enable constexpr to run as graph

mindspore/ccsrc/pipeline/jit/static_analysis/evaluator.cc

@@ -517,7 +517,7 @@ EvalResultPtr TrivialPrimEvaluator::Run(AnalysisEnginePtr engine, const ConfigPt
 EvalResultPtr TransitionPrimEvaluator::Run(AnalysisEnginePtr engine, const ConfigPtrList &args_conf_list,
                                            const AnfNodeConfigPtr &out_conf) {
   if (args_conf_list.empty() && identifier_ != "MakeTupleEvaluator" && identifier_ != "MakeListEvaluator" &&
-      identifier_ != "RaiseEvaluator") {
+      identifier_ != "RaiseEvaluator" && identifier_ != "ConstexprEvaluator") {
     MS_LOG(EXCEPTION) << "Size should be greater than 0, during running " << identifier_;
   }
   AbstractBasePtrList args_abs_list = EvaluateArguments(args_conf_list);

mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc

@@ -1737,6 +1737,68 @@ EvalResultPtr StaticGetter(const AnalysisEnginePtr &engine, const AbstractBasePt
 }
 }  // namespace
 
+EvalResultPtr ConstexprEvaluator::EvalPrim(const AnalysisEnginePtr &engine, const AbstractBasePtrList &args_spec_list,
+                                           const ConfigPtr &, const AnfNodeConfigPtr &out_conf) {
+  // Consider all primitive implemented python infer() real use the tuple/list arguments.
+  CheckSequenceArgumentForPythonPrimitive(prim_py_, args_spec_list);
+  MS_EXCEPTION_IF_NULL(prim_py_);
+  auto py_args = PreparePyInputs(prim_py_, args_spec_list);
+  prim_py_->BeginRecordAddAttr();
+  py::dict output = prim_py_->RunInfer(py_args);
+  prim_py_->EndRecordAddAttr();
+  if (output.contains("fn")) {
+    // The inputs contain variable, the constexpr will run as graph.
+    py::tuple values = output["fn"];
+    if (values.empty()) {
+      MS_LOG(EXCEPTION) << "Can not get origin function from constexpr.";
+    }
+    auto inner_val = parse::ParsePythonCode(values[0]);
+    MS_EXCEPTION_IF_NULL(inner_val);
+    auto inner_fg = dyn_cast<FuncGraph>(inner_val);
+    MS_EXCEPTION_IF_NULL(inner_fg);
+    auto mng = Manage(inner_fg, false);
+    inner_fg->set_manager(mng);
+    MS_EXCEPTION_IF_NULL(out_conf);
+    auto out_node = out_conf->node();
+    MS_EXCEPTION_IF_NULL(out_node);
+    auto out_cnode = dyn_cast<CNode>(out_node);
+    MS_EXCEPTION_IF_NULL(out_cnode);
+    FuncGraphPtr func_graph = out_node->func_graph();
+    MS_EXCEPTION_IF_NULL(func_graph);
+    std::vector<AnfNodePtr> new_cnode_inputs = {NewValueNode(inner_fg)};
+    const auto &out_cnode_inputs = out_cnode->inputs();
+    (void)std::copy(out_cnode_inputs.begin() + 1, out_cnode_inputs.end(), std::back_inserter(new_cnode_inputs));
+    auto new_node = func_graph->NewCNodeInOrder(new_cnode_inputs);
+    func_graph->ReplaceInOrder(out_node, new_node);
+    AnalysisEnginePtr eng = out_conf->engine();
+    MS_EXCEPTION_IF_NULL(eng);
+    AnfNodeConfigPtr fn_conf = eng->MakeConfig(new_node, out_conf->context(), out_conf->func_graph());
+    return eng->ForwardConfig(out_conf, fn_conf);
+  }
+  // If all inputs are constant value, use python prim evaluator.
+  // Ensure input arguments are evaluated.
+  auto ret_abstract = EvalUndeterminedArgs(args_spec_list);
+  if (ret_abstract != nullptr) {
+    MS_LOG(DEBUG) << "PythonPrimEvaluator eval Undetermined";
+    return ret_abstract;
+  }
+  auto forbid_reuse = prim_py_->HasAttr(GRAPH_FLAG_FORBID_REUSE_RESULT);
+  if (!forbid_reuse) {
+    // Try to get infer result from evaluator cache.
+    EvalResultPtr eval_result = evaluator_cache_mgr_->GetValue(args_spec_list);
+    if (eval_result != nullptr) {
+      return std::make_shared<EvalResult>(eval_result->abstract()->Clone(), eval_result->attribute());
+    }
+  }
+  const auto &added_attrs = prim_py_->evaluate_added_attrs();
+  MS_LOG(DEBUG) << "Output type is " << py::str(output);
+  auto res_abs = PyInferRes2Abstract(prim_py_, output);
+  MS_LOG(DEBUG) << "Python InferTensor result abstract: " << res_abs->ToString();
+  EvalResultPtr eval_result = std::make_shared<EvalResult>(res_abs, std::make_shared<AttrValueMap>(added_attrs));
+  evaluator_cache_mgr_->SetValue(args_spec_list, eval_result);
+  return eval_result;
+}
+
 EvalResultPtr MakeTupleEvaluator::EvalPrim(const AnalysisEnginePtr &, const AbstractBasePtrList &args_abs_list,
                                            const ConfigPtr &, const AnfNodeConfigPtr &out_conf) {
   std::shared_ptr<AnfNodeWeakPtrList> sequence_nodes = std::make_shared<AnfNodeWeakPtrList>();

mindspore/ccsrc/pipeline/jit/static_analysis/prim.h

@@ -160,6 +160,19 @@ class MixedPrecisionCastEvaluator final : public Evaluator {
   PrimitivePtr prim_;
 };
 
+class ConstexprEvaluator : public TransitionPrimEvaluator {
+ public:
+  explicit ConstexprEvaluator(const PrimitivePyPtr primitive)
+      : TransitionPrimEvaluator("ConstexprEvaluator"), prim_py_(primitive) {}
+  ~ConstexprEvaluator() override = default;
+  MS_DECLARE_PARENT(ConstexprEvaluator, TransitionPrimEvaluator)
+  EvalResultPtr EvalPrim(const AnalysisEnginePtr &, const AbstractBasePtrList &args_spec_list, const ConfigPtr &,
+                         const AnfNodeConfigPtr &out_conf) override;
+
+ private:
+  PrimitivePyPtr prim_py_;
+};
+
 class MakeTupleEvaluator : public TransitionPrimEvaluator {
  public:
   MakeTupleEvaluator() : TransitionPrimEvaluator("MakeTupleEvaluator") {}

mindspore/ccsrc/pipeline/jit/static_analysis/static_analysis.cc

@@ -503,6 +503,29 @@ void AnalysisEngine::Clear() {
   root_context_ = nullptr;
 }
 
+EvaluatorPtr GetPyEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr &engine) {
+  auto prim_py = dyn_cast<PrimitivePy>(prim);
+  if (prim_py != nullptr) {
+    auto is_constexpr = prim_py->HasAttr(GRAPH_FLAG_CONSTEXPR_PRIM);
+    if (is_constexpr) {
+      return std::make_shared<ConstexprEvaluator>(prim_py);
+    }
+    if (engine == nullptr) {
+      return std::make_shared<PythonPrimEvaluator>(prim_py);
+    }
+
+    const auto &iter = engine->prim_py_evaluators_.find(prim_py);
+    if (iter != engine->prim_py_evaluators_.end()) {
+      return iter->second;
+    }
+    auto evaluator = std::make_shared<PythonPrimEvaluator>(prim_py);
+    engine->prim_py_evaluators_[prim_py] = evaluator;
+    return evaluator;
+  }
+  MS_LOG(ERROR) << "The primitive with python evaluator should be a python primitive.";
+  return nullptr;
+}
+
 EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr &engine) {
   // Custom Primitive with python infer_shape, infer_type
   MS_EXCEPTION_IF_NULL(prim);
@@ -533,22 +556,7 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr
   // Use python infer function if the infer function not founded in the map return a python evaluator
   EvaluatorPtr evaluator = nullptr;
   if (prim->HasPyEvaluator()) {
-    auto prim_py = dyn_cast<PrimitivePy>(prim);
-    if (prim_py != nullptr) {
-      if (engine == nullptr) {
-        return std::make_shared<PythonPrimEvaluator>(prim_py);
-      }
-
-      const auto &iter = engine->prim_py_evaluators_.find(prim_py);
-      if (iter != engine->prim_py_evaluators_.end()) {
-        return iter->second;
-      }
-      evaluator = std::make_shared<PythonPrimEvaluator>(prim_py);
-      engine->prim_py_evaluators_[prim_py] = evaluator;
-      return evaluator;
-    }
-    MS_LOG(ERROR) << "The primitive with python evaluator should be a python primitive.";
-    return nullptr;
+    return GetPyEvaluator(prim, engine);
   }
 
   // Return a default evaluator

mindspore/core/utils/flags.h

@@ -30,6 +30,7 @@ inline const char GRAPH_FLAG_SIDE_EFFECT_PROPAGATE[] = "side_effect_propagate";
 inline const char GRAPH_FLAG_SIDE_EFFECT_BACKPROP[] = "side_effect_backprop";
 inline const char GRAPH_FLAG_SIDE_EFFECT_BACKPROP_MEM[] = "side_effect_backprop_mem";
 inline const char GRAPH_FLAG_FORBID_REUSE_RESULT[] = "forbid_reuse_result";
+inline const char GRAPH_FLAG_CONSTEXPR_PRIM[] = "constexpr_prim";
 inline const char GRAPH_FLAG_IS_WHILE_HEADER[] = "is_while_header";
 inline const char GRAPH_FLAG_ORDER_ENFORCE_SKIP[] = "order_enforce_skip";
 inline const char GRAPH_FLAG_BPROP_RETURN_SPARSE[] = "bprop_return_sparse";

mindspore/python/mindspore/numpy/utils_const.py

@@ -77,7 +77,7 @@ def _check_dtype(dtype):
 @constexpr
 def _is_shape_empty(shp):
     """Check whether shape contains zero"""
-    if shp is None:
+    if F.is_sequence_shape_unknown(shp):
         return False
     if isinstance(shp, int):
         return shp == 0

mindspore/python/mindspore/ops/composite/multitype_ops/_compile_utils.py

@@ -432,6 +432,8 @@ def tensor_index_by_list(data, list_index):
             if all(isinstance(i, bool) for i in list_index):
                 const_utils.raise_unimplemented_error(
                     "Not supported to the dynamic shape tensor slice by using list of Boolean type")
+            tensor_index = const_utils.sequence_to_index(list_index, None)
+        else:
             tensor_index = const_utils.sequence_to_index(list_index, data_shape[0])
         if tensor_index is False:
             const_utils.raise_index_error("When tensor is indexed by list, the list can't be empty.")
@@ -1109,6 +1111,8 @@ def format_list_indices(list_indices, length):
     # If eyery element in list is bool, it's treated as 1-D bool tensor.
     # If every element in list is int(not all bool), it's treated as int tensor.
     if const_utils.judge_indexes_types(indices_types, mstype.int_type + (mstype.bool_,)):
+        if not F.isconstant(length):
+            return const_utils.sequence_to_index(list_indices, None)
         return const_utils.sequence_to_index(list_indices, length)
     # If list contains other types(.../list/tuple/None), it's treated as a tuple
     return const_utils.deep_tuple(list_indices)

mindspore/python/mindspore/ops/composite/multitype_ops/_constexpr_utils.py

@@ -25,7 +25,6 @@ from mindspore.common._register_for_tensor import tensor_operator_registry
 from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
 from mindspore.ops.primitive import constexpr
-
 from mindspore import log as logger
 
 ALL_TENSOR = 0
@@ -166,6 +165,7 @@ def _deep_tensor_to_nparray(array_like):
 
 
 #TODO: remove comment
+#@constexpr(run_graph=False)
 @constexpr
 def check_range(x, dim_size):
     if dim_size is None:
@@ -634,12 +634,6 @@ def _judge_order_continuous(order_sequence):
 @constexpr
 def scalar_in_sequence(x, y):
     """Determine whether the scalar in the sequence."""
-    if x is None:
-        raise ValueError("Judge scalar in tuple or list require scalar and sequence must be constant, "
-                         "but the scalar is not.")
-    if y is None:
-        raise ValueError("Judge scalar in tuple or list require scalar and sequence must be constant, "
-                         "but the sequence is not.")
     return x in y
 
 

mindspore/python/mindspore/ops/function/sparse_func.py

@@ -32,6 +32,7 @@ from mindspore.common import dtype as mstype
 from mindspore.ops.primitive import constexpr, Primitive
 from mindspore.ops.operations.array_ops import GatherNd, Coalesce
 from mindspore.ops.operations import _csr_ops
+from mindspore.ops import functional as F
 from mindspore.common import CSRTensor, COOTensor, Tensor
 from mindspore.ops.composite.multitype_ops._constexpr_utils import raise_value_error, raise_type_error, make_tensor,\
     promote_binary_dtype
@@ -63,12 +64,11 @@ def _make_tensor_with_dtype(data, dtype):
     return Tensor(data, dtype=dtype)
 
 
-@constexpr
 def _convert_shape(shape):
     """Temporary solution to get shape value, will be removed when shape op is supported."""
-    if shape is None:
+    if F.is_sequence_shape_unknown(shape):
         return (-2,)
-    shape = [-1 if i is None else i for i in shape]
+    shape = [-1 if not F.isconstant(i) else i for i in shape]
     return tuple(shape)
 
 

mindspore/python/mindspore/ops/primitive.py

@@ -777,6 +777,7 @@ def constexpr(fn=None, get_instance=True, name=None, reuse_result=True, check=Tr
                 PrimitiveWithInfer.__init__(self, op_name)
                 self.set_const_prim(True)
                 self.fn = fn
+                self.add_prim_attr('constexpr_prim', True)
                 if not reuse_result:
                     self.add_prim_attr('forbid_reuse_result', True)
 
@@ -786,6 +787,7 @@ def constexpr(fn=None, get_instance=True, name=None, reuse_result=True, check=Tr
                     if (item["dtype"] is not None and item["value"] is None and check):
                         logger.warning("The \"" + self.name + "\" is a constexpr function." \
                                                               " The input arguments must be all constant value.")
+                        return {'dtype': None, 'shape': None, 'value': None, 'fn': (fn,)}
                     value_args.append(item["value"])
                 return {'dtype': None, 'shape': None, 'value': fn(*value_args)}
 

mindspore/python/mindspore/scipy/utils_const.py

@@ -16,6 +16,7 @@
 from __future__ import absolute_import
 from types import FunctionType
 from collections.abc import Iterable
+from mindspore.ops import functional as F
 from .. import context
 from ..ops.primitive import constexpr
 from ..common import Tensor, CSRTensor
@@ -29,13 +30,17 @@ def _callable_const(x):
 
 
 @constexpr
+def is_pynative():
+    return context.get_context("mode") == context.PYNATIVE_MODE
+
+
 def is_within_graph(x):
     """
     Returns true if x is None. It's aim to check whether the call is within MindSpore graph.
     Because in graph mode, x should be None in constexpr when x is a variable of MindSpore.
     Note that always return true if the call is in pynative mode.
     """
-    return context.get_context("mode") == context.PYNATIVE_MODE or x is None
+    return is_pynative() or not F.isconstant(x) or x is None
 
 
 @constexpr

tests/ut/python/dynamic_sequence/test_mutable_variable_length.py

@@ -19,7 +19,6 @@ from mindspore.ops import functional as F
 from mindspore import Tensor
 from mindspore import jit
 from mindspore import context
-from mindspore.ops.primitive import constexpr
 
 
 def test_generate_mutable_sequence_with_dynamic_length_with_jit():
@@ -137,33 +136,6 @@ def test_dynamic_length_sequence_length_sequence_value_shape_unknown_2():
     assert not ret2
 
 
-def test_dynamic_length_sequence_length_sequence_with_constexpr():
-    """
-    Feature: Mutable with dynamic length.
-    Description: Dynamic length sequence should be convert to None with passing to constexpr.
-    Expectation: No exception.
-    """
-    context.set_context(mode=context.GRAPH_MODE)
-
-    @constexpr
-    def test(x):
-        return x
-
-    @jit
-    def foo(x):
-        return test(x)
-
-    x = mutable([Tensor([1]), Tensor([2])], True)
-    ret = foo(x)
-    assert ret is None
-
-    y = mutable([Tensor([1]), Tensor([2])], False)
-    ret = foo(y)
-    assert len(ret) == 2
-    assert ret[0] is None
-    assert ret[1] is None
-
-
 def test_dynamic_length_sequence_getitem():
     """
     Feature: Mutable with dynamic length.