pavankumarbalijepalli/phi2-sqlcoder

1# coding=utf-82# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.3#4# Licensed under the Apache License, Version 2.0 (the "License");5# you may not use this file except in compliance with the License.6# You may obtain a copy of the License at7#8#     http://www.apache.org/licenses/LICENSE-2.09#10# Unless required by applicable law or agreed to in writing, software11# distributed under the License is distributed on an "AS IS" BASIS,12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.13# See the License for the specific language governing permissions and14# limitations under the License.15 16""" Phi model configuration"""17 18 19from transformers.configuration_utils import PretrainedConfig20from transformers.utils import logging21 22 23logger = logging.get_logger(__name__)24 25PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {26    "microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",27}28 29 30class PhiConfig(PretrainedConfig):31    r"""32    This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi33    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the34    defaults will yield a similar configuration to that of the Phi35    [microsoft/phi-1](https://huggingface.co/microsoft/phi-1).36 37    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the38    documentation from [`PretrainedConfig`] for more information.39 40    Args:41        vocab_size (`int`, *optional*, defaults to 51200):42            Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the43            `inputs_ids` passed when calling [`PhiModel`].44        hidden_size (`int`, *optional*, defaults to 2048):45            Dimension of the hidden representations.46        intermediate_size (`int`, *optional*, defaults to 8192):47            Dimension of the MLP representations.48        num_hidden_layers (`int`, *optional*, defaults to 24):49            Number of hidden layers in the Transformer decoder.50        num_attention_heads (`int`, *optional*, defaults to 32):51            Number of attention heads for each attention layer in the Transformer decoder.52        num_key_value_heads (`int`, *optional*):53            This is the number of key_value heads that should be used to implement Grouped Query Attention. If54            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if55            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When56            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed57            by meanpooling all the original heads within that group. For more details checkout [this58            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to59            `num_attention_heads`.60        resid_pdrop (`float`, *optional*, defaults to 0.0):61            Dropout probability for mlp outputs.62        embd_pdrop (`int`, *optional*, defaults to 0.0):63            The dropout ratio for the embeddings.64        attention_dropout (`float`, *optional*, defaults to 0.0):65            The dropout ratio after computing the attention scores.66        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):67            The non-linear activation function (function or string) in the decoder.68        max_position_embeddings (`int`, *optional*, defaults to 2048):69            The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 204870            tokens.71        initializer_range (`float`, *optional*, defaults to 0.02):72            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.73        layer_norm_eps (`float`, *optional*, defaults to 1e-05):74            The epsilon used by the rms normalization layers.75        use_cache (`bool`, *optional*, defaults to `True`):76            Whether or not the model should return the last key/values attentions (not used by all models). Only77            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.78        tie_word_embeddings (`bool`, *optional*, defaults to `False`):79            Whether to tie weight embeddings80        rope_theta (`float`, *optional*, defaults to 10000.0):81            The base period of the RoPE embeddings.82        rope_scaling (`Dict`, *optional*):83            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling84            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format85            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update86            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how87            these scaling strategies behave:88            https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This89            is an experimental feature, subject to breaking API changes in future versions.90        partial_rotary_factor (`float`, *optional*, defaults to 0.5):91            Percentage of the query and keys which will have rotary embedding.92        qk_layernorm (`bool`, *optional*, defaults to `False`):93            Whether or not to normalize the Queries and Keys after projecting the hidden states.94        bos_token_id (`int`, *optional*, defaults to 1):95            Denotes beginning of sequences token id.96        eos_token_id (`int`, *optional*, defaults to 2):97            Denotes end of sequences token id.98 99    Example:100 101    ```python102    >>> from transformers import PhiModel, PhiConfig103 104    >>> # Initializing a Phi-1 style configuration105    >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")106 107    >>> # Initializing a model from the configuration108    >>> model = PhiModel(configuration)109 110    >>> # Accessing the model configuration111    >>> configuration = model.config112    ```"""113 114    model_type = "phi"115    keys_to_ignore_at_inference = ["past_key_values"]116 117    def __init__(118        self,119        vocab_size=51200,120        hidden_size=2048,121        intermediate_size=8192,122        num_hidden_layers=24,123        num_attention_heads=32,124        num_key_value_heads=None,125        resid_pdrop=0.0,126        embd_pdrop=0.0,127        attention_dropout=0.0,128        hidden_act="gelu_new",129        max_position_embeddings=2048,130        initializer_range=0.02,131        layer_norm_eps=1e-5,132        use_cache=True,133        tie_word_embeddings=False,134        rope_theta=10000.0,135        rope_scaling=None,136        partial_rotary_factor=0.5,137        qk_layernorm=False,138        bos_token_id=1,139        eos_token_id=2,140        **kwargs,141    ):142        self.vocab_size = vocab_size143        self.hidden_size = hidden_size144        self.intermediate_size = intermediate_size145        self.num_hidden_layers = num_hidden_layers146        self.num_attention_heads = num_attention_heads147 148        if num_key_value_heads is None:149            num_key_value_heads = num_attention_heads150 151        self.num_key_value_heads = num_key_value_heads152        self.resid_pdrop = resid_pdrop153        self.embd_pdrop = embd_pdrop154        self.attention_dropout = attention_dropout155        self.hidden_act = hidden_act156        self.max_position_embeddings = max_position_embeddings157        self.initializer_range = initializer_range158        self.layer_norm_eps = layer_norm_eps159        self.use_cache = use_cache160        self.rope_theta = rope_theta161        self.rope_scaling = rope_scaling162        self.partial_rotary_factor = partial_rotary_factor163        self.qk_layernorm = qk_layernorm164        self._rope_scaling_validation()165 166        super().__init__(167            bos_token_id=bos_token_id,168            eos_token_id=eos_token_id,169            tie_word_embeddings=tie_word_embeddings,170            **kwargs,171        )172 173    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation174    def _rope_scaling_validation(self):175        """176        Validate the `rope_scaling` configuration.177        """178        if self.rope_scaling is None:179            return180 181        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:182            raise ValueError(183                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "184                f"got {self.rope_scaling}"185            )186        rope_scaling_type = self.rope_scaling.get("type", None)187        rope_scaling_factor = self.rope_scaling.get("factor", None)188        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:189            raise ValueError(190                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"191            )192        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:193            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")