# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
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# Licensed under the Apache License, Version 2.0 (the "License");
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# https://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import typing
from eformer.common_types import ColumnWise, Replicated, RowWise
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.utils import AttnMaskDetail, AttnMaskType
[docs]class Qwen2VLVisionConfig(EasyDeLBaseConfig):
"""
Configuration class for the vision component of Qwen2VL model.
This class stores the configuration parameters for the vision encoder part of the Qwen2VL multimodal model.
Args:
depth (`int`, *optional*, defaults to 32):
Number of layers in the vision transformer.
embed_dim (`int`, *optional*, defaults to 1280):
Dimensionality of the embeddings produced by the vision encoder.
hidden_size (`int`, *optional*, defaults to 3584):
Dimensionality of the intermediate representations in the vision transformer.
hidden_act (`str`, *optional*, defaults to "quick_gelu"):
The non-linear activation function used in the vision transformer.
mlp_ratio (`int`, *optional*, defaults to 4):
Ratio of the hidden size to the intermediate size in the MLP layers.
num_heads (`int`, *optional*, defaults to 16):
Number of attention heads in the vision transformer.
in_channels (`int`, *optional*, defaults to 3):
Number of input channels for the image (typically 3 for RGB).
patch_size (`int`, *optional*, defaults to 14):
Size of the patches that the image is divided into.
spatial_merge_size (`int`, *optional*, defaults to 2):
The merge size for spatial dimensions in the vision transformer.
temporal_patch_size (`int`, *optional*, defaults to 2):
Size of the temporal patches when processing video input.
"""
model_type = "qwen2_vl"
base_config_key = "vision_config"
def __init__(
self,
depth=32,
embed_dim=1280,
hidden_size=3584,
hidden_act="quick_gelu",
mlp_ratio=4,
num_heads=16,
in_channels=3,
patch_size=14,
spatial_merge_size=2,
temporal_patch_size=2,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.embed_dim = embed_dim
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.mlp_ratio = mlp_ratio
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.initializer_range = initializer_range
[docs]class Qwen2VLTextConfig(EasyDeLBaseConfig):
"""Configuration for the Qwen2-VL text decoder stack."""
model_type = "qwen2_vl_text"
base_config_key = "text_config"
keys_to_ignore_at_inference: typing.ClassVar = ["past_key_values"]
def __init__(
self,
vocab_size: int = 152064,
hidden_size: int = 8192,
intermediate_size: int = 29568,
num_hidden_layers: int = 80,
num_attention_heads: int = 64,
num_key_value_heads: int | None = None,
hidden_act: str = "silu",
max_position_embeddings: int = 32768,
initializer_range: float = 0.02,
rms_norm_eps: float = 1e-05,
use_cache: bool = True,
tie_word_embeddings: bool = False,
rope_theta: float = 1000000.0,
use_sliding_window: bool = False,
sliding_window: int = 4096,
max_window_layers: int = 80,
attention_dropout: float = 0.0,
rope_scaling: dict | None = None,
rope_parameters: dict | None = None,
layer_types: list[str] | None = None,
**kwargs,
):
super().__init__(**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 if self.use_sliding_window else None
self.max_window_layers = max_window_layers
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
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.attention_dropout = attention_dropout
self.tie_word_embeddings = tie_word_embeddings
# Rope scaling / parameters compatibility with HF naming
self.rope_scaling = rope_scaling or rope_parameters
if self.rope_scaling is not None and "type" in self.rope_scaling:
if self.rope_scaling["type"] == "mrope":
self.rope_scaling["type"] = "default"
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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)
]
[docs]class Qwen2VLConfig(EasyDeLBaseConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2VLModel`]. It is used to instantiate a
Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
Args:
text_config (`Union[Qwen2VLTextConfig, dict]`, *optional*):
The config for the text decoder.
vision_config (`Union[Qwen2VLVisionConfig, dict]`, *optional*):
The config for the vision encoder.
image_token_id (`int`, *optional*, defaults to 151655):
The image token index to encode image prompts.
video_token_id (`int`, *optional*, defaults to 151656):
The video token index to encode video prompts.
vision_start_token_id (`int`, *optional*, defaults to 151652):
The token index to denote start of vision input.
vision_end_token_id (`int`, *optional*, defaults to 151653):
The token index to denote end of vision input.
"""
model_type = "qwen2_vl"
sub_configs: typing.ClassVar = {
"vision_config": Qwen2VLVisionConfig,
"text_config": Qwen2VLTextConfig,
}
keys_to_ignore_at_inference: typing.ClassVar = ["past_key_values"]
def __init__(
self,
text_config: typing.Mapping[str, typing.Any] | Qwen2VLTextConfig | None = None,
vision_config: typing.Mapping[str, typing.Any] | Qwen2VLVisionConfig | None = None,
image_token_id: int = 151655,
video_token_id: int = 151656,
vision_start_token_id: int = 151652,
vision_end_token_id: int = 151653,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {}
if isinstance(vision_config, dict):
self.vision_config = Qwen2VLVisionConfig(**vision_config)
elif isinstance(vision_config, Qwen2VLVisionConfig):
self.vision_config = vision_config
else:
raise ValueError("vision_config must be a dict or Qwen2VLVisionConfig.")
if text_config is None:
text_config = {}
if isinstance(text_config, dict):
self.text_config = Qwen2VLTextConfig(**text_config)
elif isinstance(text_config, Qwen2VLTextConfig):
self.text_config = text_config
else:
raise ValueError("text_config must be a dict or Qwen2VLTextConfig.")
self.image_token_id = image_token_id
self.video_token_id = video_token_id
self.vision_start_token_id = vision_start_token_id
self.vision_end_token_id = vision_end_token_id
# Sync fields for convenience access on self
self._sync_text_config_fields()
def _sync_text_config_fields(self):
"""Expose text config fields on the top-level config for compatibility."""
self.vocab_size = self.text_config.vocab_size
self.max_position_embeddings = self.text_config.max_position_embeddings
self.hidden_size = self.text_config.hidden_size
self.intermediate_size = self.text_config.intermediate_size
self.num_hidden_layers = self.text_config.num_hidden_layers
self.num_attention_heads = self.text_config.num_attention_heads
self.num_key_value_heads = self.text_config.num_key_value_heads
self.hidden_act = self.text_config.hidden_act
self.initializer_range = self.text_config.initializer_range
self.rms_norm_eps = self.text_config.rms_norm_eps
self.use_cache = self.text_config.use_cache
self.rope_theta = self.text_config.rope_theta
self.attention_dropout = self.text_config.attention_dropout
self.rope_scaling = self.text_config.rope_scaling
self.head_dim = self.text_config.head_dim
self.layer_types = self.text_config.layer_types
self.use_sliding_window = self.text_config.use_sliding_window
self.sliding_window = self.text_config.sliding_window
self.max_window_layers = self.text_config.max_window_layers
[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/.*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)/kernel",
pmag.resolve(Replicated),
),
(r"norm/kernel", pmag.resolve(Replicated)),
(r"visual/patch_embed/proj/kernel", pmag.resolve(ColumnWise)), #
(r"attn/qkv/kernel", pmag.resolve(ColumnWise)),
(r"attn/qkv/bias", pmag.resolve(Replicated)),
(r"attn/proj/kernel", pmag.resolve(RowWise)),
(r"attn/proj/bias", pmag.resolve(Replicated)),
(r"mlp/fc1/kernel", pmag.resolve(ColumnWise)),
(r"mlp/fc1/bias", pmag.resolve(Replicated)),
(r"mlp/fc2/kernel", pmag.resolve(RowWise)),
(r"mlp/fc2/bias", pmag.resolve(Replicated)),
(r"norm(1|2)/scale", pmag.resolve(Replicated)),
(r"norm(1|2)/bias", pmag.resolve(Replicated)),
(r"visual/merger/ln_q/scale", pmag.resolve(Replicated)),
(r"visual/merger/ln_q/bias", pmag.resolve(Replicated)),
(r"visual/merger/mlp/0/kernel", pmag.resolve(ColumnWise)),
(r"visual/merger/mlp/0/bias", pmag.resolve(Replicated)),
(r"visual/merger/mlp/2/kernel", pmag.resolve(RowWise)),
(r"visual/merger/mlp/2/bias", pmag.resolve(Replicated)),
(r"multi_modal_projector/linear_1/kernel", pmag.resolve(ColumnWise)),
(r"multi_modal_projector/linear_1/bias", pmag.resolve(Replicated)),
(r"lm_head/kernel", pmag.resolve(ColumnWise)),
(r"lm_head/bias", pmag.resolve(Replicated)),
(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 = {}
for layer_idx in range(self.num_hidden_layers):
if self.sliding_window is not None and self.use_sliding_window:
mapping[layer_idx] = AttnMaskDetail(mask_type=AttnMaskType.SLIDING, size=self.sliding_window)
return mapping