245 lines
12 KiB
Python
245 lines
12 KiB
Python
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# coding=utf-8
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# Copyright Deepmind and The HuggingFace Inc. team. All rights reserved.
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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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""" Perceiver model configuration"""
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from collections import OrderedDict
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from typing import Any, Mapping, Optional, Union
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from ...configuration_utils import PretrainedConfig
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from ...feature_extraction_utils import FeatureExtractionMixin
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from ...onnx import OnnxConfig
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from ...onnx.utils import compute_effective_axis_dimension
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from ...tokenization_utils_base import PreTrainedTokenizerBase
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from ...utils import TensorType, logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class PerceiverConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PerceiverModel`]. It is used to instantiate an
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Perceiver model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the Perceiver
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[deepmind/language-perceiver](https://huggingface.co/deepmind/language-perceiver) architecture.
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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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num_latents (`int`, *optional*, defaults to 256):
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The number of latents.
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d_latents (`int`, *optional*, defaults to 1280):
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Dimension of the latent embeddings.
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d_model (`int`, *optional*, defaults to 768):
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Dimension of the inputs. Should only be provided in case [*PerceiverTextPreprocessor*] is used or no
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preprocessor is provided.
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num_blocks (`int`, *optional*, defaults to 1):
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Number of blocks in the Transformer encoder.
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num_self_attends_per_block (`int`, *optional*, defaults to 26):
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The number of self-attention layers per block.
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num_self_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each self-attention layer in the Transformer encoder.
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num_cross_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each cross-attention layer in the Transformer encoder.
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qk_channels (`int`, *optional*):
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Dimension to project the queries + keys before applying attention in the cross-attention and self-attention
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layers of the encoder. Will default to preserving the dimension of the queries if not specified.
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v_channels (`int`, *optional*):
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Dimension to project the values before applying attention in the cross-attention and self-attention layers
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of the encoder. Will default to preserving the dimension of the queries if not specified.
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cross_attention_shape_for_attention (`str`, *optional*, defaults to `"kv"`):
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Dimension to use when downsampling the queries and keys in the cross-attention layer of the encoder.
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self_attention_widening_factor (`int`, *optional*, defaults to 1):
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Dimension of the feed-forward layer in the cross-attention layer of the Transformer encoder.
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cross_attention_widening_factor (`int`, *optional*, defaults to 1):
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Dimension of the feed-forward layer in the self-attention layers of the Transformer encoder.
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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"` are supported.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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use_query_residual (`float`, *optional*, defaults to `True`):
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Whether to add a query residual in the cross-attention layer of the encoder.
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vocab_size (`int`, *optional*, defaults to 262):
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Vocabulary size for the masked language modeling model.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that the masked language modeling model might ever be used with. Typically set
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this to something large just in case (e.g., 512 or 1024 or 2048).
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image_size (`int`, *optional*, defaults to 56):
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Size of the images after preprocessing, for [`PerceiverForImageClassificationLearned`].
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train_size (`List[int]`, *optional*, defaults to `[368, 496]`):
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Training size of the images for the optical flow model.
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num_frames (`int`, *optional*, defaults to 16):
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Number of video frames used for the multimodal autoencoding model.
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audio_samples_per_frame (`int`, *optional*, defaults to 1920):
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Number of audio samples per frame for the multimodal autoencoding model.
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samples_per_patch (`int`, *optional*, defaults to 16):
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Number of audio samples per patch when preprocessing the audio for the multimodal autoencoding model.
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output_shape (`List[int]`, *optional*, defaults to `[1, 16, 224, 224]`):
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Shape of the output (batch_size, num_frames, height, width) for the video decoder queries of the multimodal
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autoencoding model. This excludes the channel dimension.
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output_num_channels (`int`, *optional*, defaults to 512):
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Number of output channels for each modalitiy decoder.
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Example:
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```python
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>>> from transformers import PerceiverModel, PerceiverConfig
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>>> # Initializing a Perceiver deepmind/language-perceiver style configuration
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>>> configuration = PerceiverConfig()
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>>> # Initializing a model from the deepmind/language-perceiver style configuration
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>>> model = PerceiverModel(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 = "perceiver"
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def __init__(
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self,
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num_latents=256,
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d_latents=1280,
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d_model=768,
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num_blocks=1,
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num_self_attends_per_block=26,
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num_self_attention_heads=8,
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num_cross_attention_heads=8,
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qk_channels=None,
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v_channels=None,
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cross_attention_shape_for_attention="kv",
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self_attention_widening_factor=1,
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cross_attention_widening_factor=1,
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hidden_act="gelu",
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attention_probs_dropout_prob=0.1,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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use_query_residual=True,
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vocab_size=262,
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max_position_embeddings=2048,
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image_size=56,
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train_size=[368, 496],
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num_frames=16,
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audio_samples_per_frame=1920,
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samples_per_patch=16,
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output_shape=[1, 16, 224, 224],
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output_num_channels=512,
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_label_trainable_num_channels=1024,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.num_latents = num_latents
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self.d_latents = d_latents
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self.d_model = d_model
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self.num_blocks = num_blocks
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self.num_self_attends_per_block = num_self_attends_per_block
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self.num_self_attention_heads = num_self_attention_heads
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self.num_cross_attention_heads = num_cross_attention_heads
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self.qk_channels = qk_channels
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self.v_channels = v_channels
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self.cross_attention_shape_for_attention = cross_attention_shape_for_attention
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self.self_attention_widening_factor = self_attention_widening_factor
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self.cross_attention_widening_factor = cross_attention_widening_factor
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self.hidden_act = hidden_act
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.use_query_residual = use_query_residual
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# masked language modeling attributes
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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# image classification attributes
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self.image_size = image_size
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# flow attributes
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self.train_size = train_size
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# multimodal autoencoding attributes
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self.num_frames = num_frames
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self.audio_samples_per_frame = audio_samples_per_frame
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self.samples_per_patch = samples_per_patch
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self.output_shape = output_shape
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self.output_num_channels = output_num_channels
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self._label_trainable_num_channels = _label_trainable_num_channels
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class PerceiverOnnxConfig(OnnxConfig):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task == "multiple-choice":
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dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
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else:
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dynamic_axis = {0: "batch", 1: "sequence"}
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return OrderedDict(
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[
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("inputs", dynamic_axis),
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("attention_mask", dynamic_axis),
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]
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)
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@property
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def atol_for_validation(self) -> float:
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return 1e-4
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def generate_dummy_inputs(
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self,
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preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
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batch_size: int = -1,
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seq_length: int = -1,
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num_choices: int = -1,
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is_pair: bool = False,
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framework: Optional[TensorType] = None,
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num_channels: int = 3,
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image_width: int = 40,
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image_height: int = 40,
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) -> Mapping[str, Any]:
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# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
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if isinstance(preprocessor, PreTrainedTokenizerBase):
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# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
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batch_size = compute_effective_axis_dimension(
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batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
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)
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# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
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token_to_add = preprocessor.num_special_tokens_to_add(is_pair)
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seq_length = compute_effective_axis_dimension(
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seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
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)
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# Generate dummy inputs according to compute batch and sequence
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dummy_input = [" ".join(["a"]) * seq_length] * batch_size
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inputs = dict(preprocessor(dummy_input, return_tensors=framework))
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inputs["inputs"] = inputs.pop("input_ids")
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return inputs
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elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values":
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# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
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batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
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dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
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inputs = dict(preprocessor(images=dummy_input, return_tensors=framework))
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inputs["inputs"] = inputs.pop("pixel_values")
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return inputs
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else:
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raise ValueError(
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"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."
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)
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