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configuration_spec_vision.py

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+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. 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.
+
+""" Spec-Vision model configuration"""
+
+from typing import Dict, Optional, Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class SpecVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SpecVisionModel`]. It is used to instantiate a Spec-Vision
+    model according to the specified arguments, defining the model architecture.
+
+    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 32064):
+            Vocabulary size of the model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`SpecVisionModel`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            Number of key/value heads for implementing Grouped Query Attention.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for MLP outputs.
+        embd_pdrop (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing attention scores.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-5):
+            The epsilon value used for RMSNorm.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether to use the past key/values attentions for faster inference.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            Configuration for RoPE scaling strategy.
+        embd_layer (`dict`, *optional*):
+            Configuration for the embedding layer, including image embedding settings.
+    """
+    model_type = "spec_vision"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size: int = 32064,
+        hidden_size: int = 3072,
+        intermediate_size: int = 8192,
+        num_hidden_layers: int = 32,
+        num_attention_heads: int = 32,
+        num_key_value_heads: Optional[int] = None,
+        resid_pdrop: float = 0.0,
+        embd_pdrop: float = 0.0,
+        attention_dropout: float = 0.0,
+        hidden_act: str = "silu",
+        max_position_embeddings: int = 4096,
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-5,
+        use_cache: bool = True,
+        rope_theta: float = 10000.0,
+        rope_scaling: Optional[Dict] = None,
+        embd_layer: Dict[str, Union[str, bool]] = {
+            "embedding_cls": "image",
+            "hd_transform_order": "sub_glb",
+            "projection_cls": "mlp",
+            "use_hd_transform": True,
+            "with_learnable_separator": True
+        },
+        bos_token_id: int = 1,
+        eos_token_id: int = 32000,
+        pad_token_id: int = 32000,
+        tie_word_embeddings: bool = False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        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_key_value_heads = num_key_value_heads or num_attention_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.embd_layer = embd_layer
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        
+        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
+            
+        head_dim = self.hidden_size // self.num_attention_heads // 2
+        
+        for factor, name in [(rope_scaling_short_factor, "short_factor"), (rope_scaling_long_factor, "long_factor")]:
+            if not (isinstance(factor, list) and all(isinstance(x, (int, float)) for x in factor)):
+                raise ValueError(f"`rope_scaling`'s {name} field must be a list of numbers, got {factor}")
+            if len(factor) != head_dim:
+                raise ValueError(f"`rope_scaling`'s {name} field must have length {head_dim}, got {len(factor)}")