# Copyright 2023 The EASYDEL Author @erfanzar (Erfan Zare Chavoshi).
#
# 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
#
# https://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.
import typing as tp
from jax.sharding import PartitionSpec
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.etils import EasyDeLGradientCheckPointers
from easydel.infra.factory import register_config
[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: tp.Optional[int] = None,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
**kwargs,
) -> None:
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
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 add_jax_args(
self,
bits: tp.Optional[int] = None,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
**kwargs,
):
self.bits = bits
self.gradient_checkpointing = gradient_checkpointing
for k, v in kwargs.items():
if not hasattr(self, k):
setattr(self, k, v)
[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.
"""
return (
("embed_tokens/embedding", PartitionSpec(("fsdp", "sp"), "tp")),
("norm/kernel", PartitionSpec(("fsdp", "sp"))),
("post_attention_layernorm/kernel", PartitionSpec(("fsdp", "sp"))),
("input_layernorm/kernel", PartitionSpec(("fsdp", "sp"))),
("mlp/gate_up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/down_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/o_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/qkv_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
def _rope_scaling_validation(self):
"""Validate the `rope_scaling` configuration."""
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:
return getattr(
self,
"freq_max_position_embeddings",
self.max_position_embeddings,
)
@property
def granted_mask_max_position_embedding(self) -> int:
return getattr(
self,
"mask_max_position_embeddings",
self.max_position_embeddings,
)