# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
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import typing as tp
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("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: int | None = None,
scan_layers: bool = True,
layer_types: list[str] | None = None,
rope_scaling: tp.Mapping[str, str | float] | None = None,
**kwargs,
):
"""Initializes a Qwen2Config object.
Args:
vocab_size (int, optional): Vocabulary size. Defaults to 151936.
hidden_size (int, optional): Dimensionality of the embeddings and hidden states. Defaults to 4096.
intermediate_size (int, optional): Dimensionality of the intermediate layer in MLP. Defaults to 22016.
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 32.
hidden_act (str, optional): Activation function name. Defaults to "silu".
max_position_embeddings (int, optional): Maximum sequence length. Defaults to 32768.
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-6.
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.
use_sliding_window (bool, optional): Whether to use sliding window attention. Defaults to False.
sliding_window (int, optional): Sliding window size. Defaults to 4096.
max_window_layers (int, optional): Maximum number of layers for sliding window attention. Defaults to 28.
attention_dropout (float, optional): Dropout probability for attention scores. Defaults to 0.0.
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.
gradient_checkpointing (EasyDeLGradientCheckPointers, optional): Gradient checkpointing strategy.
Defaults to EasyDeLGradientCheckPointers.NONE.
fcm_min_ratio (float, optional): Minimum ratio for Flash Attention. Defaults to 0.0.
fcm_max_ratio (float, optional): Maximum ratio for Flash Attention. Defaults to 0.0.
use_scan_mlp (bool, optional): Whether to use scan for MLP layers. Defaults to False.
scan_mlp_chunk_size (int, optional): Chunk size for scan MLP. Defaults to 1024.
number_rep_kv (int, optional): Number of repetitions for key/value vectors. Defaults to 1.
bits (tp.Optional[int], optional): Quantization bits. Defaults to None.
scan_layers (bool, optional): Whether to use scan for transformer layers. Defaults to True.
rope_scaling (tp.Optional[tp.Mapping[str, str | float]], optional):
RoPE scaling configuration. Defaults to None.
**kwargs: Additional keyword arguments passed to the parent class.
"""
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
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
for i in range(self.num_hidden_layers)
]
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, *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/(q_proj|k_proj|v_proj)/kernel", pmag.resolve(ColumnWise)),
(r"self_attn/o_proj/kernel", pmag.resolve(RowWise)),
(r"self_attn/(q_proj|k_proj|v_proj)/bias", pmag.resolve(Replicated)),
(r"self_attn/o_proj/bias", pmag.resolve(Replicated)),
(r"mlp/(gate_proj|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)),
)
[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