feat: improve onnx support

modelling_codegen.py

@@ -40,7 +40,10 @@ def fixed_pos_embedding(x, seq_dim=1, seq_len=None):
     if seq_len is None:
         seq_len = x.shape[seq_dim]
     inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
-    sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(seq_len), inv_freq).to(x.device).float()
+    # original
+    # sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(seq_len), inv_freq).to(x.device).float()
+    # QHD fix onnx error by https://github.com/microsoft/onnxruntime/discussions/10121#discussioncomment-1987845
+    sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(seq_len).float(), inv_freq).to(x.device).float()
     return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)