import torch
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import torch.nn as nn
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from funasr.register import tables
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@tables.register("adaptor_classes", "Linear")
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class Linear(nn.Module):
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def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
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super().__init__()
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self.k = downsample_rate
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self.encoder_dim = encoder_dim
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self.llm_dim = llm_dim
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self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
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self.relu = nn.ReLU()
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self.linear2 = nn.Linear(ffn_dim, self.llm_dim)
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def forward(self, x):
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batch_size, seq_len, dim = x.size()
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num_frames_to_discard = seq_len % self.k
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if num_frames_to_discard > 0:
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x = x[:, :-num_frames_to_discard, :]
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seq_len = x.size(1)
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x = x.contiguous()
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x = x.view(batch_size, seq_len // self.k, dim * self.k)
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x = self.linear1(x)
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x = self.relu(x)
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x = self.linear2(x)
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return x
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@tables.register("adaptor_classes", "QFormer")
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class EncoderProjectorQFormer(nn.Module):
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def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
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super().__init__()
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self.encoder_dim = encoder_dim
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self.llm_dim = llm_dim
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from transformers import Blip2QFormerConfig, Blip2QFormerModel
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configuration = Blip2QFormerConfig()
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configuration.encoder_hidden_size = self.encoder_dim
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configuration.num_hidden_layers = 2
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self.query_len = 64
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self.query = nn.Parameter(torch.zeros(1, self.query_len, configuration.hidden_size))
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self.query.data.normal_(mean=0.0, std=1.0)
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self.qformer = Blip2QFormerModel(configuration)
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self.linear = nn.Linear(configuration.hidden_size, self.llm_dim)
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self.norm = nn.LayerNorm(self.llm_dim, eps=1e-5)
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def forward(self, x, atts):
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query = self.query.expand(x.shape[0], -1, -1)
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query_output = self.qformer(
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query_embeds=query,
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encoder_hidden_states=x,
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encoder_attention_mask=atts,
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return_dict=True,
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)
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query_proj = self.norm(self.linear(query_output.last_hidden_state))
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return query_proj
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