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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/06/14 21:53:54 UTC

[GitHub] [tvm] comaniac commented on a change in pull request #8251: [Frontend, Tensorflow] Support for broadcasting in batch_matmul when shapes differ

comaniac commented on a change in pull request #8251:
URL: https://github.com/apache/tvm/pull/8251#discussion_r651303466



##########
File path: python/tvm/relay/frontend/tensorflow_ops.py
##########
@@ -1157,11 +1154,18 @@ def _impl(inputs, attr, params, mod):
                 new_shape_y = _op.concatenate(_op.Tuple(new_shape_y), axis=0)
 
             input_x = _op.reshape(input_x, newshape=new_shape_x)
-            input_y = _op.reshape(input_y, newshape=new_shape_y)
+
+            if np.prod(orig_shape_y) < np.prod(new_shape_y):
+                input_y = _op.broadcast_to(input_y, new_shape_y)

Review comment:
       Agree. Please refer to ONNX and PyTorch frontend to avoid explicit broadcasting. Now both x86 and CUDA implementations of batch_matmul support implicit broadcasting, so simply `expand_dims(input_y)` to make it `(1, k, n)` would be sufficient.




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