From 6c4befeb63c5130806fb0f2e4302514be965c2ae Mon Sep 17 00:00:00 2001 From: Gustavo de Rosa Date: Mon, 29 Apr 2024 16:25:27 +0000 Subject: [PATCH] Delete configuration_phi.py --- configuration_phi.py | 193 ------------------------------------------- 1 file changed, 193 deletions(-) delete mode 100644 configuration_phi.py diff --git a/configuration_phi.py b/configuration_phi.py deleted file mode 100644 index 00694f6..0000000 --- a/configuration_phi.py +++ /dev/null @@ -1,193 +0,0 @@ -# coding=utf-8 -# Copyright 2023 Microsoft 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. - -""" Phi model configuration""" - - -from transformers.configuration_utils import PretrainedConfig -from transformers.utils import logging - - -logger = logging.get_logger(__name__) - -PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = { - "microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json", -} - - -class PhiConfig(PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi - 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 Phi - [microsoft/phi-1](https://huggingface.co/microsoft/phi-1). - - 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 51200): - Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the - `inputs_ids` passed when calling [`PhiModel`]. - hidden_size (`int`, *optional*, defaults to 2048): - Dimension of the hidden representations. - intermediate_size (`int`, *optional*, defaults to 8192): - Dimension of the MLP representations. - num_hidden_layers (`int`, *optional*, defaults to 24): - Number of hidden layers in the Transformer decoder. - num_attention_heads (`int`, *optional*, defaults to 32): - Number of attention heads for each attention layer in the Transformer decoder. - num_key_value_heads (`int`, *optional*): - This is the number of key_value heads that should be used to implement Grouped Query Attention. If - `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if - `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When - converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed - by meanpooling all the original heads within that group. For more details checkout [this - paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to - `num_attention_heads`. - resid_pdrop (`float`, *optional*, defaults to 0.0): - Dropout probability for mlp outputs. - embd_pdrop (`int`, *optional*, defaults to 0.0): - The dropout ratio for the embeddings. - attention_dropout (`float`, *optional*, defaults to 0.0): - The dropout ratio after computing the attention scores. - hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): - The non-linear activation function (function or string) in the decoder. - max_position_embeddings (`int`, *optional*, defaults to 2048): - The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048 - tokens. - 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-05): - The epsilon used by the rms normalization layers. - use_cache (`bool`, *optional*, defaults to `True`): - Whether or not the model should return the last key/values attentions (not used by all models). Only - relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not. - tie_word_embeddings (`bool`, *optional*, defaults to `False`): - Whether to tie weight embeddings - rope_theta (`float`, *optional*, defaults to 10000.0): - The base period of the RoPE embeddings. - rope_scaling (`Dict`, *optional*): - Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling - strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format - is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update - `max_position_embeddings` to the expected new maximum. See the following thread for more information on how - these scaling strategies behave: - https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This - is an experimental feature, subject to breaking API changes in future versions. - partial_rotary_factor (`float`, *optional*, defaults to 0.5): - Percentage of the query and keys which will have rotary embedding. - qk_layernorm (`bool`, *optional*, defaults to `False`): - Whether or not to normalize the Queries and Keys after projecting the hidden states. - bos_token_id (`int`, *optional*, defaults to 1): - Denotes beginning of sequences token id. - eos_token_id (`int`, *optional*, defaults to 2): - Denotes end of sequences token id. - - Example: - - ```python - >>> from transformers import PhiModel, PhiConfig - - >>> # Initializing a Phi-1 style configuration - >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1") - - >>> # Initializing a model from the configuration - >>> model = PhiModel(configuration) - - >>> # Accessing the model configuration - >>> configuration = model.config - ```""" - - model_type = "phi" - keys_to_ignore_at_inference = ["past_key_values"] - - def __init__( - self, - vocab_size=51200, - hidden_size=2048, - intermediate_size=8192, - num_hidden_layers=24, - num_attention_heads=32, - num_key_value_heads=None, - resid_pdrop=0.0, - embd_pdrop=0.0, - attention_dropout=0.0, - hidden_act="gelu_new", - max_position_embeddings=2048, - initializer_range=0.02, - layer_norm_eps=1e-5, - use_cache=True, - tie_word_embeddings=False, - rope_theta=10000.0, - rope_scaling=None, - partial_rotary_factor=0.5, - qk_layernorm=False, - bos_token_id=1, - eos_token_id=2, - **kwargs, - ): - self.vocab_size = vocab_size - self.hidden_size = hidden_size - self.intermediate_size = intermediate_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - - if num_key_value_heads is None: - num_key_value_heads = num_attention_heads - - self.num_key_value_heads = num_key_value_heads - self.resid_pdrop = resid_pdrop - self.embd_pdrop = embd_pdrop - self.attention_dropout = attention_dropout - self.hidden_act = hidden_act - self.max_position_embeddings = max_position_embeddings - self.initializer_range = initializer_range - self.layer_norm_eps = layer_norm_eps - self.use_cache = use_cache - self.rope_theta = rope_theta - self.rope_scaling = rope_scaling - self.partial_rotary_factor = partial_rotary_factor - self.qk_layernorm = qk_layernorm - self._rope_scaling_validation() - - super().__init__( - bos_token_id=bos_token_id, - eos_token_id=eos_token_id, - tie_word_embeddings=tie_word_embeddings, - **kwargs, - ) - - # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation - def _rope_scaling_validation(self): - """ - Validate the `rope_scaling` configuration. - """ - if self.rope_scaling is None: - return - - if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: - raise ValueError( - "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " - f"got {self.rope_scaling}" - ) - rope_scaling_type = self.rope_scaling.get("type", None) - rope_scaling_factor = self.rope_scaling.get("factor", None) - if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: - raise ValueError( - f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" - ) - if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: - raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")