# 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("qwen2")
class Qwen2Config(EasyDeLBaseConfig):
"""
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 151936):
Vocabulary size of the Qwen-2 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 4096):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 22016):
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*, defaults to 32):
Number of key and value heads for each attention layer in the Transformer encoder.
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 32768):
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).
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-6):
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.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use a sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
The sliding window size.
max_window_layers (`int`, *optional*, defaults to 28):
The maximum number of layers to use for the sliding window attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
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.
gradient_checkpointing (`str`, *optional*, defaults to `"nothing_saveable"`):
The gradient checkpointing configuration.
fcm_min_ratio (`float`, *optional*, defaults to 0.0):
The minimum ratio for Flash Attention.
fcm_max_ratio (`float`, *optional*, defaults to 0.0):
The maximum ratio for Flash Attention.
use_scan_mlp (`bool`, *optional*, defaults to `False`):
Whether to use the scan implementation for the MLP.
scan_mlp_chunk_size (`int`, *optional*, defaults to 1024):
The chunk size to use when scanning the MLP.
number_rep_kv (`int`, *optional*, defaults to 1):
Number of repetitions for the key and value vectors.
bits (`int`, *optional*):
The number of bits to quantize the model to.
scan_layers (`bool`, *optional*, defaults to `True`):
Whether to use the scan implementation for the layers.
rope_scaling (`tp.Dict[str, tp.Union[str, float]]`, *optional*):
The configuration for rope scaling.
"""
model_type: str = "qwen2"
def __init__(
self,
vocab_size=151936,
hidden_size=4096,
intermediate_size=22016,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=28,
attention_dropout=0.0,
resid_pdrop: float = 0.0,
embd_pdrop: float = 0.0,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
fcm_min_ratio: float = 0.0,
fcm_max_ratio: float = 0.0,
use_scan_mlp: bool = False,
scan_mlp_chunk_size: int = 1024,
number_rep_kv: int = 1,
bits: tp.Optional[int] = None,
scan_layers: bool = True,
rope_scaling: tp.Optional[tp.Mapping[str, str | float]] = None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.rope_scaling = rope_scaling
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.scan_layers = scan_layers
self.embd_pdrop = embd_pdrop
self.number_rep_kv = number_rep_kv
self.resid_pdrop = resid_pdrop
self.attention_dropout = attention_dropout
self.tie_word_embeddings = tie_word_embeddings
self.gradient_checkpointing = gradient_checkpointing
self.fcm_min_ratio = fcm_min_ratio
self.fcm_max_ratio = fcm_max_ratio
self.use_scan_mlp = use_scan_mlp
self.scan_mlp_chunk_size = scan_mlp_chunk_size
self.bits = bits
self.head_dim = hidden_size // num_attention_heads
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
super().__init__(
tie_word_embeddings=tie_word_embeddings,
use_scan_mlp=use_scan_mlp,
scan_mlp_chunk_size=scan_mlp_chunk_size,
bits=bits,
**kwargs,
)
[docs] def get_partition_rules(self, fully_sharded_data_parallel: bool = True):
"""
Get the partition rules for the model.
Args:
fully_sharded_data_parallel (`bool`, *optional*, defaults to `True`):
Whether to use fully sharded data parallelism.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
return (
("model/embed_tokens/embedding", PartitionSpec("tp", ("fsdp", "sp"))),
(
"self_attn/(q_proj|k_proj|v_proj)/kernel",
PartitionSpec(("fsdp", "sp"), "tp"),
),
("self_attn/o_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
)
[docs] def add_jax_args(
self,
resid_pdrop: float = 0.0,
embd_pdrop: float = 0.0,
attention_dropout: float = 0.0,
tie_word_embeddings: bool = False,
gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE,
fcm_min_ratio: float = 0.0,
fcm_max_ratio: float = 0.0,
use_scan_mlp: bool = False,
scan_mlp_chunk_size: int = 1024,
number_rep_kv: int = 1,
bits: tp.Optional[int] = None,
rope_theta: float = 10000.0,
hidden_act: str = "silu",
scan_layers: bool = True,
rope_scaling: tp.Optional[tp.Mapping[str, str | float]] = None,
**kwargs,
):
"""The add_jax_args function adds the following arguments to the Transformer class:
Args:
self: Refer to the current object
resid_pdrop: float: Set the dropout rate for residual
connections
embd_pdrop: float: Set the probability of dropping an
embedding
attention_dropout: float: Set the probability of dropping
out the attention layer
tie_word_embeddings: bool: Tie the word embeddings to the
decoder
gradient_checkpointing: str: Control the amount of memory
used by jax
fcm_min_ratio: float: Control the minimum ratio of the
number of chunks to be used in flash-based computation
fcm_max_ratio: float: Set the maximum ratio of the number of
input tokens to output tokens
use_scan_mlp: bool: Determine whether to use the scan_mlp
function or not
scan_mlp_chunk_size: int: Set the chunk size for scan_mlp
number_rep_kv: int: Determine how many times the key and
value vectors are repeated
bits: tp.Optional[int]: Determine the number of bits used in
the quantization
rope_theta: float : rope_theta for compute rope
hidden_act: str : hidden_act for mlp
scan_layers: bool: Determine whether to use scan layers or
not
Returns:
The following:
"""
self.head_dim = self.hidden_size // self.num_attention_heads
self.scan_layers = scan_layers
self.embd_pdrop = embd_pdrop
self.number_rep_kv = number_rep_kv
self.resid_pdrop = resid_pdrop
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_dropout = attention_dropout
self.hidden_act = hidden_act
self.tie_word_embeddings = tie_word_embeddings
self.gradient_checkpointing = gradient_checkpointing
self.fcm_min_ratio = fcm_min_ratio
self.fcm_max_ratio = fcm_max_ratio
self.use_scan_mlp = use_scan_mlp
self.scan_mlp_chunk_size = scan_mlp_chunk_size
self.bits = bits
[docs] @staticmethod
def get_weight_decay_exclusions():
return tuple()
[docs] @staticmethod
def rng_keys():
return "params", "dropout", "fcm"
@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,
)