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!3880 fix auto mix precision issue in pynative 

Merge pull request !3880 from wangqiuliang/fix-gradients-issue-in-pynative
This commit is contained in:
mindspore-ci-bot 2020-08-03 19:23:03 +08:00 committed by Gitee
parent 952f96d860 577535b387
commit 27a0a2e333
3 changed files with 31 additions and 9 deletions
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30
mindspore/ccsrc/pipeline/pynative/pynative_execute.cc
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@ -316,7 +316,10 @@ OpExecInfoPtr GenerateOpExecInfo(const py::args &args, py::list *const out_args)
}
op_exec_info->py_primitive = prim;
op_exec_info->op_attrs = py::getattr(args[PY_PRIM], "attrs");
op_exec_info->value = PynativeExecutor::GetInstance()->GetForwardValue(op_exec_info);
auto inst = PynativeExecutor::GetInstance();
if (inst->grad_flag()) {
op_exec_info->value = inst->GetForwardValue(op_exec_info);
}
if (op_exec_info->op_inputs.size() != op_exec_info->inputs_mask.size()) {
MS_LOG(ERROR) << "Op:" << op_exec_info->op_name << " inputs size not equal op_mask";
return nullptr;
@ -1029,15 +1032,24 @@ std::vector<AnfNodePtr> PynativeExecutor::GetWeightsArgs(const py::object &weigh
AnfNodePtr para_node = nullptr;
if (graph_info_map_[df_builder_].param_map.count(param_id)) {
para_node = graph_info_map_[df_builder_].param_map[param_id];
AnfNodePtr value = parse::GetMixedPrecisionCastHelp(df_builder_, para_node);
AnfNodePtr make_ref = NewValueNode(prim::kPrimMakeRef);
auto refkey = std::make_shared<RefKey>(para_node->cast<ParameterPtr>()->name());
AnfNodePtr ref_key_node = NewValueNode(refkey);
AnfNodePtr ref_node = df_builder_->NewCNode({make_ref, ref_key_node, value, para_node});
w_args.push_back(ref_node);
} else {
auto name_attr = mindspore::parse::python_adapter::GetPyObjAttr(param, "name");
if (py::isinstance<py::none>(name_attr)) {
MS_LOG(EXCEPTION) << "Parameter object should have name attribute";
}
auto param_name = py::cast<std::string>(name_attr);
auto free_param = df_builder_->add_parameter();
free_param->set_name(param_name);
free_param->set_default_param(py::cast<tensor::TensorPtr>(param));
free_param->debug_info()->set_name(param_name);
para_node = free_param;
}
AnfNodePtr value = parse::GetMixedPrecisionCastHelp(df_builder_, para_node);
AnfNodePtr make_ref = NewValueNode(prim::kPrimMakeRef);
auto refkey = std::make_shared<RefKey>(para_node->cast<ParameterPtr>()->name());
AnfNodePtr ref_key_node = NewValueNode(refkey);
AnfNodePtr ref_node = df_builder_->NewCNode({make_ref, ref_key_node, value, para_node});
w_args.push_back(ref_node);
}
} else {
MS_LOG(DEBUG) << "training not paramter_tuple";

6
mindspore/common/tensor.py
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@ -185,6 +185,9 @@ class Tensor(Tensor_):
def __imod__(self, other):
return self.__mod__(other)
def __rmod__(self, other):
return tensor_operator_registry.get('__mod__')(other, self)
def __pow__(self, other):
return tensor_operator_registry.get('__pow__')(self, other)
@ -194,6 +197,9 @@ class Tensor(Tensor_):
def __ifloordiv__(self, other):
return self.__floordiv__(other)
def __rfloordiv__(self, other):
return tensor_operator_registry.get('__floordiv__')(other, self)
def __str__(self):
if self.dtype == mstype.type_none:
return "Unknown Tensor type!"

4
tests/ut/python/ir/test_tensor.py
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@ -472,3 +472,7 @@ def test_tensor_operation():
assert np.all(x.asnumpy() == np.ones((3, 3)))
with pytest.raises(ValueError):
res = x * (2, 3)
res = 5 % x
assert np.all(x.asnumpy() == np.ones((3, 3)))
res = 5 // x
assert np.all(x.asnumpy() == np.ones((3, 3)))