Aluode/PerceptionLabPortable

1# coding=utf-82# Copyright 2024 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-3 model configuration"""17 18from ...configuration_utils import PretrainedConfig19from ...utils import logging20 21 22logger = logging.get_logger(__name__)23 24 25class Phi3Config(PretrainedConfig):26    r"""27    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-328    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the29    defaults will yield a similar configuration to that of the30    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).31 32    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the33    documentation from [`PretrainedConfig`] for more information.34 35    Args:36        vocab_size (`int`, *optional*, defaults to 32064):37            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the38            `inputs_ids` passed when calling [`Phi3Model`].39        hidden_size (`int`, *optional*, defaults to 3072):40            Dimension of the hidden representations.41        intermediate_size (`int`, *optional*, defaults to 8192):42            Dimension of the MLP representations.43        num_hidden_layers (`int`, *optional*, defaults to 32):44            Number of hidden layers in the Transformer decoder.45        num_attention_heads (`int`, *optional*, defaults to 32):46            Number of attention heads for each attention layer in the Transformer decoder.47        num_key_value_heads (`int`, *optional*):48            This is the number of key_value heads that should be used to implement Grouped Query Attention. If49            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if50            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When51            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed52            by meanpooling all the original heads within that group. For more details, check out [this53            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to54            `num_attention_heads`.55        resid_pdrop (`float`, *optional*, defaults to 0.0):56            Dropout probability for mlp outputs.57        embd_pdrop (`int`, *optional*, defaults to 0.0):58            The dropout ratio for the embeddings.59        attention_dropout (`float`, *optional*, defaults to 0.0):60            The dropout ratio after computing the attention scores.61        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):62            The non-linear activation function (function or string) in the decoder.63        max_position_embeddings (`int`, *optional*, defaults to 4096):64            The maximum sequence length that this model might ever be used with.65        original_max_position_embeddings (`int`, *optional*, defaults to 4096):66            The maximum sequence length that this model was trained with. This is used to determine the size of the67            original RoPE embeddings when using long scaling.68        initializer_range (`float`, *optional*, defaults to 0.02):69            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.70        rms_norm_eps (`float`, *optional*, defaults to 1e-05):71            The epsilon value used for the RMSNorm.72        use_cache (`bool`, *optional*, defaults to `True`):73            Whether or not the model should return the last key/values attentions (not used by all models). Only74            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.75        tie_word_embeddings (`bool`, *optional*, defaults to `False`):76            Whether to tie weight embeddings77        rope_theta (`float`, *optional*, defaults to 10000.0):78            The base period of the RoPE embeddings.79        rope_scaling (`dict`, *optional*):80            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must81            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and82            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size83            divided by the number of attention heads divided by 2.84        partial_rotary_factor (`float`, *optional*, defaults to 1.0):85            Percentage of the query and keys which will have rotary embedding. Must be between 0.0 and 1.0.86        bos_token_id (`int`, *optional*, defaults to 1):87            The id of the "beginning-of-sequence" token.88        eos_token_id (`int`, *optional*, defaults to 32000):89            The id of the "end-of-sequence" token.90        pad_token_id (`int`, *optional*, defaults to 32000):91            The id of the padding token.92        sliding_window (`int`, *optional*):93            Sliding window attention window size. If `None`, no sliding window is applied.94 95    Example:96 97    ```python98    >>> from transformers import Phi3Model, Phi3Config99 100    >>> # Initializing a Phi-3 style configuration101    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")102 103    >>> # Initializing a model from the configuration104    >>> model = Phi3Model(configuration)105 106    >>> # Accessing the model configuration107    >>> configuration = model.config108    ```"""109 110    model_type = "phi3"111    keys_to_ignore_at_inference = ["past_key_values"]112    base_model_tp_plan = {113        "layers.*.self_attn.qkv_proj": "colwise_rep",  # we need to replicate here due to the slicing of qkv114        "layers.*.self_attn.o_proj": "rowwise_rep",  # we need to replicate here due to the slicing of qkv115        "layers.*.mlp.gate_up_proj": "colwise_rep",  # we need to replicate here due to the `chunk` operation116        "layers.*.mlp.down_proj": "rowwise_rep",  # we need to replicate here due to the `chunk` operation117    }118    base_model_pp_plan = {119        "embed_tokens": (["input_ids"], ["inputs_embeds"]),120        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),121        "norm": (["hidden_states"], ["hidden_states"]),122    }123 124    def __init__(125        self,126        vocab_size=32064,127        hidden_size=3072,128        intermediate_size=8192,129        num_hidden_layers=32,130        num_attention_heads=32,131        num_key_value_heads=None,132        resid_pdrop=0.0,133        embd_pdrop=0.0,134        attention_dropout=0.0,135        hidden_act="silu",136        max_position_embeddings=4096,137        original_max_position_embeddings=4096,138        initializer_range=0.02,139        rms_norm_eps=1e-5,140        use_cache=True,141        tie_word_embeddings=False,142        rope_theta=10000.0,143        rope_scaling=None,144        partial_rotary_factor=1.0,145        bos_token_id=1,146        eos_token_id=32000,147        pad_token_id=32000,148        sliding_window=None,149        **kwargs,150    ):151        self.vocab_size = vocab_size152        self.hidden_size = hidden_size153        self.intermediate_size = intermediate_size154        self.num_hidden_layers = num_hidden_layers155        self.num_attention_heads = num_attention_heads156 157        if num_key_value_heads is None:158            num_key_value_heads = num_attention_heads159 160        self.num_key_value_heads = num_key_value_heads161        self.resid_pdrop = resid_pdrop162        self.embd_pdrop = embd_pdrop163        self.attention_dropout = attention_dropout164        self.hidden_act = hidden_act165        self.max_position_embeddings = max_position_embeddings166        self.original_max_position_embeddings = original_max_position_embeddings167        self.initializer_range = initializer_range168        self.rms_norm_eps = rms_norm_eps169        self.use_cache = use_cache170        self.rope_theta = rope_theta171        self.rope_scaling = rope_scaling172        self.partial_rotary_factor = partial_rotary_factor173        self._rope_scaling_adjustment()174        self._rope_scaling_validation()175        self.sliding_window = sliding_window176 177        super().__init__(178            bos_token_id=bos_token_id,179            eos_token_id=eos_token_id,180            pad_token_id=pad_token_id,181            tie_word_embeddings=tie_word_embeddings,182            **kwargs,183        )184 185    def _rope_scaling_adjustment(self):186        """187        Adjust the `type` of the `rope_scaling` configuration for backward compatibility.188        """189        if self.rope_scaling is None:190            return191 192        rope_scaling_type = self.rope_scaling.get("type", None)193 194        # For backward compatibility if previous version used "su" or "yarn"195        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:196            self.rope_scaling["type"] = "longrope"197 198    def _rope_scaling_validation(self):199        """200        Validate the `rope_scaling` configuration.201        """202        if self.rope_scaling is None:203            return204 205        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:206            raise ValueError(207                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "208                f"got {self.rope_scaling}"209            )210        rope_scaling_type = self.rope_scaling.get("type", None)211        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)212        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)213        if rope_scaling_type is None or rope_scaling_type != "longrope":214            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")215        if not (216            isinstance(rope_scaling_short_factor, list)217            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)218        ):219            raise ValueError(220                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"221            )222        rotary_ndims = int(self.hidden_size // self.num_attention_heads * self.partial_rotary_factor)223        if not len(rope_scaling_short_factor) == rotary_ndims // 2:224            raise ValueError(225                f"`rope_scaling`'s short_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_short_factor)}"226            )227        if not (228            isinstance(rope_scaling_long_factor, list)229            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)230        ):231            raise ValueError(232                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"233            )234        if not len(rope_scaling_long_factor) == rotary_ndims // 2:235            raise ValueError(236                f"`rope_scaling`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_long_factor)}"237            )238 239 240__all__ = ["Phi3Config"]241