kevinthesun commented on a change in pull request #5306: [Torch] Support Python
list, more realistic recurrent networks
URL: https://github.com/apache/incubator-tvm/pull/5306#discussion_r407269285
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File path: python/tvm/relay/frontend/pytorch.py
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@@ -1077,6 +1186,50 @@ def _impl(inputs, input_types):
return _op.cast(inputs[0], "float32")
return _impl
+
+def _mm():
+ def _impl(inputs, input_types):
+ return _op.nn.dense(inputs[0], inputs[1])
+ return _impl
+
+
+def _list_getitem(prelude):
+ def _impl(inputs, input_types):
+ return prelude.nth(inputs[0], _wrap_const(inputs[1]))
+ return _impl
+
+
+def _list_len(prelude):
+ def _impl(inputs, input_types):
+ return prelude.length(inputs[0])
+ return _impl
+
+
+def _add(prelude):
+ # add_ is overloaded for tensor add and list concat
+ def _impl(inputs, input_types):
+ if input_types[0] == "ListType":
+ return prelude.concat(inputs[0], inputs[1])
+ return _elemwise("add")(inputs, input_types)
+ return _impl
+
+
+def _tensor_array_stack(prelude):
+ def _impl(inputs, input_types):
+ tensor_array = _convert_to_tensor_array(inputs[0], prelude)
+ shape = get_tensor_array_shape(tensor_array, "float32", prelude)
+ stack = prelude.get_var_static('tensor_array_stack', "float32", shape)
Review comment:
I think we need to register static tensor array op first?
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