Fix forward method overload in TextDecoder

Author: eleanorTurintech

whisper/model.py

@@ -229,91 +229,58 @@ def __init__(
             self.register_buffer("mask_cuda", mask.cuda(), persistent=False)
 
 
-    def forward(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+    def forward(self, tokens: Tensor, audio_features: Tensor, kv_cache: Optional[dict] = None) -> Tensor:
         """
         Args:
             tokens: (n_batch, n_token)
             audio_features: (n_batch, n_audio_ctx, n_audio_state)
+            kv_cache: Optional cache for key/value tensors
 
         Returns:
             logits: (n_batch, n_token, n_vocab)
         """
         n_batch, n_token = tokens.shape
-        n_audio_ctx, n_audio_state = audio_features.shape[1:]
-
-        x = self.token_embedding(tokens) + self.positional_embedding[:n_token]
+        
+        # Get the dtype of audio_features to ensure consistency
+        dtype = audio_features.dtype
+        
+        # Handle kv_cache for token embedding offset
+        if kv_cache is not None:
+            offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+            x = self.token_embedding(tokens) + self.positional_embedding[offset:offset + tokens.shape[1]]
+        else:
+            x = self.token_embedding(tokens) + self.positional_embedding[:n_token]
+        
+        # Convert to the same dtype as audio_features
+        x = x.to(dtype)
 
         # Optimisation: Move audio_features to GPU once here.
         if torch.cuda.is_available():
             audio_features = audio_features.cuda()
 
-
+        # Process through attention blocks
         for block in self.blocks:
-            x = block(x, audio_features)
+            x = block(x, audio_features, kv_cache=kv_cache)
 
         x = self.ln(x)
-        logits = x @ self.token_embedding.weight.T
-
-        # Optimisation: Apply the precomputed CUDA mask if available.
-        if torch.cuda.is_available():
-             mask = self.mask_cuda[:n_token, :n_token]
-        else:
-            mask = self.mask[:n_token, :n_token]
 
-        logits = logits + mask
-
-        return logits
-
-
-    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
-        """
-        Args:
-            tokens: (n_batch, n_token) or x tensor
-            audio_features: (n_batch, n_audio_ctx, n_audio_state) or xa tensor
-            kv_cache: Optional cache for key/value tensors
-        """
-        if kv_cache is not None:
-            # Handle the kv_cache case
-            offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
-            x = (
-                self.token_embedding(tokens)
-                + self.positional_embedding[offset : offset + tokens.shape[-1]]
-            )
-            x = x.to(audio_features.dtype)
-
-            for block in self.blocks:
-                x = block(x, audio_features, mask=self.mask, kv_cache=kv_cache)
-
-            x = self.ln(x)
-            logits = (
-                x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
-            ).float()
-
-            return logits
-        else:
-            # Handle the non-kv_cache case
-            n_batch, n_token = tokens.shape
-            x = self.token_embedding(tokens) + self.positional_embedding[:n_token]
-
-            if torch.cuda.is_available():
-                audio_features = audio_features.cuda()
-
-            for block in self.blocks:
-                x = block(x, audio_features)
-
-            x = self.ln(x)
-            logits = x @ self.token_embedding.weight.T
-
+        # Ensure consistent dtype for matrix multiplication
+        # Convert token_embedding weight to the same dtype as x
+        embedding_weights = self.token_embedding.weight.to(x.dtype)
+        logits = x @ embedding_weights.T
+
+        # Apply mask if not using kv_cache (inference)
+        if kv_cache is None:
+            # Optimisation: Apply the precomputed CUDA mask if available.
             if torch.cuda.is_available():
                 mask = self.mask_cuda[:n_token, :n_token]
             else:
                 mask = self.mask[:n_token, :n_token]
-
+            
             logits = logits + mask
 
-            return logits
-
-
+        return logits
+    
 # The Whisper class has been moved outside of TextDecoder and is now a top-level class
 class Whisper(nn.Module):
     def __init__(self, dims: ModelDimensions):