Create configuration_molformer.py

configuration_molformer.py

@@ -0,0 +1,158 @@
+# 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.
+""" Molformer model configuration"""
+
+from collections import OrderedDict
+from typing import Mapping
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "ibm/MoLFormer-XL-both-10pct": "https://huggingface.co/ibm/MoLFormer-XL-both-10pct/resolve/main/config.json",
+}
+
+
+class MolformerConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MolformerModel`]. It is used to instantiate an
+    Molformer model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the Molformer
+    [ibm/MoLFormer-XL-both-10pct](https://huggingface.co/ibm/MoLFormer-XL-both-10pct) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 2362):
+            Vocabulary size of the Molformer model. Defines the number of different tokens that can be represented by
+            the `inputs_ids` passed when calling [`MolformerModel`] or [`TFMolformerModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimension of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 768):
+            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        embedding_dropout_prob (`float`, *optional*, defaults to 0.2):
+            The dropout probability for the word embeddings.
+        max_position_embeddings (`int`, *optional*, defaults to 202):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 1536).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        linear_attention_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the linear attention layers normalization step.
+        num_random_features (`int`, *optional*, defaults to 32):
+            Random feature map dimension used in linear attention.
+        feature_map_kernel (`str` or `function`, *optional*, defaults to `"relu"`):
+            The non-linear activation function (function or string) in the generalized random features. If string,
+            `"gelu"`, `"relu"`, `"selu"`, and `"gelu_new"` ar supported.
+        deterministic_eval (`bool`, *optional*, defaults to `False`):
+            Whether the random features should only be redrawn when training or not. If `True` and `model.training` is
+            `False`, linear attention random feature weights will be constant, i.e., deterministic.
+        classifier_dropout_prob (`float`, *optional*):
+            The dropout probability for the classification head. If `None`, use `hidden_dropout_prob`.
+        classifier_skip_connection (`bool`, *optional*, defaults to `True`):
+            Whether a skip connection should be made between the layers of the classification head or not.
+        pad_token_id (`int`, *optional*, defaults to 2):
+            The id of the _padding_ token.
+
+    Example:
+
+    ```python
+    >>> from transformers import MolformerModel, MolformerConfig
+
+    >>> # Initializing a Molformer ibm/MoLFormer-XL-both-10pct style configuration
+    >>> configuration = MolformerConfig()
+
+    >>> # Initializing a model from the ibm/MoLFormer-XL-both-10pct style configuration
+    >>> model = MolformerModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "molformer"
+
+    def __init__(
+        self,
+        vocab_size=2362,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=768,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        embedding_dropout_prob=0.2,
+        max_position_embeddings=202,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        linear_attention_eps=1e-6,
+        num_random_features=32,
+        feature_map_kernel="relu",
+        deterministic_eval=False,
+        classifier_dropout_prob=None,
+        classifier_skip_connection=True,
+        pad_token_id=2,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        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.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.embedding_dropout_prob = embedding_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.linear_attention_eps = linear_attention_eps
+        self.num_random_features = num_random_features
+        self.feature_map_kernel = feature_map_kernel
+        self.deterministic_eval = deterministic_eval
+        self.classifier_dropout_prob = classifier_dropout_prob
+        self.classifier_skip_connection = classifier_skip_connection
+
+
+# Copied from transformers.models.roberta.configuration_roberta.RobertaOnnxConfig with Roberta->Molformer
+class MolformerOnnxConfig(OnnxConfig):
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task == "multiple-choice":
+            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+        else:
+            dynamic_axis = {0: "batch", 1: "sequence"}
+        return OrderedDict(
+            [
+                ("input_ids", dynamic_axis),
+                ("attention_mask", dynamic_axis),
+            ]
+        )