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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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
#
# http://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 transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
class PaddleOCRVisionConfig(PretrainedConfig):
model_type = "paddleocr_vl"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
num_channels=3,
image_size=224,
patch_size=14,
hidden_act="gelu_pytorch_tanh",
layer_norm_eps=1e-6,
attention_dropout=0.0,
spatial_merge_size=2,
temporal_patch_size=2,
tokens_per_second=2,
**kwargs,
):
super().__init__(**kwargs)
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.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.tokens_per_second = tokens_per_second
class PaddleOCRVLConfig(PretrainedConfig):
"""
Configuration class.
This class stores the configuration of an Ernie model, defining the model architecture.
It inherits from PretrainedConfig and can be used to control model outputs.
"""
model_type = "paddleocr_vl"
keys_to_ignore_at_inference = ["past_key_values"]
sub_configs = {"vision_config": PaddleOCRVisionConfig}
# Default tensor parallel plan for base model `Qwen3`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=32000,
hidden_size=768,
intermediate_size=11008,
max_position_embeddings=32768,
num_hidden_layers=2,
num_attention_heads=2,
image_token_id=101304,
video_token_id=101305,
vision_start_token_id=101306,
rms_norm_eps=1e-6,
use_cache=False,
use_flash_attention=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
head_dim=128,
hidden_act="silu",
use_bias=False,
rope_theta=10000,
weight_share_add_bias=True,
ignored_index=-100,
attention_probs_dropout_prob=0.0,
hidden_dropout_prob=0.0,
compression_ratio: float = 1.0,
num_key_value_heads=None,
max_sequence_length=None,
tie_word_embeddings=False,
vision_config=None,
rope_scaling=None,
**kwargs,
):
"""
Initialize configuration with default or specified parameters.
Args:
vocab_size (int): Size of the vocabulary (number of unique tokens)
hidden_size (int): Dimensionality of the encoder layers and the pooler layer
intermediate_size (int): Dimensionality of the "intermediate" (feed-forward) layer
max_position_embeddings (int): Maximum sequence length the model can handle
num_hidden_layers (int): Number of hidden layers in the Transformer encoder
num_attention_heads (int): Number of attention heads for each attention layer
rms_norm_eps (float): The epsilon used by the RMS normalization layers
use_cache (bool): Whether to use caching for faster generation (decoding)
use_flash_attention (bool): Whether to use FlashAttention for optimized attention computation
pad_token_id (int): Token ID used for padding sequences
bos_token_id (int): Token ID used for beginning-of-sequence
eos_token_id (int): Token ID used for end-of-sequence
use_bias (bool): Whether to use bias terms in linear layers
rope_theta (float): The base period of the RoPE embeddings
weight_share_add_bias (bool): Whether to share bias weights in certain layers
ignored_index (int): Target value that is ignored during loss computation
attention_probs_dropout_prob (float): Dropout probability for attention weights
hidden_dropout_prob (float): Dropout probability for hidden layers
compression_ratio (float): Ratio for KV cache compression (1.0 = no compression)
num_key_value_heads (int): Number of key/value heads (for Grouped Query Attention)
max_sequence_length (int): Maximum sequence length for positional embeddings
**kwargs: Additional keyword arguments passed to parent class
"""
# Set default for tied embeddings if not specified.
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
if isinstance(vision_config, dict):
self.vision_config = self.sub_configs["vision_config"](**vision_config)
elif vision_config is None:
self.vision_config = self.sub_configs["vision_config"]()
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.max_position_embeddings = max_position_embeddings
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.use_flash_attention = use_flash_attention
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.image_token_id = image_token_id
self.video_token_id = video_token_id
self.vision_start_token_id = vision_start_token_id
self.head_dim = head_dim
self.hidden_act=hidden_act
self.sliding_window = None
self.hidden_size = hidden_size
self.use_bias = use_bias
self.weight_share_add_bias = weight_share_add_bias
self.rope_theta = rope_theta
self.ignored_index = ignored_index
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.hidden_dropout_prob = hidden_dropout_prob
self.compression_ratio = compression_ratio
self.num_key_value_heads = num_key_value_heads
self.max_sequence_length = max_sequence_length
self.rope_scaling = rope_scaling
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"]
rope_config_validation(self, ignore_keys={"mrope_section"})
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) |