first commit for takway.ai

funasr/models/llm_asr/adaptor.py

@@ -0,0 +1,65 @@
+import torch
+import torch.nn as nn
+
+from funasr.register import tables
+
+
+@tables.register("adaptor_classes", "Linear")
+class Linear(nn.Module):
+    def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
+        super().__init__()
+        self.k = downsample_rate
+        self.encoder_dim = encoder_dim
+        self.llm_dim = llm_dim
+        self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
+        self.relu = nn.ReLU()
+        self.linear2 = nn.Linear(ffn_dim, self.llm_dim)
+
+    def forward(self, x):
+        batch_size, seq_len, dim = x.size()
+        num_frames_to_discard = seq_len % self.k
+        if num_frames_to_discard > 0:
+            x = x[:, :-num_frames_to_discard, :]
+        seq_len = x.size(1)
+
+        x = x.contiguous()
+        x = x.view(batch_size, seq_len // self.k, dim * self.k)
+        x = self.linear1(x)
+        x = self.relu(x)
+        x = self.linear2(x)
+        return x
+
+
+@tables.register("adaptor_classes", "QFormer")
+class EncoderProjectorQFormer(nn.Module):
+    def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
+        super().__init__()
+        self.encoder_dim = encoder_dim
+        self.llm_dim = llm_dim
+        from transformers import Blip2QFormerConfig, Blip2QFormerModel
+
+        configuration = Blip2QFormerConfig()
+        configuration.encoder_hidden_size = self.encoder_dim
+        configuration.num_hidden_layers = 2
+
+        self.query_len = 64
+        self.query = nn.Parameter(torch.zeros(1, self.query_len, configuration.hidden_size))
+        self.query.data.normal_(mean=0.0, std=1.0)
+        self.qformer = Blip2QFormerModel(configuration)
+
+        self.linear = nn.Linear(configuration.hidden_size, self.llm_dim)
+        self.norm = nn.LayerNorm(self.llm_dim, eps=1e-5)
+
+    def forward(self, x, atts):
+        query = self.query.expand(x.shape[0], -1, -1)
+
+        query_output = self.qformer(
+            query_embeds=query,
+            encoder_hidden_states=x,
+            encoder_attention_mask=atts,
+            return_dict=True,
+        )
+
+        query_proj = self.norm(self.linear(query_output.last_hidden_state))
+
+        return query_proj