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# coding=utf-8
# Copyright 2025 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.
""" Phi4Flash model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
import math
logger = logging.get_logger(__name__)
class Phi4FlashConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Phi4FlashModel`]. It is used to instantiate an Phi4Flash
model according to the specified arguments, defining the model 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 51200):
Vocabulary size of the Phi4Flash model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Phi4FlashModel`].
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.
Example:
```python
>>> from transformers import Phi4FlashModel, Phi4FlashConfig
>>> # Initializing a Phi4Flash style configuration
>>> configuration = Phi4FlashConfig.from_pretrained("microsoft/Phi4-mini-flash-reasoning")
>>> # Initializing a model from the configuration
>>> model = Phi4FlashModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "phi4flash"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=51200,
hidden_size=2560,
intermediate_size=9216,
num_hidden_layers=32,
num_attention_heads=40,
num_key_value_heads=4,
resid_pdrop=0.0,
embd_pdrop=0.0,
attention_dropout=0.0,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=True,
rope_theta=10000.0,
bos_token_id=1,
eos_token_id=2,
sliding_window=2047,
mb_per_layer= 2,
mamba_d_state=16,
mamba_d_conv=4,
mamba_expand=2,
mamba_dt_rank="auto",
mamba_conv_bias=True,
mamba_proj_bias=False,
**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.mb_per_layer = mb_per_layer
self.sliding_window = [
sliding_window if layer_idx < num_hidden_layers // 2 and layer_idx % 2 == 1 else None
for layer_idx in range(num_hidden_layers)
]
self.mamba_d_state = mamba_d_state
self.mamba_d_conv = mamba_d_conv
self.mamba_expand = mamba_expand
self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank
self.mamba_conv_bias = mamba_conv_bias
self.mamba_proj_bias = mamba_proj_bias
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
@property
def layers_block_type(self):
layer_block_types = []
for i in range(self.num_hidden_layers):
if i % 2 == 1:
layer_block_type = "attention" if i <= (self.num_hidden_layers //2 +1) else "shared_attention"
else:
layer_block_type = "mamba"
layer_block_types.append(layer_block_type)
return layer_block_types |