# 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.
from jax.sharding import PartitionSpec
from easydel.infra.base_module import EasyDeLBaseConfig
[docs]class Qwen2VLVisionConfig(EasyDeLBaseConfig):
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,
**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
[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).
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 152064):
Vocabulary size of the Qwen2VL model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2VLModel`]
hidden_size (`int`, *optional*, defaults to 8192):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 29568):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 80):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
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-05):
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 the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 80):
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
vision_config (`tp.Dict`, *optional*):
The config for the visual encoder initialization.
rope_scaling (`tp.Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`tp.List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`tp.List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
```python
>>> from transformers import Qwen2VLForConditionalGeneration, Qwen2VLConfig
>>> # Initializing a Qwen2VL style configuration
>>> configuration = Qwen2VLConfig()
>>> # Initializing a model from the Qwen2-VL-7B style configuration
>>> model = Qwen2VLForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_vl"
sub_configs = {"vision_config": Qwen2VLVisionConfig}
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=152064,
hidden_size=8192,
intermediate_size=29568,
num_hidden_layers=80,
num_attention_heads=64,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-05,
use_cache=True,
tie_word_embeddings=False,
rope_theta=1000000.0,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=80,
attention_dropout=0.0,
vision_config=None,
rope_scaling=None,
vision_start_token_id=151652,
vision_end_token_id=151653,
vision_token_id=151654,
image_token_id=151655,
video_token_id=151656,
**kwargs,
):
if isinstance(vision_config, dict):
self.vision_config = Qwen2VLVisionConfig(**vision_config)
elif vision_config is None:
self.vision_config = Qwen2VLVisionConfig()
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.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.rope_scaling = rope_scaling
# EasyDeL Extended args.
self.head_dim = hidden_size // num_attention_heads
self.vision_start_token_id = vision_start_token_id
self.vision_end_token_id = vision_end_token_id
self.vision_token_id = vision_token_id
self.image_token_id = image_token_id
self.video_token_id = video_token_id
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"]
super().__init__(tie_word_embeddings=tie_word_embeddings, **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.
"""
return (
# Language model embeddings
("embed_tokens/embedding", PartitionSpec("tp", ("fsdp", "sp"))),
# Language model attention layers
(
"layers/.*/self_attn/(q_proj|k_proj|v_proj)/kernel",
PartitionSpec(("fsdp", "sp"), "tp"),
),
("layers/.*/self_attn/o_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("layers/.*/self_attn/(q_proj|k_proj|v_proj)/bias", PartitionSpec("tp")),
("layers/.*/self_attn/o_proj/bias", PartitionSpec(None)),
# Language model MLP layers
("layers/.*/mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("layers/.*/mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("layers/.*/mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("layers/.*/mlp/(gate_proj|down_proj|up_proj)/bias", PartitionSpec(None)),
# Language model norms
("layers/.*/input_layernorm/kernel", PartitionSpec(None)),
("layers/.*/post_attention_layernorm/kernel", PartitionSpec(None)),
("norm/kernel", PartitionSpec(None)),
# Language model head
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("lm_head/bias", PartitionSpec(None)),
# Visual model patch embedding
("patch_embed/proj/kernel", PartitionSpec(None, None, None, None, "tp")),
("patch_embed/proj/bias", PartitionSpec(None)),
# Visual model attention blocks
("blocks/.*/attn/qkv/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("blocks/.*/attn/qkv/bias", PartitionSpec("tp")),
("blocks/.*/attn/proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("blocks/.*/attn/proj/bias", PartitionSpec("tp")),
# Visual model MLP blocks
("blocks/.*/mlp/fc1/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("blocks/.*/mlp/fc1/bias", PartitionSpec("tp")),
("blocks/.*/mlp/fc2/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("blocks/.*/mlp/fc2/bias", PartitionSpec("tp")),
# Visual model norms
("blocks/.*/norm1/(bias|scale)", PartitionSpec(None)),
("blocks/.*/norm2/(bias|scale)", PartitionSpec(None)),
# Visual model merger
("merger/ln_q/(bias|scale)", PartitionSpec(None)),
("merger/mlp/.*/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("merger/mlp/.*/bias", PartitionSpec("tp")),
# Catch-all for any remaining parameters
(".*", PartitionSpec(None)),
)