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"""OPT model configuration""" |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class OPTConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT model |
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according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
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defaults will yield a similar configuration to that of the OPT |
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[facebook/opt-350m](https://huggingface.co/facebook/opt-350m) architecture. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 50272): |
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Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`OPTModel`] |
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hidden_size (`int`, *optional*, defaults to 768): |
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Dimensionality of the layers and the pooler layer. |
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num_hidden_layers (`int`, *optional*, defaults to 12): |
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Number of decoder layers. |
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ffn_dim (`int`, *optional*, defaults to 3072): |
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. |
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num_attention_heads (`int`, *optional*, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer decoder. |
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activation_function (`str` or `function`, *optional*, defaults to `"relu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, |
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`"relu"`, `"silu"` and `"gelu_new"` are supported. |
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max_position_embeddings (`int`, *optional*, defaults to 2048): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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do_layer_norm_before (`bool`, *optional*, defaults to `True`): |
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Whether to perform layer normalization before the attention block. |
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word_embed_proj_dim (`int`, *optional*): |
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`word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to |
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`hidden_size`. |
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dropout (`float`, *optional*, defaults to 0.1): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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attention_dropout (`float`, *optional*, defaults to 0.0): |
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The dropout ratio for the attention probabilities. |
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layerdrop (`float`, *optional*, defaults to 0.0): |
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The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more |
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details. |
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init_std (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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use_cache (`bool`, *optional*, defaults to `True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). |
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enable_bias (`bool`, *optional*, defaults to `True`): |
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Whether or not if the linear layers in the attention blocks should use the bias term. |
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layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`): |
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Whether or not if the layer norms should have learnable parameters. |
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Example: |
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```python |
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>>> from transformers import OPTConfig, OPTModel |
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>>> # Initializing a OPT facebook/opt-large style configuration |
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>>> configuration = OPTConfig() |
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>>> # Initializing a model (with random weights) from the facebook/opt-large style configuration |
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>>> model = OPTModel(configuration) |
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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```""" |
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model_type = "opt" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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vocab_size=50272, |
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hidden_size=768, |
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num_hidden_layers=12, |
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ffn_dim=3072, |
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max_position_embeddings=2048, |
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do_layer_norm_before=True, |
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_remove_final_layer_norm=False, |
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word_embed_proj_dim=None, |
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dropout=0.1, |
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attention_dropout=0.0, |
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num_attention_heads=12, |
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activation_function="relu", |
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layerdrop=0.0, |
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init_std=0.02, |
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use_cache=True, |
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pad_token_id=1, |
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bos_token_id=2, |
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eos_token_id=2, |
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enable_bias=True, |
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layer_norm_elementwise_affine=True, |
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**kwargs, |
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): |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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**kwargs, |
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) |
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self.vocab_size = vocab_size |
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self.max_position_embeddings = max_position_embeddings |
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self.num_attention_heads = num_attention_heads |
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self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size |
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self.ffn_dim = ffn_dim |
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self.hidden_size = hidden_size |
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self.num_hidden_layers = num_hidden_layers |
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self.dropout = dropout |
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self.attention_dropout = attention_dropout |
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self.activation_function = activation_function |
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self.init_std = init_std |
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self.layerdrop = layerdrop |
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self.use_cache = use_cache |
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self.do_layer_norm_before = do_layer_norm_before |
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self.enable_bias = enable_bias |
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self.layer_norm_elementwise_affine = layer_norm_elementwise_affine |
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self._remove_final_layer_norm = _remove_final_layer_norm |
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