327 lines
16 KiB
Python
327 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Idefics model configuration"""
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import copy
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from transformers import PretrainedConfig
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IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"HuggingFaceM4/idefics-9b": "https://huggingface.co/HuggingFaceM4/idefics-9b/blob/main/config.json",
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"HuggingFaceM4/idefics-80b": "https://huggingface.co/HuggingFaceM4/idefics-80b/blob/main/config.json",
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}
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class IdeficsVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
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Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Idefics-9B.
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e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `hidden_size`)
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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intermediate_size (`int`, *optional*, defaults to 5120):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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image_num_channels (`int`, *optional*, defaults to `3`):
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Number of image channels.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
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testing).
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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"""
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model_type = "idefics"
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attribute_map = {
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"hidden_size": "embed_dim",
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}
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def __init__(
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self,
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embed_dim=768,
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image_size=224,
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intermediate_size=5120,
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patch_size=14,
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num_hidden_layers=32,
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num_attention_heads=16,
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num_channels=3,
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hidden_act="gelu",
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layer_norm_eps=1e-5,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=1.0,
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**kwargs,
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):
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self.embed_dim = embed_dim
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self.image_size = image_size
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self.intermediate_size = intermediate_size
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self.patch_size = patch_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.layer_norm_eps = layer_norm_eps
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.hidden_act = hidden_act
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super().__init__(**kwargs)
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class IdeficsPerceiverConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
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Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Idefics-9B.
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e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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use_resampler (`bool`, *optional*, defaults to `False`):
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Whether or not to use the resampler
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resampler_n_latents (`int`, *optional*, defaults to ):
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Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
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resampler_depth (`int`, *optional*, defaults to 6):
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Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
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resampler_n_heads (`int`, *optional*, defaults to 16):
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Number of heads in each Transformer block (for multi-headed self-attention).
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resampler_head_dim (`int`, *optional*, defaults to 96):
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Dimensionality of each head projection in the Transformer block.
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qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`):
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Whether or not to use qk layer norms in perceiver
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"""
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model_type = "idefics"
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def __init__(
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self,
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use_resampler=False,
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resampler_n_latents=64,
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resampler_depth=6,
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resampler_n_heads=16,
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resampler_head_dim=96,
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qk_layer_norms_perceiver=False,
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**kwargs,
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):
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self.use_resampler = use_resampler
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self.resampler_n_latents = resampler_n_latents
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self.resampler_depth = resampler_depth
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self.resampler_n_heads = resampler_n_heads
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self.resampler_head_dim = resampler_head_dim
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self.qk_layer_norms_perceiver = qk_layer_norms_perceiver
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super().__init__(**kwargs)
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class IdeficsConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
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Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Idefics-9B.
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e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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additional_vocab_size (`int`, *optional`, defaults to 0):
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Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens
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are always trainable whereas regular vocab tokens can be frozen or not.
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`~IdeficsModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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alpha_initializer (`str`, *optional*, defaults to `"zeros"`):
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Initialization type for the alphas.
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alphas_initializer_range (`float`, *optional*, defaults to 0.0):
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The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
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Attention.
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alpha_type (`str`, *optional*, defaults to `"float"`):
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Whether the gating alphas should be vectors or single floats.
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rms_norm_eps (`float`, *optional*, defaults to 1e-6):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*, defaults to 0)
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1)
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2)
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End of stream token id.
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tie_word_embeddings(`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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cross_layer_interval (`int`, *optional*, default to 1)
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Interval for cross attention (from text to image) layers.
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qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k
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freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers
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freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`):
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Exceptions to freezing text layers when `freeze_text_layers` is `True`
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freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head
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freeze_vision_layers (`bool`, *optional*, defaults to `True`): Whether to freeze vision layers
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freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`):
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Exceptions to freezing vision layers when `freeze_vision_layers` is `True`
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use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler
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vision_config (`IdeficsVisionConfig`, *optional*): Custom vision config or dict
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perceiver_config (`IdeficsPerceiverConfig`, *optional*): Custom perceiver config or dict
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Example:
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```python
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>>> from transformers import IdeficsModel, IdeficsConfig
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>>> # Initializing a Idefics idefics-9b style configuration
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>>> configuration = IdeficsConfig()
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>>> # Initializing a model from the idefics-9b style configuration
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>>> model = IdeficsModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "idefics"
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is_composition = True
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def __init__(
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self,
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vocab_size=32000,
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additional_vocab_size=0,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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dropout=0.0,
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hidden_act="silu",
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initializer_range=0.02,
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alpha_initializer="zeros",
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alphas_initializer_range=0.0,
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alpha_type="float",
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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cross_layer_interval=1,
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qk_layer_norms=False,
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freeze_text_layers=True,
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freeze_text_module_exceptions=[],
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freeze_lm_head=False,
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freeze_vision_layers=True,
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freeze_vision_module_exceptions=[],
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use_resampler=False,
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vision_config=None,
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perceiver_config=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.additional_vocab_size = additional_vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.dropout = dropout
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.alpha_initializer = alpha_initializer
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self.alphas_initializer_range = alphas_initializer_range
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self.alpha_type = alpha_type
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.cross_layer_interval = cross_layer_interval
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self.qk_layer_norms = qk_layer_norms
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self.freeze_vision_layers = freeze_vision_layers
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self.freeze_text_layers = freeze_text_layers
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self.freeze_text_module_exceptions = freeze_text_module_exceptions
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self.freeze_vision_module_exceptions = freeze_vision_module_exceptions
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self.freeze_lm_head = freeze_lm_head
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self.use_resampler = use_resampler
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if perceiver_config is None:
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self.perceiver_config = IdeficsPerceiverConfig()
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elif isinstance(perceiver_config, dict):
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self.perceiver_config = IdeficsPerceiverConfig(**perceiver_config)
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elif isinstance(perceiver_config, IdeficsPerceiverConfig):
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self.perceiver_config = perceiver_config
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if vision_config is None:
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self.vision_config = IdeficsVisionConfig()
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elif isinstance(vision_config, dict):
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self.vision_config = IdeficsVisionConfig(**vision_config)
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elif isinstance(vision_config, IdeficsVisionConfig):
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self.vision_config = vision_config
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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# IMPORTANT: Do not do any __init__ args-based checks in the constructor, since
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# PretrainedConfig.from_dict first instantiates the class with the config dict and only then
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# updates the config object with `kwargs` from from_pretrained, so during the instantiation
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# of this object many attributes have default values and haven't yet been overridden.
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# Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run.
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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
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Returns:
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
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"""
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output = copy.deepcopy(self.__dict__)
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output["vision_config"] = self.vision_config.to_dict()
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output["perceiver_config"] = self.perceiver_config.to_dict()
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output["model_type"] = self.__class__.model_type
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return output
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