Reduce whisper decoder file size with onnx export (#328)

@@ -200,10 +200,25 @@ class TextDecoderTensorCache(nn.Module):
 
         x = self.textDecoder.ln(x)
 
-        logits = (
-            x
-            @ torch.transpose(self.textDecoder.token_embedding.weight.to(x.dtype), 0, 1)
-        ).float()
+        if False:
+            # x.shape (1, 3, 384)
+            # weight.shape (51684, 384)
+
+            logits = (
+                x
+                @ torch.transpose(
+                    self.textDecoder.token_embedding.weight.to(x.dtype), 0, 1
+                )
+            ).float()
+        else:
+            logits = (
+                torch.matmul(
+                    self.textDecoder.token_embedding.weight.to(x.dtype),
+                    x.permute(0, 2, 1),
+                )
+                .permute(0, 2, 1)
+                .float()
+            )
 
         return logits, n_layer_self_k_cache, n_layer_self_v_cache
 
@@ -246,6 +261,19 @@ def main():
     opset_version = 13
 
     model = whisper.load_model(name)
+    print(
+        f"number of model parameters: {name}",
+        sum(p.numel() for p in model.parameters()),
+    )
+    print(
+        f"number of encoder parameters: {name}",
+        sum(p.numel() for p in model.encoder.parameters()),
+    )
+    print(
+        f"number of decoder parameters: {name}",
+        sum(p.numel() for p in model.decoder.parameters()),
+    )
+
     convert_tokens(name=name, model=model)
 
     # write tokens
@@ -419,7 +447,7 @@ def main():
         },
     )
 
-    if 'large' in args.model:
+    if "large" in args.model:
         # it causes errors for large models, so skip it.
         return
     # Generate int8 quantization models