[relay][frontend] Return module from frontend parsers (apache#3353)

python/tvm/relay/backend/interpreter.py

@@ -21,7 +21,8 @@
 import numpy as np
 
 from . import _backend
-from .. import _make, ir_pass
+from .. import _make, ir_pass, transform
+from .. import module
 from ... import register_func, nd
 from ..base import NodeBase, register_relay_node
 from ..expr import Tuple, RefCreate, Call, Constant, GlobalVar, Function, const
@@ -191,14 +192,14 @@ def _convert_args(self, expr, args, kwargs):
 
         return tuple(cargs)
 
-    def _make_executor(self, _):
+    def _make_executor(self, expr=None):
         """
         Construct a Python function that implements the evaluation
         of expression.
 
         Parameters
         ----------
-        expr: relay.Expr
+        expr: Optional[relay.Expr]
             The Relay expression to execute.
 
         Returns
@@ -208,16 +209,16 @@ def _make_executor(self, _):
         """
         raise NotImplementedError()
 
-    def evaluate(self, expr, binds=None):
+    def evaluate(self, expr=None, binds=None):
         """
         Evaluate a Relay expression on the executor.
 
         Parameters
         ----------
-        expr: tvm.relay.Expr
+        expr: Optional[tvm.relay.Expr]
             The expression to evaluate.
 
-        binds: Map[tvm.relay.Var, tvm.relay.Expr]
+        binds: Optional[Map[tvm.relay.Var, tvm.relay.Expr]]
             Additional binding of free variable.
 
         Returns
@@ -232,6 +233,9 @@ def evaluate(self, expr, binds=None):
             scope_builder.ret(expr)
             expr = scope_builder.get()
 
+        if not expr:
+            return self._make_executor()
+
         if isinstance(expr, Function):
             assert not ir_pass.free_vars(expr)
 
@@ -264,46 +268,47 @@ def __init__(self, mod, ctx, target):
         self.target = target
         self._intrp = _backend.CreateInterpreter(mod, ctx, target)
 
-    def optimize(self, expr):
-        """Optimize an expr.
-
-        Parameters
-        ----------
-        expr : Expr
-            The expression to be optimized.
+    def optimize(self):
+        """Optimize functions in a module.
 
         Returns
         -------
-        opt_expr : Expr
-            The optimized expression.
+        opt_mod : tvm.relay.Module
+            The optimized module.
         """
-        # TODO: We need to move this optimization code into the optimizer/pass manager
-        wrapped_expr = expr if isinstance(expr, Function) else Function([], expr)
-        if self.mod:
-            self.mod[self.mod.entry_func] = wrapped_expr
-        ck_expr = ir_pass.infer_type(wrapped_expr, mod=self.mod)
-        simp_expr = ir_pass.simplify_inference(ck_expr)
-        ck_simp = ir_pass.infer_type(simp_expr, mod=self.mod)
-        fused_expr = ir_pass.fuse_ops(ck_simp, 0, mod=self.mod)
-        ck_fused = ir_pass.infer_type(fused_expr, mod=self.mod)
-        return ck_fused if isinstance(expr, Function) else Call(ck_fused, [])
-
-    def _make_executor(self, expr):
+        seq = transform.Sequential([transform.SimplifyInference(),
+                                    transform.FuseOps(0),
+                                    transform.InferType()])
+        return seq(self.mod)
+
+    def _make_executor(self, expr=None):
+        if expr is None or isinstance(expr, GlobalVar):
+            assert self.mod is not None
         def _interp_wrapper(*args, **kwargs):
-            args = self._convert_args(expr, args, kwargs)
+            if expr is None:
+                args = self._convert_args(self.mod[self.mod.entry_func], args, kwargs)
+            else:
+                args = self._convert_args(expr, args, kwargs)
 
             relay_args = []
             for arg in args:
                 relay_args.append(_arg_to_ast(arg))
 
-            if isinstance(expr, GlobalVar):
-                func = self.mod[expr]
-                func = self.optimize(func)
-                self.mod._add(expr, func, True)
-                opt_expr = Call(expr, relay_args)
-                return self._intrp(opt_expr)
+            # Set the entry function for the module.
+            if expr is None:
+                pass
+            elif isinstance(expr, GlobalVar):
+                self.mod[self.mod.entry_func] = self.mod[expr]
             else:
-                call = Call(expr, relay_args)
-                opt_expr = self.optimize(call)
-                return self._intrp(opt_expr)
+                assert isinstance(expr, Function)
+                func = Function([], Call(expr, relay_args))
+                relay_args = []
+                if self.mod:
+                    self.mod[self.mod.entry_func] = func
+                else:
+                    self.mod = module.Module.from_expr(func)
+
+            mod = self.optimize()
+            opt_expr = Call(mod[self.mod.entry_func.name_hint], relay_args)
+            return self._intrp(opt_expr)
         return _interp_wrapper

python/tvm/relay/backend/vm.py

@@ -130,9 +130,11 @@ def __init__(self, mod, ctx, target):
         self.ctx = ctx
         self.target = target
 
-    def _make_executor(self, expr):
-        assert isinstance(expr, Expr)
-        self.mod[self.mod.entry_func] = expr
+    def _make_executor(self, expr=None):
+        expr = expr if expr else self.mod
+        assert expr, "either expr or self.mod should be not null."
+        if isinstance(expr, Expr):
+            self.mod[self.mod.entry_func] = expr
         main = self.mod[self.mod.entry_func]
 
         def _vm_wrapper(*args, **kwargs):

python/tvm/relay/build_module.py

@@ -219,16 +219,19 @@ def __init__(self, mod, ctx, target):
         self.ctx = ctx
         self.target = target
 
-    def _make_executor(self, func):
-        ret_type = ir_pass.infer_type(func).ret_type
+    def _make_executor(self, expr=None):
+        if not expr:
+            assert self.mod, "either expr or self.mod should be not null."
+            expr = self.mod[self.mod.entry_func]
+        ret_type = ir_pass.infer_type(expr).ret_type
         num_outputs = len(ret_type.fields) if isinstance(ret_type, _ty.TupleType) else 1
-        graph_json, mod, params = build(func, target=self.target)
+        graph_json, mod, params = build(expr, target=self.target)
         gmodule = _graph_rt.create(graph_json, mod, self.ctx)
         if params:
             gmodule.set_input(**params)
 
         def _graph_wrapper(*args, **kwargs):
-            args = self._convert_args(func, args, kwargs)
+            args = self._convert_args(expr, args, kwargs)
             # Create map of inputs.
             for i, arg in enumerate(args):
                 gmodule.set_input(i, arg)

python/tvm/relay/frontend/caffe2.py

@@ -20,6 +20,7 @@
 import tvm
 from .. import ir_pass
 from .. import expr as _expr
+from .. import module as _module
 from .. import op as _op
 from ... import nd as _nd
 from .common import AttrCvt, Renamer
@@ -382,6 +383,7 @@ def __init__(self, shape, dtype):
         self._ops = {}
         self._shape = shape
         self._dtype = dtype
+        self._mod = _module.Module({})
 
     def from_caffe2(self, init_net, predict_net):
         """Construct Relay expression from caffe2 graph.
@@ -393,8 +395,9 @@ def from_caffe2(self, init_net, predict_net):
 
         Returns
         -------
-        func : tvm.relay.expr.Function
-            Compatible relay function
+        mod : tvm.relay.Module
+            The module that optimizations will be performed on.
+
         params : dict
             A dict of name: tvm.nd.array pairs, used as pretrained weights
         """
@@ -448,8 +451,9 @@ def from_caffe2(self, init_net, predict_net):
             outputs = out[0]
 
         func = _expr.Function(ir_pass.free_vars(outputs), outputs)
+        self._mod[self._mod.entry_func] = func
 
-        return func, self._params
+        return self._mod, self._params
 
     def _get_node(self, blob):
         """Get the Symbol of blob and detect cyclic dependency in the graph."""
@@ -560,8 +564,8 @@ def from_caffe2(init_net, predict_net, shape=None, dtype="float32"):
 
     Returns
     -------
-    sym : tvm.relay.expr.Function
-        Compatible relay function
+    mod : tvm.relay.Module
+        The module that optimizations will be performed on.
 
     params : dict of str to tvm.ndarray
         Dict of converted parameters stored in tvm.ndarray format

python/tvm/relay/frontend/coreml.py

@@ -21,6 +21,7 @@
 import tvm
 from .. import ir_pass
 from .. import expr as _expr
+from .. import module as _module
 from .. import op as _op
 from ... import nd as _nd
 from ..._ffi import base as _base
@@ -416,8 +417,8 @@ def from_coreml(model, shape=None):
 
     Returns
     -------
-    func : tvm.relay.Function
-        Compatible relay Function.
+    mod : tvm.relay.Module
+        The relay module for compilation.
 
     params : dict of str to tvm.NDArray
         The parameter dict to be used by Relay.
@@ -463,4 +464,4 @@ def from_coreml(model, shape=None):
     outexpr = outexpr[0]
     func = _expr.Function(ir_pass.free_vars(outexpr), outexpr)
     params = {k:_nd.array(np.array(v, dtype=np.float32)) for k, v in etab.params.items()}
-    return func, params
+    return _module.Module.from_expr(func), params

python/tvm/relay/frontend/darknet.py

@@ -25,6 +25,7 @@
 import tvm
 from .. import ir_pass
 from .. import expr as _expr
+from .. import module as _module
 from .common import get_relay_op, new_var
 
 __all__ = ['from_darknet']
@@ -820,7 +821,7 @@ def from_darknet(self):
         outputs = _as_list(sym) + self._outs
         outputs = outputs[0] if len(outputs) == 1 else _expr.Tuple(outputs)
         sym = _expr.Function(ir_pass.free_vars(outputs), outputs)
-        return sym, self._tvmparams
+        return _module.Module.from_expr(sym), self._tvmparams
 
 def from_darknet(net,
                  shape=None,
@@ -838,8 +839,9 @@ def from_darknet(net,
 
     Returns
     -------
-    sym : tvm.relay.Function
-        Compatible relay Function
+    mod : tvm.relay.Module
+        The relay module for compilation.
+
     params : dict of str to tvm.NDArray
         The parameter dict to be used by relay
     """

python/tvm/relay/frontend/keras.py

@@ -22,6 +22,7 @@
 import tvm
 from .. import ir_pass
 from .. import expr as _expr
+from .. import module as _module
 from .. import op as _op
 from ... import nd as _nd
 from .common import ExprTable, new_var
@@ -679,8 +680,8 @@ def from_keras(model, shape=None):
 
     Returns
     -------
-    func : tvm.relay.Function
-        Compatible relay Function.
+    mod : tvm.relay.Module
+        The relay module for compilation.
 
     params : dict of str to tvm.NDArray
         The parameter dict to be used by Relay.
@@ -744,4 +745,4 @@ def _convert_input_layer(keras_layer):
     outexpr = outexpr[0] if len(outexpr) == 1 else _expr.Tuple(outexpr)
     func = _expr.Function(ir_pass.free_vars(outexpr), outexpr)
     params = {k:_nd.array(np.array(v, dtype=np.float32)) for k, v in etab.params.items()}
-    return func, params
+    return _module.Module.from_expr(func), params

python/tvm/relay/frontend/mxnet.py

@@ -23,6 +23,7 @@
 from .. import ir_pass
 from .. import expr as _expr
 from .. import op as _op
+from .. import module as _module
 from ... import nd as _nd
 
 from .common import StrAttrsDict
@@ -992,7 +993,8 @@ def _lstm_cell(data, states, i2h_weight, h2h_weight, i2h_bias, h2h_bias):
 _convert_map.update({k : _rename(k) for k in _identity_list})
 
 
-def _from_mxnet_impl(symbol, shape_dict, dtype_info):
+def _from_mxnet_impl(symbol, shape_dict, dtype_info, mod=None):
+    #pylint: disable=unused-argument
     """Convert mxnet symbol to compatible relay Function.
 
     Reconstruct a relay Function by traversing the mxnet symbol.
@@ -1009,6 +1011,10 @@ def _from_mxnet_impl(symbol, shape_dict, dtype_info):
     dtype_info : dict or str.
         Known parameter dtypes
 
+    mod : tvm.relay.Module
+        The module that contains global information. It will be used for
+        converting ops that need global information, e.g. control-flow ops.
+
     Returns:
     -------
     func : tvm.relay.Function
@@ -1097,8 +1103,8 @@ def from_mxnet(symbol,
 
     Returns
     -------
-    sym : tvm.relay.Function
-        Compatible relay Function
+    mod : tvm.relay.Module
+        The relay module for compilation
 
     params : dict of str to tvm.NDArray
         The parameter dict to be used by nnvm
@@ -1108,6 +1114,7 @@ def from_mxnet(symbol,
     except ImportError as e:
         raise ImportError("{}. MXNet is required to parse symbols.".format(e))
 
+    mod = _module.Module()
     if isinstance(symbol, mx.sym.Symbol):
         params = {}
         arg_params = arg_params if arg_params else {}
@@ -1117,7 +1124,7 @@ def from_mxnet(symbol,
         for k, v in aux_params.items():
             params[k] = _nd.array(v.asnumpy())
         shape, dtype = _update_shape_dtype(shape, dtype, params)
-        sym = _from_mxnet_impl(symbol, shape, dtype)
+        func = _from_mxnet_impl(symbol, shape, dtype, mod)
     elif isinstance(symbol, mx.gluon.HybridBlock):
         if arg_params is not None or aux_params is not None:
             raise ValueError("arg_params and aux_params ae not used when importing HybridBlock")
@@ -1129,10 +1136,11 @@ def from_mxnet(symbol,
         if isinstance(sym, (list, tuple)):
             sym = mx.sym.Group(sym)
         shape, dtype = _update_shape_dtype(shape, dtype, params)
-        sym = _from_mxnet_impl(sym, shape, dtype)
+        func = _from_mxnet_impl(sym, shape, dtype, mod)
     elif isinstance(symbol, mx.gluon.Block):
         raise NotImplementedError("Only Hybrid Blocks are supported now.")
     else:
         msg = "mxnet.Symbol or gluon.HybridBlock expected, got {}".format(type(symbol))
         raise ValueError(msg)
-    return sym, params
+    mod[mod.entry_func] = func
+    return mod, params