# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
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from eformer.common_types import ColumnWise, Replicated, RowWise
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
from easydel.infra.utils import AttnMaskDetail, AttnMaskType
[docs]@register_config("phi3")
class Phi3Config(EasyDeLBaseConfig):
"""
Configuration objects inherit from [`EasyDeLBaseConfig`] and can be used to control the model outputs. Read
the documentation from [`EasyDeLBaseConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32064):
Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method.
hidden_size (`int`, *optional*, defaults to 3072):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 8192):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*):
Number of key and value heads for each attention layer in the Transformer encoder. Will default to
`num_attention_heads` if not set.
resid_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.0):
The dropout ratio for the embeddings.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) to use in the encoder and pooler. If string,
`"gelu"`, `"relu"`, `"swish"` and `"gelu_new"` are supported.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 2048 or 4096).
original_max_position_embeddings (`int`, *optional*, defaults to 4096):
The original maximum sequence length that this model might ever be used with. Typically set this to
something large just in case (e.g., 2048 or 4096).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-5):
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`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie the weights of the input embeddings and the output embeddings.
rope_theta (`float`, *optional*, defaults to 10000.0):
The theta value to use for rotary position embeddings.
rope_scaling (`tp.Dict[str, tp.Union[str, float]]`, *optional*):
The configuration for rope scaling.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the *beginning-of-sequence* token.
eos_token_id (`int`, *optional*, defaults to 32000):
The id of the *end-of-sequence* token.
pad_token_id (`int`, *optional*, defaults to 32000):
The index of the padding token in the vocabulary.
sliding_window (`int`, *optional*):
The sliding window size.
bits (`int`, *optional*):
The number of bits to quantize the model to.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
"""
model_type: str = "phi3"
def __init__(
self,
vocab_size=32064,
hidden_size=3072,
intermediate_size=8192,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
resid_pdrop=0.0,
embd_pdrop=0.0,
attention_dropout=0.0,
hidden_act="silu",
max_position_embeddings=4096,
original_max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
bos_token_id=1,
eos_token_id=32000,
pad_token_id=32000,
sliding_window=None,
bits: int | None = None,
layer_types: list[str] | None = None,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
**kwargs,
) -> None:
"""Initializes a Phi3Config object.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 32064.
hidden_size (int, optional): Dimensionality of the embeddings and hidden states. Defaults to 3072.
intermediate_size (int, optional): Dimensionality of the intermediate layer in MLP. Defaults to 8192.
num_hidden_layers (int, optional): Number of hidden layers. Defaults to 32.
num_attention_heads (int, optional): Number of attention heads. Defaults to 32.
num_key_value_heads (int, optional): Number of key/value heads (for GQA). Defaults to `num_attention_heads`.
resid_pdrop (float, optional): Dropout probability for residual connections. Defaults to 0.0.
embd_pdrop (float, optional): Dropout probability for embeddings. Defaults to 0.0.
attention_dropout (float, optional): Dropout probability for attention scores. Defaults to 0.0.
hidden_act (str, optional): Activation function name. Defaults to "silu".
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 4096.
original_max_position_embeddings (int, optional): Original maximum sequence length. Defaults to 4096.
initializer_range (float, optional): Standard deviation for weight initialization. Defaults to 0.02.
rms_norm_eps (float, optional): Epsilon for RMS normalization. Defaults to 1e-5.
use_cache (bool, optional): Whether to use KV cache. Defaults to True.
tie_word_embeddings (bool, optional): Whether to tie input/output embeddings. Defaults to False.
rope_theta (float, optional): Base value for RoPE. Defaults to 10000.0.
rope_scaling (dict, optional): RoPE scaling configuration. Defaults to None.
bos_token_id (int, optional): Beginning-of-sequence token ID. Defaults to 1.
eos_token_id (int, optional): End-of-sequence token ID. Defaults to 32000.
pad_token_id (int, optional): Padding token ID. Defaults to 32000.
sliding_window (int, optional): Sliding window size for attention. Defaults to None.
bits (tp.Optional[int], optional): Quantization bits. Defaults to None.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
**kwargs: Additional keyword arguments passed to the parent class.
"""
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.original_max_position_embeddings = original_max_position_embeddings
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.sliding_window = sliding_window
self.bits = bits
self.gradient_checkpointing = gradient_checkpointing
self.head_dim = hidden_size // num_attention_heads
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention" if self.sliding_window is not None else "full_attention"
for i in range(self.num_hidden_layers)
]
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
bits=bits,
**kwargs,
)
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the model.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
pmag = self.partition_manager
return (
(r"embed_tokens/embedding", pmag.resolve(ColumnWise)),
(r"self_attn/qkv_proj/kernel", pmag.resolve(ColumnWise)),
(r"self_attn/o_proj/kernel", pmag.resolve(RowWise)),
(r"self_attn/.*proj/bias", pmag.resolve(Replicated)),
(r"mlp/gate_up_proj/kernel", pmag.resolve(ColumnWise)),
(r"mlp/down_proj/kernel", pmag.resolve(RowWise)),
(r"mlp/.*proj/bias", pmag.resolve(Replicated)),
(
r".*/(input_layernorm|post_attention_layernorm|norm)/kernel",
pmag.resolve(Replicated),
),
(r"lm_head/kernel", pmag.resolve(ColumnWise)),
(r"score/kernel", pmag.resolve(RowWise)),
(r".*bias", pmag.resolve(Replicated)),
(r".*", pmag.resolve(Replicated)),
)
def _rope_scaling_validation(self):
"""Validate the `rope_scaling` configuration."""
"""Validates the `rope_scaling` configuration dictionary.
Ensures that the `rope_scaling` dictionary contains the correct keys (`type`, `factor`)
and that the values are valid (type is 'linear', 'dynamic', or 'longrope', factor is > 1.0).
It also handles backward compatibility for 'su' and 'yarn' types, mapping them to 'longrope'.
Raises:
ValueError: If `rope_scaling` is not a dictionary, is missing keys,
or has invalid values for `type` or `factor`.
"""
if self.rope_scaling is None:
return
rope_scaling_type = self.rope_scaling.get("type", None)
# For backward compatibility if previous version used "su" or "yarn"
if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
self.rope_scaling["type"] = "longrope"
@property
def granted_freq_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size specifically for frequency-based position embeddings.
If `freq_max_position_embeddings` is set, it returns that value. Otherwise, it falls back to
`max_position_embeddings`.
Returns:
int: The granted maximum position embedding size for frequency encoding.
"""
return getattr(self, "freq_max_position_embeddings", self.max_position_embeddings)
@property
def granted_mask_max_position_embedding(self) -> int:
"""Returns the maximum position embedding size specifically for mask-based position embeddings.
If `mask_max_position_embeddings` is set, it returns that value. Otherwise, it falls back to
`max_position_embeddings`.
Returns:
int: The granted maximum position embedding size for mask encoding.
"""
return getattr(self, "mask_max_position_embeddings", self.max_position_embeddings)
[docs] def get_mask_details(self) -> dict[int, AttnMaskDetail]:
"""Retrieve attention mask details for each layer in the model.
This method generates a dictionary mapping layer indices to their corresponding attention mask details.
If a sliding window is defined, each layer is assigned a sliding window attention mask with the specified size.
Returns:
dict[int, AttnMaskDetail]: A dictionary where keys are layer indices (int) and values are AttnMaskDetail
objects specifying the attention mask type and size for each layer.
Notes:
- If `self.sliding_window` is None, an empty dictionary is returned.
- The method iterates over `self.num_hidden_layers` to assign mask details for each layer.
- The attention mask type is set to `AttnMaskType.SLIDING` when a sliding window is defined.
"""
mapping = {}
if self.layer_types is not None:
for layer_idx in range(self.num_hidden_layers):
mapping[layer_idx] = AttnMaskDetail(
mask_type=AttnMaskType.from_hf(self.layer_types[layer_idx]),
size=self.sliding_window,
)
return mapping