Upload configuration_opt.py

configuration_opt.py

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+# coding=utf-8
+# Copyright 2022 The Metaseq Authors and 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.
+"""OPT model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class OPTConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT 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 OPT
+    [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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 50272):
+            Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`OPTModel`]
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of decoder layers.
+        ffn_dim (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            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 2048).
+        do_layer_norm_before (`bool`, *optional*, defaults to `True`):
+            Whether to perform layer normalization before the attention block.
+        word_embed_proj_dim (`int`, *optional*):
+            `word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to
+            `hidden_size`.
+        dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
+            details.
+        init_std (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+        enable_bias (`bool`, *optional*, defaults to `True`):
+            Whether or not if the linear layers in the attention blocks should use the bias term.
+        layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether or not if the layer norms should have learnable parameters.
+
+    Example:
+
+    ```python
+    >>> from transformers import OPTConfig, OPTModel
+
+    >>> # Initializing a OPT facebook/opt-large style configuration
+    >>> configuration = OPTConfig()
+
+    >>> # Initializing a model (with random weights) from the facebook/opt-large style configuration
+    >>> model = OPTModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "opt"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50272,
+        hidden_size=768,
+        num_hidden_layers=12,
+        ffn_dim=3072,
+        max_position_embeddings=2048,
+        do_layer_norm_before=True,
+        _remove_final_layer_norm=False,
+        word_embed_proj_dim=None,
+        dropout=0.1,
+        attention_dropout=0.0,
+        num_attention_heads=12,
+        activation_function="relu",
+        layerdrop=0.0,
+        init_std=0.02,
+        use_cache=True,
+        pad_token_id=1,
+        bos_token_id=2,
+        eos_token_id=2,
+        enable_bias=True,
+        layer_norm_elementwise_affine=True,
+        attn_implementation='eager',
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.num_attention_heads = num_attention_heads
+        self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
+        self.ffn_dim = ffn_dim
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_function = activation_function
+        self.init_std = init_std
+        self.layerdrop = layerdrop
+        self.use_cache = use_cache
+        self.do_layer_norm_before = do_layer_norm_before
+        # We keep these variables at `True` for backward compatibility.
+        self.enable_bias = enable_bias
+        self.layer_norm_elementwise_affine = layer_norm_elementwise_affine
+
+        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
+        # with checkpoints that have been fine-tuned before transformers v4.20.1
+        # see https://github.com/facebookresearch/metaseq/pull/164
+        self._remove_final_layer_norm = _remove_final_layer_norm
+        self.attn_implementation = attn_implementation