2298 lines
102 KiB
Python
2298 lines
102 KiB
Python
# coding=utf-8
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# Copyright 2023 The LAION-AI Team and The HuggingFace 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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""" PyTorch CLAP model."""
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import collections
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import math
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from dataclasses import dataclass
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from typing import Any, List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPooling,
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BaseModelOutputWithPoolingAndCrossAttentions,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, meshgrid, prune_linear_layer
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from ...utils import (
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ModelOutput,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from .configuration_clap import ClapAudioConfig, ClapConfig, ClapTextConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "laion/clap-htsat-fused"
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from ..deprecated._archive_maps import CLAP_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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# Adapted from: https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/utils.py#L191
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def interpolate(hidden_states, ratio):
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"""
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Interpolate data in time domain. This is used to compensate the resolution reduction in downsampling of a CNN.
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Args:
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hidden_states (`torch.FloatTensor` of shape (batch_size, time_length, classes_num)):
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Input hidden states
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ratio (`int`):
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The ratio of the length of the output to the length of the input.
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"""
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(batch_size, time_length, classes_num) = hidden_states.shape
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upsampled = hidden_states[:, :, None, :].repeat(1, 1, ratio, 1)
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upsampled = upsampled.reshape(batch_size, time_length * ratio, classes_num)
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return upsampled
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# Adapted from https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/htsat.py#L249
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def window_partition(hidden_states, window_size):
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"""
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Returns the resized hidden states. The output shape should be `(batch_size * num_windows, window_size, window_size,
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num_channels)`
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Args:
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hidden_states (`torch.FloatTensor` of shape `(batch_size, height, width, num_channels)`):
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Input hidden states
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window_size (`int`):
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Window size
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"""
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batch_size, height, width, num_channels = hidden_states.shape
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hidden_states = hidden_states.view(
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batch_size, height // window_size, window_size, width // window_size, window_size, num_channels
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)
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windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels)
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return windows
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# Adapted from https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/htsat.py#L263
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def window_reverse(windows, window_size, height, width):
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"""
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Merges windows to produce higher resolution features.
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Args:
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windows (`torch.FloatTensor` of shape `(num_windows * batch_size, window_size, window_size, num_channels)`):
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Input windows
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window_size (`int`):
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Window size
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height (`int`):
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Height of the resized audio
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width (`int`):
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Width of the resized audio
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"""
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num_channels = windows.shape[-1]
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windows = windows.view(-1, height // window_size, width // window_size, window_size, window_size, num_channels)
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windows = windows.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, height, width, num_channels)
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return windows
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# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
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def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
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"""
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
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are ignored. This is modified from fairseq's `utils.make_positions`.
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Args:
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x: torch.Tensor x:
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Returns: torch.Tensor
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"""
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# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
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mask = input_ids.ne(padding_idx).int()
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incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
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return incremental_indices.long() + padding_idx
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# contrastive loss function, adapted from
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# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html#CLIP-loss-function
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def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
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labels = torch.arange(len(logits), device=logits.device)
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return nn.functional.cross_entropy(logits, labels)
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@dataclass
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# Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Clap
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class ClapTextModelOutput(ModelOutput):
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"""
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Base class for text model's outputs that also contains a pooling of the last hidden states.
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Args:
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text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
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The text embeddings obtained by applying the projection layer to the pooler_output.
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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text_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class ClapAudioModelOutput(ModelOutput):
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"""
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ClapAudio model output to mimic the output of the original implementation.
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Args:
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audio_embeds (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
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The Audio embeddings obtained by applying the projection layer to the pooler_output.
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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"""
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audio_embeds: Optional[torch.FloatTensor] = None
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Clap, vision->audio, Vision->Audio, image->audio
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class ClapOutput(ModelOutput):
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"""
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
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Contrastive loss for audio-text similarity.
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logits_per_audio:(`torch.FloatTensor` of shape `(audio_batch_size, text_batch_size)`):
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The scaled dot product scores between `audio_embeds` and `text_embeds`. This represents the audio-text
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similarity scores.
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logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, audio_batch_size)`):
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The scaled dot product scores between `text_embeds` and `audio_embeds`. This represents the text-audio
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similarity scores.
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text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
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The text embeddings obtained by applying the projection layer to the pooled output of [`ClapTextModel`].
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audio_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
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The audio embeddings obtained by applying the projection layer to the pooled output of [`ClapAudioModel`].
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text_model_output(`BaseModelOutputWithPooling`):
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The output of the [`ClapTextModel`].
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audio_model_output(`BaseModelOutputWithPooling`):
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The output of the [`ClapAudioModel`].
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"""
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loss: Optional[torch.FloatTensor] = None
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logits_per_audio: torch.FloatTensor = None
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logits_per_text: torch.FloatTensor = None
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text_embeds: torch.FloatTensor = None
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audio_embeds: torch.FloatTensor = None
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text_model_output: BaseModelOutputWithPooling = None
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audio_model_output: BaseModelOutputWithPooling = None
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def to_tuple(self) -> Tuple[Any]:
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return tuple(
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self[k] if k not in ["text_model_output", "audio_model_output"] else getattr(self, k).to_tuple()
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for k in self.keys()
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)
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# Adapted from transformers.models.swin.modeling_swin.SwinDropPath
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class ClapDropPath(nn.Module):
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is a slightly
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refactored version of the `SwinDropPath` implementation.
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"""
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def __init__(self, drop_prob=None):
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super().__init__()
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self.drop_prob = drop_prob
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def forward(self, hidden_states):
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if self.drop_prob == 0.0 or not self.training:
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return hidden_states
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keep_prob = 1 - self.drop_prob
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# work with diff dim tensors, not just 2D ConvNets
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shape = (hidden_states.shape[0],) + (1,) * (hidden_states.ndim - 1)
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random_tensor = keep_prob + torch.rand(shape, dtype=hidden_states.dtype, device=hidden_states.device)
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random_tensor.floor_() # binarize
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output = hidden_states.div(keep_prob) * random_tensor
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return output
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# Adapted from https://github.com/LAION-AI/CLAP/blob/6ad05a971ba0622f6acee8c41993e0d02bbed639/src/open_clip/feature_fusion.py#L133
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class ClapAudioAFFBlock(nn.Module):
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r"""
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ATTENTIONAL FEATURE FUSION Block from CLAP, since in CLAP we are always in 2D mode, it is not needed to implement
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the 1D version.
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"""
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def __init__(self, config: ClapAudioConfig):
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super().__init__()
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channels = config.patch_embeds_hidden_size
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downsize_ratio = config.aff_block_r
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inter_channels = int(channels // downsize_ratio)
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self.local_att = nn.Sequential(
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nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(inter_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(channels),
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)
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self.global_att = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(inter_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(channels),
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)
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self.sigmoid = nn.Sigmoid()
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def forward(self, hidden_states, residual):
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attention_input = hidden_states + residual
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fused_layer_output = self.local_att(attention_input) + self.global_att(attention_input)
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fused_layer_output = self.sigmoid(fused_layer_output)
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output = 2 * hidden_states * fused_layer_output + 2 * residual * (1 - fused_layer_output)
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return output
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class ClapAudioPatchEmbed(nn.Module):
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"""
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This module converts the hidden states reshaped as an image to patch embeddings ready to be passed to the
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Transformer block.
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"""
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def __init__(self, config: ClapAudioConfig):
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super().__init__()
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img_size = (config.spec_size, config.spec_size) if isinstance(config.spec_size, int) else config.spec_size
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patch_size = (
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(config.patch_size, config.patch_size) if isinstance(config.patch_size, int) else config.patch_size
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)
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patch_stride = (
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(config.patch_stride, config.patch_stride) if isinstance(config.patch_stride, int) else config.patch_stride
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)
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self.img_size = img_size
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self.patch_stride = patch_stride
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self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.flatten = config.flatten_patch_embeds
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self.enable_fusion = config.enable_fusion
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padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
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scale_factor = 4 if (self.enable_fusion) and (config.fusion_type == "channel_map") else 1
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self.proj = nn.Conv2d(
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config.patch_embed_input_channels * scale_factor,
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config.patch_embeds_hidden_size,
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kernel_size=patch_size,
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stride=patch_stride,
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padding=padding,
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)
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self.norm = nn.LayerNorm(config.patch_embeds_hidden_size) if config.enable_patch_layer_norm else nn.Identity()
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if self.enable_fusion:
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self.fusion_model = ClapAudioAFFBlock(config)
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self.mel_conv2d = nn.Conv2d(
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config.patch_embed_input_channels,
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config.patch_embeds_hidden_size,
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kernel_size=(patch_size[0], patch_size[1] * 3),
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stride=(patch_stride[0], patch_stride[1] * 3),
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padding=padding,
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)
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def forward(self, hidden_states, is_longer_idx=None):
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if self.enable_fusion:
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# retrieve the last mel as we have transposed the input
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global_hidden_states = hidden_states[:, 0:1, :, :]
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# global processing
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batch_size, num_channels, height, width = global_hidden_states.shape
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if height != self.img_size[0] or width != self.img_size[1]:
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raise ValueError(
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f"Input audio size ({height}*{width}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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)
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global_hidden_states = self.proj(global_hidden_states)
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output_width = global_hidden_states.size(-1)
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if len(is_longer_idx) > 0:
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# local processing
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local_hidden_states = hidden_states[is_longer_idx, 1:, :, :].contiguous()
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batch_size, num_channels, height, width = local_hidden_states.shape
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local_hidden_states = local_hidden_states.view(batch_size * num_channels, 1, height, width)
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local_hidden_states = self.mel_conv2d(local_hidden_states)
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_, features, height, width = local_hidden_states.shape
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local_hidden_states = local_hidden_states.view(batch_size, num_channels, features, height, width)
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local_hidden_states = local_hidden_states.permute((0, 2, 3, 1, 4)).contiguous().flatten(3)
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local_width = local_hidden_states.size(-1)
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local_hidden_states = torch.nn.functional.pad(
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local_hidden_states, (0, output_width - local_width), "constant", 0
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)
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global_hidden_states[is_longer_idx] = self.fusion_model(
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global_hidden_states[is_longer_idx], local_hidden_states
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)
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hidden_states = global_hidden_states
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else:
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_, _, height, width = hidden_states.shape
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if height != self.img_size[0] or width != self.img_size[1]:
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raise ValueError(
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f"Input audio size ({height}*{width}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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)
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hidden_states = self.proj(hidden_states)
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if self.flatten:
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hidden_states = hidden_states.flatten(2).transpose(1, 2)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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# Copied from transformers.models.swin.modeling_swin.SwinSelfAttention with Swin->ClapAudio
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class ClapAudioSelfAttention(nn.Module):
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def __init__(self, config, dim, num_heads, window_size):
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super().__init__()
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if dim % num_heads != 0:
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raise ValueError(
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f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
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)
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self.num_attention_heads = num_heads
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self.attention_head_size = int(dim / num_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.window_size = (
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window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size)
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)
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), num_heads)
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)
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(self.window_size[0])
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coords_w = torch.arange(self.window_size[1])
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coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij"))
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coords_flatten = torch.flatten(coords, 1)
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
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relative_coords = relative_coords.permute(1, 2, 0).contiguous()
|
|
relative_coords[:, :, 0] += self.window_size[0] - 1
|
|
relative_coords[:, :, 1] += self.window_size[1] - 1
|
|
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
|
relative_position_index = relative_coords.sum(-1)
|
|
self.register_buffer("relative_position_index", relative_position_index)
|
|
|
|
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
|
|
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
|
|
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
|
|
def transpose_for_scores(self, x):
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
batch_size, dim, num_channels = hidden_states.shape
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
|
|
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)]
|
|
relative_position_bias = relative_position_bias.view(
|
|
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
|
|
)
|
|
|
|
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
|
|
attention_scores = attention_scores + relative_position_bias.unsqueeze(0)
|
|
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in ClapAudioModel forward() function)
|
|
mask_shape = attention_mask.shape[0]
|
|
attention_scores = attention_scores.view(
|
|
batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim
|
|
)
|
|
attention_scores = attention_scores + attention_mask.unsqueeze(1).unsqueeze(0)
|
|
attention_scores = attention_scores.view(-1, self.num_attention_heads, dim, dim)
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(new_context_layer_shape)
|
|
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinSelfOutput with Swin->ClapAudio
|
|
class ClapAudioSelfOutput(nn.Module):
|
|
def __init__(self, config, dim):
|
|
super().__init__()
|
|
self.dense = nn.Linear(dim, dim)
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinAttention with Swin->ClapAudio
|
|
class ClapAudioAttention(nn.Module):
|
|
def __init__(self, config, dim, num_heads, window_size):
|
|
super().__init__()
|
|
self.self = ClapAudioSelfAttention(config, dim, num_heads, window_size)
|
|
self.output = ClapAudioSelfOutput(config, dim)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinIntermediate with Swin->ClapAudio
|
|
class ClapAudioIntermediate(nn.Module):
|
|
def __init__(self, config, dim):
|
|
super().__init__()
|
|
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinOutput with Swin->ClapAudio
|
|
class ClapAudioOutput(nn.Module):
|
|
def __init__(self, config, dim):
|
|
super().__init__()
|
|
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinLayer with SwinDropPath->ClapDropPath, Swin->ClapAudio
|
|
class ClapAudioLayer(nn.Module):
|
|
def __init__(self, config, dim, input_resolution, num_heads, shift_size=0):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.shift_size = shift_size
|
|
self.window_size = config.window_size
|
|
self.input_resolution = input_resolution
|
|
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
|
|
self.attention = ClapAudioAttention(config, dim, num_heads, window_size=self.window_size)
|
|
self.drop_path = ClapDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
|
|
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
|
|
self.intermediate = ClapAudioIntermediate(config, dim)
|
|
self.output = ClapAudioOutput(config, dim)
|
|
|
|
def set_shift_and_window_size(self, input_resolution):
|
|
if min(input_resolution) <= self.window_size:
|
|
# if window size is larger than input resolution, we don't partition windows
|
|
self.shift_size = 0
|
|
self.window_size = min(input_resolution)
|
|
|
|
def get_attn_mask(self, height, width, dtype):
|
|
if self.shift_size > 0:
|
|
# calculate attention mask for SW-MSA
|
|
img_mask = torch.zeros((1, height, width, 1), dtype=dtype)
|
|
height_slices = (
|
|
slice(0, -self.window_size),
|
|
slice(-self.window_size, -self.shift_size),
|
|
slice(-self.shift_size, None),
|
|
)
|
|
width_slices = (
|
|
slice(0, -self.window_size),
|
|
slice(-self.window_size, -self.shift_size),
|
|
slice(-self.shift_size, None),
|
|
)
|
|
count = 0
|
|
for height_slice in height_slices:
|
|
for width_slice in width_slices:
|
|
img_mask[:, height_slice, width_slice, :] = count
|
|
count += 1
|
|
|
|
mask_windows = window_partition(img_mask, self.window_size)
|
|
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
|
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
|
else:
|
|
attn_mask = None
|
|
return attn_mask
|
|
|
|
def maybe_pad(self, hidden_states, height, width):
|
|
pad_right = (self.window_size - width % self.window_size) % self.window_size
|
|
pad_bottom = (self.window_size - height % self.window_size) % self.window_size
|
|
pad_values = (0, 0, 0, pad_right, 0, pad_bottom)
|
|
hidden_states = nn.functional.pad(hidden_states, pad_values)
|
|
return hidden_states, pad_values
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
input_dimensions: Tuple[int, int],
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
always_partition: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
if not always_partition:
|
|
self.set_shift_and_window_size(input_dimensions)
|
|
else:
|
|
pass
|
|
height, width = input_dimensions
|
|
batch_size, _, channels = hidden_states.size()
|
|
shortcut = hidden_states
|
|
|
|
hidden_states = self.layernorm_before(hidden_states)
|
|
|
|
hidden_states = hidden_states.view(batch_size, height, width, channels)
|
|
|
|
# pad hidden_states to multiples of window size
|
|
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
|
|
|
|
_, height_pad, width_pad, _ = hidden_states.shape
|
|
# cyclic shift
|
|
if self.shift_size > 0:
|
|
shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
|
else:
|
|
shifted_hidden_states = hidden_states
|
|
|
|
# partition windows
|
|
hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
|
|
hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
|
|
attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype)
|
|
if attn_mask is not None:
|
|
attn_mask = attn_mask.to(hidden_states_windows.device)
|
|
|
|
attention_outputs = self.attention(
|
|
hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions
|
|
)
|
|
|
|
attention_output = attention_outputs[0]
|
|
|
|
attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels)
|
|
shifted_windows = window_reverse(attention_windows, self.window_size, height_pad, width_pad)
|
|
|
|
# reverse cyclic shift
|
|
if self.shift_size > 0:
|
|
attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
|
else:
|
|
attention_windows = shifted_windows
|
|
|
|
was_padded = pad_values[3] > 0 or pad_values[5] > 0
|
|
if was_padded:
|
|
attention_windows = attention_windows[:, :height, :width, :].contiguous()
|
|
|
|
attention_windows = attention_windows.view(batch_size, height * width, channels)
|
|
|
|
hidden_states = shortcut + self.drop_path(attention_windows)
|
|
|
|
layer_output = self.layernorm_after(hidden_states)
|
|
layer_output = self.intermediate(layer_output)
|
|
layer_output = hidden_states + self.output(layer_output)
|
|
|
|
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
|
|
return layer_outputs
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinStage with Swin->ClapAudio
|
|
class ClapAudioStage(nn.Module):
|
|
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
|
|
super().__init__()
|
|
self.config = config
|
|
self.dim = dim
|
|
self.blocks = nn.ModuleList(
|
|
[
|
|
ClapAudioLayer(
|
|
config=config,
|
|
dim=dim,
|
|
input_resolution=input_resolution,
|
|
num_heads=num_heads,
|
|
shift_size=0 if (i % 2 == 0) else config.window_size // 2,
|
|
)
|
|
for i in range(depth)
|
|
]
|
|
)
|
|
|
|
# patch merging layer
|
|
if downsample is not None:
|
|
self.downsample = downsample(input_resolution, dim=dim, norm_layer=nn.LayerNorm)
|
|
else:
|
|
self.downsample = None
|
|
|
|
self.pointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
input_dimensions: Tuple[int, int],
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
always_partition: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
height, width = input_dimensions
|
|
for i, layer_module in enumerate(self.blocks):
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
hidden_states_before_downsampling = hidden_states
|
|
if self.downsample is not None:
|
|
height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
|
|
output_dimensions = (height, width, height_downsampled, width_downsampled)
|
|
hidden_states = self.downsample(hidden_states_before_downsampling, input_dimensions)
|
|
else:
|
|
output_dimensions = (height, width, height, width)
|
|
|
|
stage_outputs = (hidden_states, hidden_states_before_downsampling, output_dimensions)
|
|
|
|
if output_attentions:
|
|
stage_outputs += layer_outputs[1:]
|
|
return stage_outputs
|
|
|
|
|
|
# Copied from transformers.models.swin.modeling_swin.SwinPatchMerging with Swin->ClapAudio
|
|
class ClapAudioPatchMerging(nn.Module):
|
|
"""
|
|
Patch Merging Layer.
|
|
|
|
Args:
|
|
input_resolution (`Tuple[int]`):
|
|
Resolution of input feature.
|
|
dim (`int`):
|
|
Number of input channels.
|
|
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
|
|
Normalization layer class.
|
|
"""
|
|
|
|
def __init__(self, input_resolution: Tuple[int], dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
|
|
super().__init__()
|
|
self.input_resolution = input_resolution
|
|
self.dim = dim
|
|
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
|
self.norm = norm_layer(4 * dim)
|
|
|
|
def maybe_pad(self, input_feature, height, width):
|
|
should_pad = (height % 2 == 1) or (width % 2 == 1)
|
|
if should_pad:
|
|
pad_values = (0, 0, 0, width % 2, 0, height % 2)
|
|
input_feature = nn.functional.pad(input_feature, pad_values)
|
|
|
|
return input_feature
|
|
|
|
def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor:
|
|
height, width = input_dimensions
|
|
# `dim` is height * width
|
|
batch_size, dim, num_channels = input_feature.shape
|
|
|
|
input_feature = input_feature.view(batch_size, height, width, num_channels)
|
|
# pad input to be disible by width and height, if needed
|
|
input_feature = self.maybe_pad(input_feature, height, width)
|
|
# [batch_size, height/2, width/2, num_channels]
|
|
input_feature_0 = input_feature[:, 0::2, 0::2, :]
|
|
# [batch_size, height/2, width/2, num_channels]
|
|
input_feature_1 = input_feature[:, 1::2, 0::2, :]
|
|
# [batch_size, height/2, width/2, num_channels]
|
|
input_feature_2 = input_feature[:, 0::2, 1::2, :]
|
|
# [batch_size, height/2, width/2, num_channels]
|
|
input_feature_3 = input_feature[:, 1::2, 1::2, :]
|
|
# batch_size height/2 width/2 4*num_channels
|
|
input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1)
|
|
input_feature = input_feature.view(batch_size, -1, 4 * num_channels) # batch_size height/2*width/2 4*C
|
|
|
|
input_feature = self.norm(input_feature)
|
|
input_feature = self.reduction(input_feature)
|
|
|
|
return input_feature
|
|
|
|
|
|
class ClapAudioEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.num_layers = len(config.depths)
|
|
|
|
self.config = config
|
|
self.patch_embed = ClapAudioPatchEmbed(config)
|
|
self.enable_fusion = config.enable_fusion
|
|
self.patch_stride = self.patch_embed.patch_stride
|
|
self.spec_size = config.spec_size
|
|
self.freq_ratio = config.spec_size // config.num_mel_bins
|
|
|
|
self.num_features = int(config.patch_embeds_hidden_size * 2 ** (self.num_layers - 1))
|
|
|
|
drop_path_rate = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
|
|
|
|
grid_size = self.patch_embed.grid_size
|
|
self.input_resolutions = [(grid_size[0] // (2**i), grid_size[1] // (2**i)) for i in range(self.num_layers)]
|
|
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
ClapAudioStage(
|
|
config=config,
|
|
dim=int(config.patch_embeds_hidden_size * 2**i_layer),
|
|
input_resolution=self.input_resolutions[i_layer],
|
|
depth=config.depths[i_layer],
|
|
num_heads=config.num_attention_heads[i_layer],
|
|
drop_path=drop_path_rate[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
|
|
downsample=ClapAudioPatchMerging if (i_layer < self.num_layers - 1) else None,
|
|
)
|
|
for i_layer in range(self.num_layers)
|
|
]
|
|
)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
self.batch_norm = nn.BatchNorm2d(config.num_mel_bins)
|
|
self.norm = nn.LayerNorm(self.num_features)
|
|
self.depths = config.depths
|
|
self.avgpool = nn.AdaptiveAvgPool1d(1)
|
|
|
|
def reshape_mel2img(self, normalized_input_features):
|
|
"""
|
|
The input is 4 normalized log mel spectrograms. It is reshape to the common shape of images. Each channel
|
|
should represent 1 of the 4 crops of the spectrogram. For more details, refer to the [`ClapFeatureExtractor`].
|
|
"""
|
|
_, _, time_length, freq_length = normalized_input_features.shape
|
|
|
|
spec_width = int(self.spec_size * self.freq_ratio)
|
|
spec_heigth = self.spec_size // self.freq_ratio
|
|
|
|
if time_length > spec_width or freq_length > spec_heigth:
|
|
raise ValueError("the wav size should be less than or equal to the swin input size")
|
|
|
|
# to avoid bicubic zero error
|
|
if time_length < spec_width:
|
|
normalized_input_features = nn.functional.interpolate(
|
|
normalized_input_features, (spec_width, freq_length), mode="bicubic", align_corners=True
|
|
)
|
|
if freq_length < spec_heigth:
|
|
normalized_input_features = nn.functional.interpolate(
|
|
normalized_input_features, (time_length, spec_heigth), mode="bicubic", align_corners=True
|
|
)
|
|
|
|
batch, channels, time, freq = normalized_input_features.shape
|
|
|
|
# batch_size, channels, spec_width, spec_heigth --> batch_size, channels, spec_heigth * freq_ratio, spec_width // freq_ratio
|
|
normalized_input_features = normalized_input_features.reshape(
|
|
batch, channels * self.freq_ratio, time // self.freq_ratio, freq
|
|
)
|
|
normalized_input_features = normalized_input_features.permute(0, 1, 3, 2).contiguous()
|
|
normalized_input_features = normalized_input_features.reshape(
|
|
batch, channels, freq * self.freq_ratio, time // self.freq_ratio
|
|
)
|
|
|
|
return normalized_input_features
|
|
|
|
def forward(
|
|
self,
|
|
input_features,
|
|
is_longer: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
output_hidden_states_before_downsampling: Optional[bool] = False,
|
|
always_partition: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple, ClapAudioModelOutput]:
|
|
input_features = input_features.transpose(1, 3)
|
|
normalized_input_features = self.batch_norm(input_features)
|
|
normalized_input_features = normalized_input_features.transpose(1, 3)
|
|
|
|
is_longer_list_idx = None
|
|
if self.enable_fusion:
|
|
is_longer_list = is_longer.to(input_features.device)
|
|
is_longer_list_idx = torch.where(is_longer_list == 1)[0]
|
|
|
|
hidden_states = self.reshape_mel2img(normalized_input_features)
|
|
|
|
frames_num = hidden_states.shape[2]
|
|
|
|
hidden_states = self.patch_embed(hidden_states, is_longer_list_idx)
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_reshaped_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
input_dimensions = self.input_resolutions[0]
|
|
|
|
if output_hidden_states:
|
|
batch_size, _, hidden_size = hidden_states.shape
|
|
# rearrange batch_size (height width) channels -> batch_size channel height width
|
|
reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
|
|
reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
|
|
for i, layer_module in enumerate(self.layers):
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
input_dimensions = self.input_resolutions[i]
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__, hidden_states, input_dimensions, layer_head_mask, output_attentions
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
hidden_states_before_downsampling = layer_outputs[1]
|
|
output_dimensions = layer_outputs[2]
|
|
|
|
input_dimensions = (output_dimensions[-2], output_dimensions[-1])
|
|
|
|
if output_hidden_states and output_hidden_states_before_downsampling:
|
|
batch_size, _, hidden_size = hidden_states_before_downsampling.shape
|
|
# rearrange batch_size (height width) channels -> batch_size channel height width
|
|
# here we use the original (not downsampled) height and width
|
|
reshaped_hidden_state = hidden_states_before_downsampling.view(
|
|
batch_size, *(output_dimensions[0], output_dimensions[1]), hidden_size
|
|
)
|
|
reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states_before_downsampling,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
elif output_hidden_states and not output_hidden_states_before_downsampling:
|
|
batch_size, _, hidden_size = hidden_states.shape
|
|
# rearrange batch_size (height width) channels -> batch_size channel height width
|
|
reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
|
|
reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
|
|
all_hidden_states += (hidden_states,)
|
|
all_reshaped_hidden_states += (reshaped_hidden_state,)
|
|
|
|
if output_attentions:
|
|
all_self_attentions += layer_outputs[3:]
|
|
|
|
last_hidden_state = self.norm(hidden_states)
|
|
|
|
batch_size, _, n_channels = last_hidden_state.shape
|
|
|
|
freq_shape = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
|
|
temporal_shape = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
|
|
|
|
last_hidden_state = (
|
|
last_hidden_state.permute(0, 2, 1).contiguous().reshape(batch_size, n_channels, freq_shape, temporal_shape)
|
|
)
|
|
|
|
batch_size, n_channels, n_frequencies, n_temp = last_hidden_state.shape
|
|
# group 2D CNN
|
|
c_freq_bin = n_frequencies // self.freq_ratio
|
|
last_hidden_state = last_hidden_state.reshape(
|
|
batch_size, n_channels, n_frequencies // c_freq_bin, c_freq_bin, n_temp
|
|
)
|
|
last_hidden_state = (
|
|
last_hidden_state.permute(0, 1, 3, 2, 4).contiguous().reshape(batch_size, n_channels, c_freq_bin, -1)
|
|
)
|
|
latent_output = self.avgpool(torch.flatten(last_hidden_state, 2))
|
|
latent_output = torch.flatten(latent_output, 1)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
last_hidden_state,
|
|
latent_output,
|
|
all_reshaped_hidden_states,
|
|
all_self_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=latent_output,
|
|
hidden_states=all_reshaped_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
CLAP_START_DOCSTRING = r"""
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`ClapConfig`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
CLAP_TEXT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
|
it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
CLAP_AUDIO_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_features (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Input audio features. This should be returnes by the [`ClapFeatureExtractor`] class that you can also
|
|
retrieve from [`AutoFeatureExtractor`]. See [`ClapFeatureExtractor.__call__`] for details.
|
|
is_longer (`torch.FloatTensor`, of shape `(batch_size, 1)`, *optional*):
|
|
Whether the audio clip is longer than `max_length`. If `True`, a feature fusion will be enabled to enhance
|
|
the features.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
CLAP_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
|
it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
input_features (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Input audio features. This should be returnes by the [`ClapFeatureExtractor`] class that you can also
|
|
retrieve from [`AutoFeatureExtractor`]. See [`ClapFeatureExtractor.__call__`] for details.
|
|
return_loss (`bool`, *optional*):
|
|
Whether or not to return the contrastive loss.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
class ClapProjectionLayer(nn.Module):
|
|
def __init__(self, config: Union[ClapAudioConfig, ClapTextConfig]):
|
|
super().__init__()
|
|
self.config = config
|
|
hidden_size = config.hidden_size
|
|
projection_dim = config.projection_dim
|
|
|
|
self.linear1 = nn.Linear(hidden_size, projection_dim)
|
|
self.activation = ACT2FN[config.projection_hidden_act]
|
|
self.linear2 = nn.Linear(projection_dim, projection_dim)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.linear1(hidden_states)
|
|
hidden_states = self.activation(hidden_states)
|
|
hidden_states = self.linear2(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->ClapText, persistent=False->persistent=True
|
|
class ClapTextEmbeddings(nn.Module):
|
|
"""
|
|
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
|
|
"""
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
|
# any TensorFlow checkpoint file
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
|
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
|
self.register_buffer(
|
|
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=True
|
|
)
|
|
self.register_buffer(
|
|
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=True
|
|
)
|
|
|
|
# End copy
|
|
self.padding_idx = config.pad_token_id
|
|
self.position_embeddings = nn.Embedding(
|
|
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
|
|
)
|
|
|
|
def forward(
|
|
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
|
|
):
|
|
if position_ids is None:
|
|
if input_ids is not None:
|
|
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
|
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
|
|
else:
|
|
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
|
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
|
|
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
|
|
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
|
|
# issue #5664
|
|
if token_type_ids is None:
|
|
if hasattr(self, "token_type_ids"):
|
|
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + token_type_embeddings
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings += position_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
|
|
"""
|
|
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
|
|
|
|
Args:
|
|
inputs_embeds: torch.Tensor
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
sequence_length = input_shape[1]
|
|
|
|
position_ids = torch.arange(
|
|
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
|
|
)
|
|
return position_ids.unsqueeze(0).expand(input_shape)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->ClapText
|
|
class ClapTextSelfAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None):
|
|
super().__init__()
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
|
|
f"heads ({config.num_attention_heads})"
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
self.position_embedding_type = position_embedding_type or getattr(
|
|
config, "position_embedding_type", "absolute"
|
|
)
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
self.max_position_embeddings = config.max_position_embeddings
|
|
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
|
|
|
|
self.is_decoder = config.is_decoder
|
|
|
|
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
# If this is instantiated as a cross-attention module, the keys
|
|
# and values come from an encoder; the attention mask needs to be
|
|
# such that the encoder's padding tokens are not attended to.
|
|
is_cross_attention = encoder_hidden_states is not None
|
|
|
|
if is_cross_attention and past_key_value is not None:
|
|
# reuse k,v, cross_attentions
|
|
key_layer = past_key_value[0]
|
|
value_layer = past_key_value[1]
|
|
attention_mask = encoder_attention_mask
|
|
elif is_cross_attention:
|
|
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
|
|
attention_mask = encoder_attention_mask
|
|
elif past_key_value is not None:
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
|
|
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
|
|
else:
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
|
|
use_cache = past_key_value is not None
|
|
if self.is_decoder:
|
|
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
|
|
# Further calls to cross_attention layer can then reuse all cross-attention
|
|
# key/value_states (first "if" case)
|
|
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
|
|
# all previous decoder key/value_states. Further calls to uni-directional self-attention
|
|
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
|
|
# if encoder bi-directional self-attention `past_key_value` is always `None`
|
|
past_key_value = (key_layer, value_layer)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
|
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
|
|
if use_cache:
|
|
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
|
|
-1, 1
|
|
)
|
|
else:
|
|
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
|
|
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
|
|
distance = position_ids_l - position_ids_r
|
|
|
|
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
|
|
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
|
|
|
|
if self.position_embedding_type == "relative_key":
|
|
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
attention_scores = attention_scores + relative_position_scores
|
|
elif self.position_embedding_type == "relative_key_query":
|
|
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
|
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
|
|
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in ClapTextModel forward() function)
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(new_context_layer_shape)
|
|
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
|
|
if self.is_decoder:
|
|
outputs = outputs + (past_key_value,)
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
|
|
class ClapTextSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->ClapText
|
|
class ClapTextAttention(nn.Module):
|
|
def __init__(self, config, position_embedding_type=None):
|
|
super().__init__()
|
|
self.self = ClapTextSelfAttention(config, position_embedding_type=position_embedding_type)
|
|
self.output = ClapTextSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
|
|
class ClapTextIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertOutput
|
|
class ClapTextOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->ClapText
|
|
class ClapTextLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = ClapTextAttention(config)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
if not self.is_decoder:
|
|
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
|
|
self.crossattention = ClapTextAttention(config, position_embedding_type="absolute")
|
|
self.intermediate = ClapTextIntermediate(config)
|
|
self.output = ClapTextOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor]:
|
|
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
|
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
past_key_value=self_attn_past_key_value,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
|
|
# if decoder, the last output is tuple of self-attn cache
|
|
if self.is_decoder:
|
|
outputs = self_attention_outputs[1:-1]
|
|
present_key_value = self_attention_outputs[-1]
|
|
else:
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
cross_attn_present_key_value = None
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
if not hasattr(self, "crossattention"):
|
|
raise ValueError(
|
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
|
|
" by setting `config.add_cross_attention=True`"
|
|
)
|
|
|
|
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
|
|
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
|
|
cross_attention_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask,
|
|
head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
cross_attn_past_key_value,
|
|
output_attentions,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
|
|
|
# add cross-attn cache to positions 3,4 of present_key_value tuple
|
|
cross_attn_present_key_value = cross_attention_outputs[-1]
|
|
present_key_value = present_key_value + cross_attn_present_key_value
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
# if decoder, return the attn key/values as the last output
|
|
if self.is_decoder:
|
|
outputs = outputs + (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->ClapText
|
|
class ClapTextEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([ClapTextLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
next_decoder_cache = () if use_cache else None
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
past_key_value = past_key_values[i] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
past_key_value,
|
|
output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[-1],)
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
next_decoder_cache,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_decoder_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPooler
|
|
class ClapTextPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class ClapPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = ClapConfig
|
|
base_model_prefix = "clap"
|
|
supports_gradient_checkpointing = False
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
factor = self.config.initializer_factor
|
|
|
|
if isinstance(module, ClapTextEmbeddings):
|
|
module.position_embeddings.weight.data.normal_(mean=0.0, std=factor * 0.02)
|
|
module.token_type_embeddings.weight.data.normal_(mean=0.0, std=factor * 0.02)
|
|
elif isinstance(module, ClapModel):
|
|
nn.init.normal_(module.logit_scale_a, std=factor * 0.02)
|
|
nn.init.normal_(module.logit_scale_t, std=factor * 0.02)
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=factor * 0.02)
|
|
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, (nn.Conv2d, nn.Linear)):
|
|
in_proj_std = (self.config.hidden_size**-0.5) * ((2 * self.config.num_hidden_layers) ** -0.5) * factor
|
|
nn.init.normal_(module.weight, std=in_proj_std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
|
|
class ClapAudioModel(ClapPreTrainedModel):
|
|
config_class = ClapAudioConfig
|
|
main_input_name = "input_features"
|
|
|
|
def __init__(self, config: ClapAudioConfig):
|
|
super().__init__(config)
|
|
self.audio_encoder = ClapAudioEncoder(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.audio_encoder.patch_embed.proj
|
|
|
|
@add_start_docstrings_to_model_forward(CLAP_AUDIO_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=ClapAudioConfig)
|
|
def forward(
|
|
self,
|
|
input_features: Optional[torch.FloatTensor] = None,
|
|
is_longer: Optional[torch.BoolTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from datasets import load_dataset
|
|
>>> from transformers import AutoProcessor, ClapAudioModel
|
|
|
|
>>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
|
|
>>> audio_sample = dataset["train"]["audio"][0]["array"]
|
|
|
|
>>> model = ClapAudioModel.from_pretrained("laion/clap-htsat-fused")
|
|
>>> processor = AutoProcessor.from_pretrained("laion/clap-htsat-fused")
|
|
|
|
>>> inputs = processor(audios=audio_sample, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> last_hidden_state = outputs.last_hidden_state
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
return self.audio_encoder(
|
|
input_features=input_features,
|
|
is_longer=is_longer,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
|
|
class ClapTextModel(ClapPreTrainedModel):
|
|
"""
|
|
|
|
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
|
|
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
|
|
all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
|
|
Kaiser and Illia Polosukhin.
|
|
|
|
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
|
|
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
|
|
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
|
|
|
|
.. _*Attention is all you need*: https://arxiv.org/abs/1706.03762
|
|
|
|
"""
|
|
|
|
config_class = ClapTextConfig
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->ClapText
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = ClapTextEmbeddings(config)
|
|
self.encoder = ClapTextEncoder(config)
|
|
|
|
self.pooler = ClapTextPooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertModel.forward
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
r"""
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
|
the model is configured as a decoder.
|
|
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
|
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if self.config.is_decoder:
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
else:
|
|
use_cache = False
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
# past_key_values_length
|
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if self.config.is_decoder and encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
if encoder_attention_mask is None:
|
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(CLAP_START_DOCSTRING)
|
|
class ClapModel(ClapPreTrainedModel):
|
|
config_class = ClapConfig
|
|
|
|
def __init__(self, config: ClapConfig):
|
|
super().__init__(config)
|
|
|
|
if not isinstance(config.text_config, ClapTextConfig):
|
|
raise ValueError(
|
|
"config.text_config is expected to be of type ClapTextConfig but is of type"
|
|
f" {type(config.text_config)}."
|
|
)
|
|
|
|
if not isinstance(config.audio_config, ClapAudioConfig):
|
|
raise ValueError(
|
|
"config.audio_config is expected to be of type ClapAudioConfig but is of type"
|
|
f" {type(config.audio_config)}."
|
|
)
|
|
|
|
text_config = config.text_config
|
|
audio_config = config.audio_config
|
|
|
|
self.logit_scale_a = nn.Parameter(torch.tensor(math.log(config.logit_scale_init_value)))
|
|
self.logit_scale_t = nn.Parameter(torch.tensor(math.log(config.logit_scale_init_value)))
|
|
|
|
self.projection_dim = config.projection_dim
|
|
|
|
self.text_model = ClapTextModel(text_config)
|
|
self.text_projection = ClapProjectionLayer(text_config)
|
|
|
|
self.audio_model = ClapAudioModel(audio_config)
|
|
self.audio_projection = ClapProjectionLayer(audio_config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(CLAP_TEXT_INPUTS_DOCSTRING)
|
|
def get_text_features(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> torch.FloatTensor:
|
|
r"""
|
|
Returns:
|
|
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
|
|
applying the projection layer to the pooled output of [`ClapTextModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, ClapModel
|
|
|
|
>>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")
|
|
|
|
>>> inputs = tokenizer(["the sound of a cat", "the sound of a dog"], padding=True, return_tensors="pt")
|
|
>>> text_features = model.get_text_features(**inputs)
|
|
```"""
|
|
# Use CLAP model's config for some fields (if specified) instead of those of audio & text components.
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = text_outputs[1] if return_dict is not None else text_outputs.pooler_output
|
|
text_features = self.text_projection(pooled_output)
|
|
text_features = F.normalize(text_features, dim=-1)
|
|
|
|
return text_features
|
|
|
|
@add_start_docstrings_to_model_forward(CLAP_AUDIO_INPUTS_DOCSTRING)
|
|
def get_audio_features(
|
|
self,
|
|
input_features: Optional[torch.Tensor] = None,
|
|
is_longer: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> torch.FloatTensor:
|
|
r"""
|
|
Returns:
|
|
audio_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The audio embeddings obtained by
|
|
applying the projection layer to the pooled output of [`ClapAudioModel`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoFeatureExtractor, ClapModel
|
|
>>> import torch
|
|
|
|
>>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
|
|
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused")
|
|
>>> random_audio = torch.rand((16_000))
|
|
>>> inputs = feature_extractor(random_audio, return_tensors="pt")
|
|
>>> audio_features = model.get_audio_features(**inputs)
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
audio_outputs = self.audio_model(
|
|
input_features=input_features,
|
|
is_longer=is_longer,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = audio_outputs[1] if not return_dict else audio_outputs.pooler_output
|
|
|
|
audio_features = self.audio_projection(pooled_output)
|
|
audio_features = F.normalize(audio_features, dim=-1)
|
|
|
|
return audio_features
|
|
|
|
@add_start_docstrings_to_model_forward(CLAP_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=ClapOutput, config_class=ClapConfig)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
input_features: Optional[torch.FloatTensor] = None,
|
|
is_longer: Optional[torch.BoolTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
return_loss: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, ClapOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from datasets import load_dataset
|
|
>>> from transformers import AutoProcessor, ClapModel
|
|
|
|
>>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
|
|
>>> audio_sample = dataset["train"]["audio"][0]["array"]
|
|
|
|
>>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
|
|
>>> processor = AutoProcessor.from_pretrained("laion/clap-htsat-unfused")
|
|
|
|
>>> input_text = ["Sound of a dog", "Sound of vaccum cleaner"]
|
|
|
|
>>> inputs = processor(text=input_text, audios=audio_sample, return_tensors="pt", padding=True)
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> logits_per_audio = outputs.logits_per_audio # this is the audio-text similarity score
|
|
>>> probs = logits_per_audio.softmax(dim=-1) # we can take the softmax to get the label probabilities
|
|
```"""
|
|
# Use CLAP model's config for some fields (if specified) instead of those of audio & text components.
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
audio_outputs = self.audio_model(
|
|
input_features=input_features,
|
|
is_longer=is_longer,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
audio_embeds = audio_outputs[1] if not return_dict else audio_outputs.pooler_output
|
|
audio_embeds = self.audio_projection(audio_embeds)
|
|
|
|
text_embeds = text_outputs[1] if not return_dict else text_outputs.pooler_output
|
|
text_embeds = self.text_projection(text_embeds)
|
|
|
|
# normalized features
|
|
audio_embeds = audio_embeds / audio_embeds.norm(p=2, dim=-1, keepdim=True)
|
|
text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
|
|
|
|
# cosine similarity as logits
|
|
logit_scale_text = self.logit_scale_t.exp()
|
|
logit_scale_audio = self.logit_scale_a.exp()
|
|
logits_per_text = torch.matmul(text_embeds, audio_embeds.t()) * logit_scale_text
|
|
logits_per_audio = torch.matmul(audio_embeds, text_embeds.t()) * logit_scale_audio
|
|
|
|
loss = None
|
|
if return_loss:
|
|
caption_loss = contrastive_loss(logits_per_text)
|
|
audio_loss = contrastive_loss(logits_per_audio.t())
|
|
loss = (caption_loss + audio_loss) / 2.0
|
|
|
|
if not return_dict:
|
|
output = (logits_per_audio, logits_per_text, text_embeds, audio_embeds, text_outputs, audio_outputs)
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ClapOutput(
|
|
loss=loss,
|
|
logits_per_audio=logits_per_audio,
|
|
logits_per_text=logits_per_text,
|
|
text_embeds=text_embeds,
|
|
audio_embeds=audio_embeds,
|
|
text_model_output=text_outputs,
|
|
audio_model_output=audio_outputs,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
CLAP Text Model with a projection layer on top (a linear layer on top of the pooled output).
|
|
""",
|
|
CLAP_START_DOCSTRING,
|
|
)
|
|
class ClapTextModelWithProjection(ClapPreTrainedModel):
|
|
config_class = ClapTextConfig
|
|
|
|
def __init__(self, config: ClapTextConfig):
|
|
super().__init__(config)
|
|
self.text_model = ClapTextModel(config)
|
|
self.text_projection = ClapProjectionLayer(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.text_model.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.text_model.embeddings.word_embeddings = value
|
|
|
|
@add_start_docstrings_to_model_forward(CLAP_TEXT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=ClapTextModelOutput, config_class=ClapTextConfig)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, ClapTextModelOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, ClapTextModelWithProjection
|
|
|
|
>>> model = ClapTextModelWithProjection.from_pretrained("laion/clap-htsat-unfused")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")
|
|
|
|
>>> inputs = tokenizer(["a sound of a cat", "a sound of a dog"], padding=True, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> text_embeds = outputs.text_embeds
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
text_outputs = self.text_model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = text_outputs[1] if not return_dict else text_outputs.pooler_output
|
|
|
|
text_embeds = self.text_projection(pooled_output)
|
|
|
|
if not return_dict:
|
|
outputs = (text_embeds, text_outputs[0]) + text_outputs[2:]
|
|
return tuple(output for output in outputs if output is not None)
|
|
|
|
return ClapTextModelOutput(
|
|
text_embeds=text_embeds,
|
|
last_hidden_state=text_outputs.last_hidden_state,
|
|
hidden_states=text_outputs.hidden_states,
|
|
attentions=text_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
CLAP Audio Model with a projection layer on top (a linear layer on top of the pooled output).
|
|
""",
|
|
CLAP_START_DOCSTRING,
|
|
)
|
|
class ClapAudioModelWithProjection(ClapPreTrainedModel):
|
|
config_class = ClapAudioConfig
|
|
main_input_name = "input_features"
|
|
|
|
def __init__(self, config: ClapAudioConfig):
|
|
super().__init__(config)
|
|
self.audio_model = ClapAudioModel(config)
|
|
self.audio_projection = ClapProjectionLayer(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Module:
|
|
return self.audio_model.audio_encoder.patch_embed.proj
|
|
|
|
@add_start_docstrings_to_model_forward(CLAP_AUDIO_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=ClapAudioModelOutput, config_class=ClapAudioConfig)
|
|
def forward(
|
|
self,
|
|
input_features: Optional[torch.FloatTensor] = None,
|
|
is_longer: Optional[torch.BoolTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, ClapAudioModelOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from datasets import load_dataset
|
|
>>> from transformers import ClapAudioModelWithProjection, ClapProcessor
|
|
|
|
>>> model = ClapAudioModelWithProjection.from_pretrained("laion/clap-htsat-fused")
|
|
>>> processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")
|
|
|
|
>>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
|
|
>>> audio_sample = dataset["train"]["audio"][0]["array"]
|
|
|
|
>>> inputs = processor(audios=audio_sample, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> audio_embeds = outputs.audio_embeds
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
audio_outputs = self.audio_model(
|
|
input_features=input_features,
|
|
is_longer=is_longer,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = audio_outputs[1] if not return_dict else audio_outputs.pooler_output
|
|
|
|
audio_embeds = self.audio_projection(pooled_output)
|
|
|
|
if not return_dict:
|
|
outputs = (audio_embeds, audio_outputs[0]) + audio_outputs[2:]
|
|
return tuple(output for output in outputs if output is not None)
|
|
|
|
return ClapAudioModelOutput(
|
|
audio_embeds=audio_embeds,
|
|
last_hidden_state=audio_outputs.last_hidden_state,
|
|
attentions=audio_outputs.attentions,
|
|
hidden_states=audio_outputs.hidden_states,
|
|
)
|