linear activation function in the feed-forward layer.
+            ``"gelu"``, ``"relu"`` and any other paddle supported activation functions
+            are supported. Defaults to `glue_new`.
+        hidden_dropout_prob (float, optional):
+            The dropout probability for all fully connected layers in the embeddings and encoder.
+            Defaults to `0.1`.
+        max_position_embeddings (int, optional):
+            The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
+            sequence. Defaults to `512`.
+        type_vocab_size (int, optional):
+            The vocabulary size of `token_type_ids`. Defaults to `4`.
+        initializer_range (float, optional):
+            The standard deviation of the normal initializer. Defaults to `0.02`.
+            .. note::
+                A normal_initializer initializes weight matrices as normal distributions.
+                See :meth:`BertPretrainedModel.init_weights()` for how weights are initialized in `ElectraModel`.
+        layer_norm_eps(float, optional):
+            The `epsilon` parameter used in :class:`paddle.nn.LayerNorm` for initializing layer normalization layers.
+            A small value to the variance added to the normalization layer to prevent division by zero.
+            Defaults to `1e-12`.
+        pad_token_id (int, optional):
+            The index of padding token in the token vocabulary. Defaults to `3`.
+        add_pooling_layer(bool, optional):
+            Whether or not to add the pooling layer. Defaults to `True`.
+    """
+
+    model_type = "fnet"
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        num_hidden_layers=12,
+        intermediate_size=3072,
+        hidden_act="gelu_new",
+        hidden_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=4,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=3,
+        bos_token_id=1,
+        eos_token_id=2,
+        add_pooling_layer=True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.add_pooling_layer = add_pooling_layer