decompile tf control flow

python/tvm/relay/frontend/tensorflow.py

@@ -5,6 +5,7 @@
 
 import logging
 import warnings
+from collections import defaultdict
 # Numpy support
 import numpy as np
 
@@ -1270,6 +1271,100 @@ def _get_abs_layer_name(node):
                                                      params, num_layers)
         return sym
 
+_control_flow_nodes = ['Merge', 'Switch', 'NextIteration', 'Exit', 'Enter', 'LoopCond']
+
+class Branch:
+    """A class contains the components that are used to build up a Relay if
+    node.
+    """
+    def __init__(self):
+        self._if = None
+        self.cond_vars = set()
+        self.cond = None
+        self.true_branch = None
+        self.false_branch = None
+
+    def _if_node(self):
+        from tvm import relay
+
+        cond_vars = []
+        bind_map = {}
+        for i, var in enumerate(list(self.cond_vars)):
+            if not isinstance(var, _expr.Var):
+                raise TypeError("var is expected to be _expr.Var type, but "
+                                "received {}".format(repr(var)))
+            v = relay.var("cond_var" + str(i),
+                          type_annotation=var.type_annotation)
+            cond_vars.append(v)
+            bind_map[var] = v
+
+        self.cond = relay.bind(self.cond, bind_map)
+        cond = relay.op.min(self.cond)
+        self.true_branch = relay.bind(self.true_branch, bind_map)
+        self.false_branch = relay.bind(self.false_branch, bind_map)
+
+        return relay.If(cond, self.true_branch, self.false_branch)
+
+    def if_node(self):
+        """Create a if node if it hasn't been created yet."""
+        if self._if is None:
+            self._if = self._if_node()
+            return self._if
+        return self._if
+
+
+class Loop:
+    """A class contains the components that are used to build up a Relay
+    recursive call.
+    """
+    def __init__(self):
+        self.loop_vars = []
+        self.cond = None
+        self.body = []
+        self._loop = None
+
+    def _while_loop(self):
+        from tvm import relay
+        wl = relay.var('while_loop')
+        sb = relay.scope_builder.ScopeBuilder()
+
+        loop_vars = []
+        bind_map = {}
+        for i, var in enumerate(self.loop_vars):
+            assert isinstance(var, _expr.Var), repr(var)
+            v = relay.var("loop_var" + str(i),
+                          type_annotation=var.type_annotation)
+            loop_vars.append(v)
+            bind_map[var] = v
+
+        self.cond = relay.bind(self.cond, bind_map)
+        self.body = [relay.bind(b, bind_map) for b in self.body]
+
+        cond = relay.op.min(self.cond)
+
+        with sb.if_scope(cond):
+            sb.ret(wl(*self.body))
+        with sb.else_scope():
+            sb.ret(relay.Tuple(loop_vars))
+
+        loop_fn = relay.Function(loop_vars, sb.get())
+        sb = relay.scope_builder.ScopeBuilder()
+        sb.let(wl, loop_fn)
+        sb.ret(wl(*self.loop_vars))
+        return sb.get()
+
+    def while_loop(self):
+        if self._loop is None:
+            self._loop = self._while_loop()
+            return self._loop
+        return self._loop
+
+
+def _in_while_loop(control_flow_node_map, op_name):
+    return op_name in control_flow_node_map and \
+            "LoopCond" in control_flow_node_map[op_name]
+
+
 class GraphProto(object):
     """ A helper class for handling relay graph copying from Tensorflow GraphDef.
     Definition:
@@ -1284,6 +1379,9 @@ def __init__(self):
         self._num_rnn_layer = False
         self._outputs_are_0d = {}
         self._input_shapes = {}
+        self._loops = {}
+        self._branches = {}
+        # self.module = relay.Module({})
 
     def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
         """Construct relay nodes from tensorflow  graph definition - GraphDef.
@@ -1332,7 +1430,10 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
             raise NotImplementedError( \
                 "The following operators are not implemented: {}".format(missing_operators))
 
+        control_flow_node_map = defaultdict(set)
         for node in graph.node:
+            node_name_prefix = node.name.rsplit('/', 1)[0]
+            control_flow_node_map[node_name_prefix].add(node.op)
             if node.op == 'Placeholder':
                 if shape and node.name in shape:
                     self._input_shapes[node.name] = list(shape[node.name])
@@ -1451,8 +1552,53 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
 
                 attr['_input_shapes'] = input_shapes
                 attr['_input_0d_mismatch'] = input_0d_mismatch
+                node_name_prefix = node.name.rsplit('/', 1)[0]
 
-                op = self._convert_operator(node.op, inputs, attr, graph)
+                if node.op == "Merge":
+                    if _in_while_loop(control_flow_node_map, node_name_prefix):
+                        op = self._nodes[node.input[0]]
+                        self._loops[node_name_prefix] = Loop()
+                    else:
+                        if len(self._branches) == 0:
+                            raise RuntimeError("Cannot find a created "
+                                               "conditional for merge node")
+                        branch = self._branches[node_name_prefix]
+                        false_br = self._nodes[node.input[0]]
+                        true_br = self._nodes[node.input[1]]
+                        assert len(true_br) == 1
+                        assert len(false_br) == 1
+                        branch.true_branch = true_br[0]
+                        branch.false_branch = false_br[0]
+                        op = [branch.if_node()]
+                        # del self._branches[node_name_prefix]
+                elif node.op == "Exit":
+                    loop = self._loops[node_name_prefix]
+                    exit_name = node.name.split('/')[-1]
+                    assert str.startswith(exit_name, 'Exit')
+                    exit_number = int("0" + exit_name[4:])
+                    expr = loop.while_loop()
+                    op = _expr.TupleGetItem(expr, exit_number)
+                elif node.op == "Enter":
+                    op = self._nodes[node.input[0]]
+                elif node.op == "LoopCond":
+                    op = self._nodes[node.input[0]]
+                    assert len(op) == 1
+                    self._loops[node_name_prefix].cond = op[0]
+                elif node.op == "Switch":
+                    op = self._nodes[node.input[0]]
+                    assert len(op) == 1
+                    if _in_while_loop(control_flow_node_map, node_name_prefix):
+                        self._loops[node_name_prefix].loop_vars.append(op[0])
+                    else:
+                        if node_name_prefix not in self._branches:
+                            self._branches[node_name_prefix] = Branch()
+                        self._branches[node_name_prefix].cond = ir_pass.infer_type(op[0])
+                elif node.op == "NextIteration":
+                    op = self._nodes[node.input[0]]
+                    assert len(op) == 1
+                    self._loops[node_name_prefix].body.append(op[0])
+                else:
+                    op = self._convert_operator(node.op, inputs, attr, graph)
 
                 # Check if op is converted to param
                 if isinstance(op, np.ndarray):
@@ -1493,7 +1639,10 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
 
         out = []
         if outputs is None:
-            out = op
+            if node.op == "Exit":
+                out = [op[0].tuple_value]
+            else:
+                out = op
         else:
             for out_name in outputs:
                 if ":" in out_name:
@@ -1529,7 +1678,9 @@ def _parse_import_prerequisites(self, graph):
             elif node.op == "Const":
                 pass
             else:
-                if any([node.op in t for t in [_identity_list, _convert_map, _convert_map_rnn]]):
+                if any([node.op in t for t in [_identity_list, _convert_map,
+                                               _convert_map_rnn,
+                                               _control_flow_nodes]]):
                     pass
                 else:
                     missing_operators.add(node.op)