87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
# Copyright 2022 The Music Spectrogram Diffusion Authors.
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# Copyright 2023 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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import torch
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import torch.nn as nn
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from transformers.modeling_utils import ModuleUtilsMixin
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from transformers.models.t5.modeling_t5 import T5Block, T5Config, T5LayerNorm
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...models import ModelMixin
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class SpectrogramNotesEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
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@register_to_config
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def __init__(
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self,
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max_length: int,
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vocab_size: int,
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d_model: int,
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dropout_rate: float,
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num_layers: int,
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num_heads: int,
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d_kv: int,
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d_ff: int,
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feed_forward_proj: str,
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is_decoder: bool = False,
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):
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super().__init__()
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self.token_embedder = nn.Embedding(vocab_size, d_model)
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self.position_encoding = nn.Embedding(max_length, d_model)
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self.position_encoding.weight.requires_grad = False
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self.dropout_pre = nn.Dropout(p=dropout_rate)
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t5config = T5Config(
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vocab_size=vocab_size,
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d_model=d_model,
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num_heads=num_heads,
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d_kv=d_kv,
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d_ff=d_ff,
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dropout_rate=dropout_rate,
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feed_forward_proj=feed_forward_proj,
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is_decoder=is_decoder,
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is_encoder_decoder=False,
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)
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self.encoders = nn.ModuleList()
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for lyr_num in range(num_layers):
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lyr = T5Block(t5config)
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self.encoders.append(lyr)
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self.layer_norm = T5LayerNorm(d_model)
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self.dropout_post = nn.Dropout(p=dropout_rate)
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def forward(self, encoder_input_tokens, encoder_inputs_mask):
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x = self.token_embedder(encoder_input_tokens)
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seq_length = encoder_input_tokens.shape[1]
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inputs_positions = torch.arange(seq_length, device=encoder_input_tokens.device)
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x += self.position_encoding(inputs_positions)
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x = self.dropout_pre(x)
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# inverted the attention mask
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input_shape = encoder_input_tokens.size()
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extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape)
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for lyr in self.encoders:
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x = lyr(x, extended_attention_mask)[0]
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x = self.layer_norm(x)
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return self.dropout_post(x), encoder_inputs_mask
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