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# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
from typing import Any, Dict, List, Optional, Union
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from functools import cached_property
""" Phi3Small model configuration """
logger = logging.get_logger(__name__)
def next_mult(x, y):
return (x + y - 1) // y * y
class Phi3SmallConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a `Phi3Small` model. It is used to
instantiate a Phi-3-small 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-3-small
[phi3](https://arxiv.org/pdf/2404.14219) 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 100352):
Vocabulary size of the Phi3Small model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling `Phi3Small`.
max_position_embeddings (`int`, *optional*, defaults to 8192):
The maximum sequence length that this model might safely be used with.
rope_embedding_base (`float`, *optional*, defaults to 10^6):
The base value for the RoPE (Relative Position Encoding) embedding.
rope_position_scale (`float`, *optional*, defaults to 1.0):
The scale factor for the RoPE position encoding.
rope_scaling (`Optional[Dict[str, Union[float, List[float], int]]]`, *optional*, defaults to None):
The scaling configuration used for LongRoPE.
hidden_size (`int`, *optional*, defaults to 4096):
The size of the hidden layers in the model.
num_hidden_layers (`int`, *optional*, defaults to 32):
The number of layers in the model.
num_attention_heads (`int`, *optional*, defaults to 32):
The number of query heads in the model.
num_key_value_heads (`int`, *optional*, defaults to 8):
The number of key-value heads in the model.
hidden_act (`str`, *optional*, defaults to "gegelu"):
The activation function used in the model.
gegelu_limit (`float`, *optional*, defaults to 20.0):
The limit value for the GELU activation function (for numerical stability).
gegelu_pad_to_256 (`bool`, *optional*, defaults to True):
Whether to pad the intermediate size to a multiple of 256 (for faster matmul ops).
ff_dim_multiplier (`Optional[int]`, *optional*, defaults to None):
The dimension multiplier for the feed-forward layers.
ff_intermediate_size (`Optional[int]`, *optional*, defaults to 14336):
The intermediate size for the feed-forward layers.
One of `ff_dim_multiplier` or `ff_intermediate_size` must be specified.
blocksparse_homo_head_pattern (`bool`, *optional*, defaults to False):
Whether to use a homogeneous head pattern for block-sparse attention.
blocksparse_block_size (`int`, *optional*, defaults to 64):
The block size for block-sparse attention.
blocksparse_num_local_blocks (`int`, *optional*, defaults to 16):
The number of local blocks for block-sparse attention.
The local window used in blocksparse equals `blocksparse_num_local_blocks * blocksparse_block_size`
blocksparse_vert_stride (`int`, *optional*, defaults to 8):
The vertical stride for block-sparse attention.
blocksparse_triton_kernel_block_size (`int`, *optional*, defaults to 64):
The kernel block size for block-sparse attention.
dense_attention_every_n_layers (`Optional[int]`, *optional*, defaults to 2):
The frequency of all dense attention layers in the model
embedding_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for the embedding layer.
attention_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for the attention layers.
ffn_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for the feed-forward layers.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon value for layer normalization.
initializer_range (`float`, *optional*, defaults to 0.02):
The range for weight initialization.
mup_use_scaling (`bool`, *optional*, defaults to True):
Whether to use scaling for MuP parameters (see: https://arxiv.org/abs/2203.03466).
mup_width_multiplier (`bool`, *optional*, defaults to 8.0):
The width multiplier for MuP.
mup_embedding_multiplier (`bool`, *optional*, defaults to 10.0):
The embedding multiplier for MuP.
mup_attn_multiplier (`bool`, *optional*, defaults to 1.0):
The attention multiplier for MuP.
use_cache (`bool`, *optional*, defaults to True):
Whether to use cache for the model.
bos_token_id (`int`, *optional*, defaults to 100257):
The token ID for the beginning of sentence.
eos_token_id (`int`, *optional*, defaults to 100257):
The token ID for the end of sentence.
reorder_and_upcast_attn (`bool`, *optional*, defaults to False):
Whether to reorder and upcast attention.
pad_sequence_to_multiple_of_64 (`bool`, *optional*, defaults to True):
Whether to pad the sequence length to a multiple of 64.
**kwargs:
Additional keyword arguments.
Example:
```python
>>> from transformers import Phi3SmallConfig, Phi3SmallModel
>>> # Initializing a Phi3Small configuration
>>> configuration = Phi3SmallConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = Phi3SmallModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "phi3small"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
# General information about the model
vocab_size: int =100352,
max_position_embeddings: int = 8192,
# RoPE Related Parameters
rope_embedding_base: float = 10**6,
rope_position_scale: float = 1.0,
rope_scaling: Optional[Dict[str, Union[float, List[float], int]]] = None,
# General Model Parameters
hidden_size: int = 4096,
num_hidden_layers: int = 32,
# KV Shared Attention Configurations
num_attention_heads: int = 32,
num_key_value_heads: int = 8,
# GEGELU Related Parameters
hidden_act: str = "gegelu",
gegelu_limit: float = 20.0,
gegelu_pad_to_256: bool = True,
ff_dim_multiplier: Optional[int] = None,
ff_intermediate_size: Optional[int] = 14336,
# Block Sparse Attention Parameters
blocksparse_homo_head_pattern: bool = False,
blocksparse_block_size: int = 64,
blocksparse_num_local_blocks: int = 16,
blocksparse_vert_stride: int = 8,
blocksparse_triton_kernel_block_size: int = 64,
# Frequency of block-sparsity
dense_attention_every_n_layers: Optional[int] = 2,
# Reegularization parameters
embedding_dropout_prob: float =0.1,
attention_dropout_prob: float = 0.0,
ffn_dropout_prob: float = 0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
# MuP parameters
mup_use_scaling: bool = True,
mup_width_multiplier: bool = 8.0,
mup_embedding_multiplier: bool = 10.0,
mup_attn_multiplier: bool =1.0,
use_cache=True,
# The model does not have a bos token id
# However, in order for some of the downstream libraries to not break
# we set this to be the same as the eos_token_id
bos_token_id: int = 100257,
eos_token_id: int = 100257,
reorder_and_upcast_attn=False,
# Configuration to pad sequence length to a multiple of 64
pad_sequence_to_multiple_of_64: bool = True,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.rope_embedding_base = rope_embedding_base
self.rope_position_scale = rope_position_scale
self.rope_scaling = rope_scaling
self.hidden_size = hidden_size
# QK Shared Attention
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
# Block Sparse Attention Pattern
self.blocksparse_homo_head_pattern = blocksparse_homo_head_pattern
self.blocksparse_block_size = blocksparse_block_size
self.blocksparse_num_local_blocks = blocksparse_num_local_blocks
self.blocksparse_vert_stride = blocksparse_vert_stride
self.blocksparse_triton_kernel_block_size = blocksparse_triton_kernel_block_size
# Frequency of block sparsity
self.dense_attention_every_n_layers = dense_attention_every_n_layers
# Activation function
self.hidden_act = hidden_act
self.gegelu_limit = gegelu_limit
self.gegelu_pad_to_256 = gegelu_pad_to_256
self.ff_dim_multiplier = ff_dim_multiplier
self.ff_intermediate_size = ff_intermediate_size
if self.ff_dim_multiplier is None and self.ff_intermediate_size is None:
raise ValueError(f"Cannot have both {self.ff_dim_multiplier} and {self.ff_intermediate_size} as None")
if self.ff_dim_multiplier is not None and self.ff_intermediate_size is not None:
raise ValueError(f"Cannot specify both {self.ff_dim_multiplier} and {self.ff_intermediate_size}.")
# General regularization
self.embedding_dropout_prob = embedding_dropout_prob
self.attention_dropout_prob = attention_dropout_prob
self.ffn_dropout_prob = ffn_dropout_prob
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
# MuP parameters
self.mup_use_scaling = mup_use_scaling
self.mup_width_multiplier = mup_width_multiplier
self.mup_embedding_multiplier = mup_embedding_multiplier
self.mup_attn_multiplier = mup_attn_multiplier
self.use_cache = use_cache
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.pad_sequence_to_multiple_of_64 = pad_sequence_to_multiple_of_64
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@cached_property
def dummy_token_indices(self) -> List[int]:
# Importing here to avoid circular imports
from .tokenization_phi3_small import Phi3SmallTokenizer
tokenizer = Phi3SmallTokenizer()
return tokenizer.dummy_token_indices
@property
def intermediate_size(self) -> int:
if self.ff_intermediate_size is not None:
return self.ff_intermediate_size
intermediate_size = (self.ff_dim_multiplier) * (self.hidden_size // 3) * 2
if self.gegelu_pad_to_256:
intermediate_size = next_mult(intermediate_size, 256)
return intermediate_size
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