!28553 [Fallback] Deal with constants such as np.pi

Merge pull request !28553 from huangbingjian/attr_const

mindspore/ccsrc/pipeline/jit/parse/parse.cc

@@ -896,7 +896,18 @@ AnfNodePtr Parser::ParseAttribute(const FunctionBlockPtr &block, const py::objec
   MS_EXCEPTION_IF_NULL(block->func_graph());
   // Create the apply node
   auto attr_cnode = block->func_graph()->NewCNodeInOrder({op_node, value_node, attr_node});
-  UpdateInterpretForUserNode(attr_cnode, value_node);
+  // The fallback feature is enabled in default.
+  static const auto use_fallback = (support_fallback() != "0");
+  if (use_fallback) {
+    // Check whether it is constant, constant does not need interpret.
+    auto value_str = py::cast<std::string>(ast()->GetAstNodeText(value_body));
+    py::bool_ is_const_value =
+      ast()->CallParserObjMethod(PYTHON_PARSE_CHECK_IS_CONSTANT_VALUE, value_str, common::SafeCStr(attr_str));
+    auto is_constant = py::cast<bool>(is_const_value);
+    if (!is_constant) {
+      UpdateInterpretForUserNode(attr_cnode, value_node);
+    }
+  }
   return attr_cnode;
 }
 
@@ -1994,6 +2005,12 @@ void Parser::WriteAssignVars(const FunctionBlockPtr &block, const py::object &ta
 }
 
 void Parser::UpdateInterpretForUserNode(const AnfNodePtr &user_node, const AnfNodePtr &node) {
+  // The fallback feature is enabled in default.
+  static const auto use_fallback = (support_fallback() != "0");
+  if (!use_fallback) {
+    return;
+  }
+
   MS_EXCEPTION_IF_NULL(user_node);
   MS_EXCEPTION_IF_NULL(node);
   // Do not handle user node with internal type such as Tensor.abs().

mindspore/ccsrc/pipeline/jit/parse/parse_base.h

@@ -87,6 +87,7 @@ const char PYTHON_PARSE_GET_OPERATION_SYMBOL[] = "get_operation_symbol";
 const char PYTHON_PARSE_GET_OPERATION_NAMESPACE_SYMBOL[] = "get_operation_namespace_symbol";
 const char PYTHON_PARSE_GET_BUILTIN_NAMESPACE_SYMBOL[] = "get_builtin_namespace_symbol";
 const char PYTHON_PARSE_GET_LOCATION[] = "get_location";
+const char PYTHON_PARSE_CHECK_IS_CONSTANT_VALUE[] = "is_constant_value";
 const char PYTHON_PARSE_EXPAND_EXPR_STATEMENT[] = "expand_expr_statement";
 const char PYTHON_PARSE_GENERATE_SCOPE[] = "generate_scope";
 const char PYTHON_PARSE_GET_SCOPE_NAME[] = "get_scope_name";

mindspore/python/mindspore/_extends/parse/parser.py

@@ -639,6 +639,15 @@ class Parser:
         logger.error("Fn type is invalid")
         return None, None
 
+    def is_constant_value(self, var, attr):
+        if var in self.global_namespace:
+            module = self.global_namespace[var]
+            if hasattr(module, attr):
+                value = getattr(module, attr)
+                # Check if value is constant.
+                return isinstance(value, (int, float, bool))
+        return False
+
     def is_unsupported_namespace(self, value):
         unsupported = isinstance(value, _builtin_function_or_method_type) and value not in convert_object_map
         logger.debug(f"'{value}' unsupported: {unsupported}.")

tests/ut/python/fallback/test_graph_fallback.py

@@ -20,6 +20,7 @@ import mindspore.nn as nn
 from mindspore import Tensor, ms_function, context
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
+from mindspore.nn.probability import distribution
 import mindspore.common.dtype as mstype
 import mindspore.common._monad as monad
 
@@ -334,3 +335,34 @@ def test_self_method_2():
     net = Network()
     out = net()
     print(out)
+
+
+def test_probability_cauchy():
+    """
+    Feature: JIT Fallback
+    Description: NumPy method is called in probability cauchy.
+    Expectation: No exception.
+    """
+    class CauchyProb(nn.Cell):
+        def __init__(self, loc, scale, seed=10, dtype=mstype.float32, name='Cauchy'):
+            super().__init__()
+            self.b = distribution.Cauchy(loc, scale, seed, dtype, name)
+
+        def construct(self, value, loc=None, scale=None):
+            out1 = self.b.prob(value, loc, scale)
+            out2 = self.b.log_prob(value, loc, scale)
+            out3 = self.b.cdf(value, loc, scale)
+            out4 = self.b.log_cdf(value, loc, scale)
+            out5 = self.b.survival_function(value, loc, scale)
+            out6 = self.b.log_survival(value, loc, scale)
+            return out1, out2, out3, out4, out5, out6
+
+
+    loc = np.random.randn(1024, 512, 7, 7).astype(np.float32)
+    scale = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
+    loc_a = np.random.randn(1024, 512, 7, 7).astype(np.float32)
+    scale_a = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
+    value = np.random.randn(1024, 512, 7, 7).astype(np.float32)
+
+    net = CauchyProb(loc, scale)
+    net(Tensor(value), Tensor(loc_a), Tensor(scale_a))