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add check tensor when graph output is a const or parameter

This commit is contained in:
buxue 2020-08-28 16:36:14 +08:00
parent 135cfc6adf
commit 4168c51f0d
5 changed files with 35 additions and 26 deletions
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2
mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc
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@ -435,7 +435,7 @@ FuncGraphPtr DFunctor::KUserDefined(const FuncGraphPtr &primal) {
FuncGraphPtr bprop_graph = bprop->second.func_graph();
resources_->manager()->AddFuncGraph(bprop_graph);
if (bprop_graph->free_variables_nodes().size() != 0 || primal->free_variables_nodes().size() != 0) {
if (!bprop_graph->free_variables_nodes().empty() || !primal->free_variables_nodes().empty()) {
MS_LOG(EXCEPTION) << "User defined Cell bprop " << primal->ToString() << " in scope "
<< primal->output()->scope()->name() << " does not support Parameter data type.";
}

37
mindspore/ccsrc/pipeline/jit/pipeline.cc
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@ -99,7 +99,7 @@ py::tuple GenerateKey(const std::string &name, const std::unordered_map<std::str
MS_LOG(DEBUG) << "GenerateKey args size:" << defaults.size();
abstract::AbstractBasePtrList args_spec;
for (auto arg : defaults) {
for (const auto &arg : defaults) {
if (py::isinstance<py::module>(arg.second)) {
MS_LOG(EXCEPTION) << "GenerateKey failed, argument input should not be py::module";
}
@ -120,7 +120,7 @@ py::tuple GenerateKey(const std::string &name, const std::unordered_map<std::str
return argSpec;
}
py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple inputs) {
py::bool_ VerifyInputSignature(const py::list &input_signature, const py::tuple &inputs) {
MS_LOG(DEBUG) << "Verify args size:" << inputs.size();
if (inputs.size() != input_signature.size()) {
MS_LOG(ERROR) << "Signature size not equal to args size";
@ -131,12 +131,12 @@ py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple i
for (auto arg_obj : inputs) {
if (py::isinstance<Tensor>(arg_obj)) {
MS_LOG(DEBUG) << "Verify Tensor";
std::shared_ptr<Tensor> m_tensor = arg_obj.cast<std::shared_ptr<Tensor>>();
auto m_tensor = arg_obj.cast<std::shared_ptr<Tensor>>();
if (m_tensor == nullptr) {
MS_LOG(ERROR) << "Verify Tensor error, get ptr is null";
return false;
}
std::shared_ptr<MetaTensor> sig = input_signature[count].cast<std::shared_ptr<MetaTensor>>();
auto sig = input_signature[count].cast<std::shared_ptr<MetaTensor>>();
ShapeVector sig_shape = sig->shape();
TypePtr sig_type = sig->Dtype();
@ -301,16 +301,16 @@ std::map<std::string, std::pair<PrimitivePyPtr, std::string>> ExecutorPy::FetchI
MS_EXCEPTION_IF_NULL(func_graph);
MS_LOG(DEBUG) << "FetchInfoForQuantExport func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!";
std::map<std::string, std::pair<PrimitivePyPtr, std::string>> fake_quant_table;
auto filter = [](AnfNodePtr node) {
auto filter = [](const AnfNodePtr &node) {
return !(IsPrimitiveCNode(node, prim::kPrimConv2D) || IsPrimitiveCNode(node, prim::kPrimMatMul) ||
IsPrimitiveCNode(node, prim::kPrimDepthwiseConv2dNative));
};
std::vector<AnfNodePtr> nodes = DeepScopedGraphSearchWithFilter(func_graph->get_return(), AlwaysInclude, filter);
auto is_quant_cnode = [](AnfNodePtr node) {
auto is_quant_cnode = [](const AnfNodePtr &node) {
return IsPrimitiveCNode(node, prim::kPrimFakeQuantPerLayer) ||
IsPrimitiveCNode(node, prim::kPrimFakeQuantPerChannel);
};
for (auto node : nodes) {
for (const auto &node : nodes) {
auto cnode = node->cast<CNodePtr>();
if (cnode == nullptr || cnode->size() != 3) {
continue;
@ -756,11 +756,24 @@ py::object ExecutorPy::Run(const py::tuple &args, const py::object &phase) {
auto ret_val = std::make_shared<py::object>();
if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) {
if (IsGraphOutputValueNodeOrParameter(info_[phase_s]->func_graph->output(), args, ret_val)) {
// Check the input arg must be Tensor when backend is "ms".
if (MsContext::GetInstance()->backend_policy() == kMsConvert) {
for (std::size_t i = 0; i < size; i++) {
ValuePtr converted = nullptr;
if (!parse::ConvertData(args[i], &converted)) {
MS_LOG(EXCEPTION) << "The " << i << "th arg convert failed.";
}
if (!converted->isa<tensor::Tensor>()) {
MS_EXCEPTION(TypeError) << "The " << i << "th arg: " << converted->ToString() << " is not tensor.";
}
}
}
return *ret_val;
}
}
if (backend == "ge") {
if (args.size() > 0) {
// Virtual output constructed for test cases.
if (!args.empty()) {
return args[0];
}
return args;
@ -909,9 +922,9 @@ bool InitExecDatasetVm(const std::string &queue_name, int64_t size, int64_t batc
return true;
}
bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape, int64_t seed,
bool InitRandomNormal(float mean, float stddev, const std::vector<int64_t> &out_shape, int64_t seed,
const py::object &output_tensor) {
if (out_shape.size() == 0) {
if (out_shape.empty()) {
std::cout << "output data shape is error" << std::endl;
}
int64_t total_count = 1;
@ -923,14 +936,14 @@ bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape,
thread_num = 1;
}
auto temp = py::cast<std::shared_ptr<Tensor>>(output_tensor);
float *start_ptr = reinterpret_cast<float *>(temp->data_c());
auto *start_ptr = reinterpret_cast<float *>(temp->data_c());
if (start_ptr == nullptr) {
std::cout << "start_ptr is nullptr" << std::endl;
return false;
}
int64_t batchSize = total_count / thread_num;
std::vector<std::thread> threads(thread_num);
mindspore::kernel::PhiloxGenerator generator = mindspore::kernel::PhiloxGenerator(seed);
auto generator = mindspore::kernel::PhiloxGenerator(seed);
if (thread_num != 1) {
for (uint32_t i = 0; i < thread_num - 1; i++) {
float *offset_ptr = start_ptr + batchSize * i;

4
mindspore/ccsrc/pipeline/jit/pipeline.h
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@ -116,7 +116,7 @@ using ExecutorPyPtr = std::shared_ptr<ExecutorPy>;
// Generate a key for mapping function graph
py::tuple GenerateKey(const std::string &name, const std::unordered_map<std::string, py::object> &defaults);
py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple inputs);
py::bool_ VerifyInputSignature(const py::list &input_signature, const py::tuple &inputs);
bool InitDistribute(const std::map<std::string, std::string> &options);
@ -142,7 +142,7 @@ bool InitExecDatasetVm(const std::string &queue_name, int64_t size, int64_t batc
const std::vector<int64_t> &input_indexes, bool need_run);
// init random normal
bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> outshape, int64_t seed,
bool InitRandomNormal(float mean, float stddev, const std::vector<int64_t> &outshape, int64_t seed,
const py::object &outputTensor);
void ProcessVmArgInner(const py::tuple &args, const ResourcePtr &res, VectorRef *const arg_list);

6
tests/ut/python/pipeline/infer/test_net_infer.py
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@ -70,9 +70,9 @@ def test_assign_in_while():
def test_dup_context():
''' different func_with_fv in net1 and net2 should produce 2 different FuncGraphAbstractClosure and
""" different func_with_fv in net1 and net2 should produce 2 different FuncGraphAbstractClosure and
Evaluator.
'''
"""
context.set_context(mode=context.GRAPH_MODE)
class Net(nn.Cell):
@ -102,7 +102,7 @@ def test_dup_context():
def test_maybe_poly_func():
''' different func_with_fv in net1 and net2 may produce poly node. '''
""" different func_with_fv in net1 and net2 may produce poly node. """
context.set_context(mode=context.GRAPH_MODE)
class Net(nn.Cell):

12
tests/ut/python/nn/test_structure_output.py → tests/ut/python/pipeline/parse/test_structure_output.py
View File

@ -89,8 +89,7 @@ def test_output_parameter_tuple():
super(Net, self).__init__()
def construct(self, x):
ret = x
return ret
return x
x = (1, 2, 3)
net = Net()
@ -103,8 +102,7 @@ def test_output_parameter_list():
super(Net, self).__init__()
def construct(self, x):
ret = x
return ret
return x
x = [1, 2, 3]
net = Net()
@ -117,8 +115,7 @@ def test_output_parameter_int():
super(Net, self).__init__()
def construct(self, x):
ret = x
return ret
return x
x = 88
net = Net()
@ -131,8 +128,7 @@ def test_output_parameter_str():
super(Net, self).__init__()
def construct(self, x):
ret = x
return ret
return x
x = "hello world"
net = Net()