[Torch] Remove unnecessary reshapes for batch_matmul (apache#7675)

* [Torch] Remove unnecessary reshapes for batch_matmul

* lint

* fix

* reorder

* lint

python/tvm/relay/frontend/pytorch.py

@@ -1094,8 +1094,7 @@ def instance_norm(self, inputs, input_types):
             data, gamma, beta, axis=1, epsilon=epsilon, center=center, scale=scale
         )
 
-    @staticmethod
-    def get_dims(data):
+    def get_dims(self, data):
         import torch
 
         if isinstance(data, _expr.Expr):
@@ -1575,15 +1574,31 @@ def matmul(self, inputs, input_types):
 
         # When performing a batch matmul, we need to properly handle N-dim shapes.
         if len(a_shape) > 2 or len(b_shape) > 2:
-            # Convert a and b into 3 dimensional tensors.
-            a = _op.reshape(inputs_0, [-1, a_shape[-2], a_shape[-1]])
-            b = _op.reshape(inputs_1, [-1, b_shape[-2], b_shape[-1]])
+            # Convert a into a 3 dimensional tensors.
+            need_reshape_output = False
+            if len(a_shape) != 3:
+                a = _op.reshape(inputs_0, [-1, a_shape[-2], a_shape[-1]])
+                need_reshape_output = True
+            else:
+                a = inputs_0
+
             # Transpose matrix dimensions of b.
-            b = _op.transpose(b, [0, 2, 1])
+            trans_axes = list(range(len(b_shape)))
+            trans_axes[-2], trans_axes[-1] = trans_axes[-1], trans_axes[-2]
+            b = _op.transpose(inputs_1, trans_axes)
+
+            # Convert b into a 3 dimensional tensor. Note that the last two dimensions
+            # are transposed.
+            if len(b_shape) != 3:
+                b = _op.reshape(b, [-1, b_shape[-1], b_shape[-2]])
+
             # Perform a batch matmul.
             output = _op.nn.batch_matmul(a, b)
+
             # Reshape output to original dimensions.
-            return _op.reshape(output, [*a_shape[:-2], a_shape[-2], b_shape[-1]])
+            if need_reshape_output:
+                return _op.reshape(output, [*a_shape[:-2], a_shape[-2], b_shape[-1]])
+            return output
 
         # Otherwise a simple dense op will get the job done.
         if len(b_shape) == 1: