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make python Parameter inherit from Tensor

This commit is contained in:
Wei Luning 2020-07-29 14:14:47 +08:00
parent 2b56562770
commit a05c38bb63
46 changed files with 355 additions and 282 deletions
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4
mindspore/_extends/parse/__init__.py
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@ -21,12 +21,12 @@ from .parser import (Parser, create_obj_instance, generate_scope,
get_class_member_namespace_symbol, create_slice_obj,
get_dataclass_attributes, get_dataclass_methods, get_obj_id,
get_module_namespace, get_obj_type, get_object_key,
get_default_input, get_parse_method_of_class, get_scope_name,
get_parse_method_of_class, get_scope_name,
is_class_member, parse_cb, resolve_symbol)
from .serialize import *
__all__ = ['parse_cb', 'get_parse_method_of_class', 'get_bprop_method_of_class', 'resolve_symbol',
'get_object_key', 'get_default_input', 'get_class_instance_type', 'is_class_member',
'get_object_key', 'get_class_instance_type', 'is_class_member',
'get_obj_type', 'get_obj_id', 'create_obj_instance', 'get_module_namespace',
'get_class_member_namespace_symbol', 'get_obj_id', 'Parser', 'get_dataclass_attributes',
'get_dataclass_methods', 'dump_obj', 'load_obj', 'get_dataclass_methods', 'get_scope_name',

10
mindspore/_extends/parse/parser.py
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@ -206,16 +206,6 @@ def get_object_key(obj):
return obj_id, obj_key
def get_default_input(obj):
if hasattr(obj, '__parameter__'):
return obj.default_input
if isinstance(obj, tuple):
convert = lambda x: x.default_input if hasattr(x, '__parameter__') else x
args = tuple(convert(x) for x in obj)
return args
return obj
def is_class_member(node):
"""Check the attr is class member variable."""
type_ = node.__class__.__name__

2
mindspore/ccsrc/backend/session/ascend_inference_session.cc
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@ -76,7 +76,7 @@ GraphId AscendInferenceSession::CompileGraph(NotNull<FuncGraphPtr> func_graph) {
if (AnfAlgo::IsParameterWeight(pk_node)) {
const auto &param_value = pk_node->default_param();
MS_EXCEPTION_IF_NULL(param_value);
auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_value->value());
auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_value);
MS_EXCEPTION_IF_NULL(tensor);
if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(pk_node, 0),
LongToSize(tensor->data().nbytes()), tensor->data_type(),

11
mindspore/ccsrc/backend/session/session_basic.cc
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@ -42,12 +42,12 @@
namespace mindspore {
namespace session {
static std::shared_ptr<std::map<ParamValuePtr, ParameterPtr>> python_paras;
static std::shared_ptr<std::map<ValuePtr, ParameterPtr>> python_paras;
void ClearPythonParasMap() { python_paras = nullptr; }
namespace {
const int kSummaryGetItem = 2;
ParamValuePtr GetParamDefaultValue(const AnfNodePtr &node) {
ValuePtr GetParamDefaultValue(const AnfNodePtr &node) {
if (node == nullptr) {
return nullptr;
}
@ -209,8 +209,7 @@ ParameterPtr ConstructRunOpParameter(const std::shared_ptr<KernelGraph> &graph,
auto param = graph->NewParameter();
MS_EXCEPTION_IF_NULL(param);
if (tensor_mask == kParameterWeightTensorMask) {
auto param_value_new = std::make_shared<ParamValue>();
param->set_default_param(param_value_new);
param->set_default_param(input_tensor);
}
// set the kernel info of parameter
auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
@ -390,7 +389,7 @@ ParameterPtr SessionBasic::CreateNewParameterFromParameter(const AnfNodePtr &anf
ParameterPtr new_parameter = nullptr;
// if parameter's python parameter has been exist a backend parameter, reuse the exist parameter
if (python_paras == nullptr) {
python_paras = std::make_shared<std::map<ParamValuePtr, ParameterPtr>>();
python_paras = std::make_shared<std::map<ValuePtr, ParameterPtr>>();
}
auto iter = python_paras->find(param_value);
if (iter != python_paras->end()) {
@ -667,7 +666,7 @@ ParameterPtr SessionBasic::CreateNewParameter(const AnfNodePtr &anf, KernelGraph
auto param_value = GetParamDefaultValue(anf);
ParameterPtr new_parameter = nullptr;
if (python_paras == nullptr) {
python_paras = std::make_shared<std::map<ParamValuePtr, ParameterPtr>>();
python_paras = std::make_shared<std::map<ValuePtr, ParameterPtr>>();
}
auto iter = python_paras->find(param_value);
if (iter != python_paras->end()) {

2
mindspore/ccsrc/debug/anf_ir_utils.cc
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@ -1670,7 +1670,7 @@ class IrParser {
// load parameter default value from serialized file
py::object default_obj = LoadObject(lexer_.GetTokenText());
auto param_value_new = py::cast<ParamValuePtr>(default_obj);
auto param_value_new = py::cast<tensor::TensorPtr>(default_obj);
param->set_default_param(param_value_new);
tok = lexer_.GetNextToken();

5
mindspore/ccsrc/debug/draw.cc
View File

@ -318,8 +318,9 @@ void BaseDigraph::FuncGraphParameters(const FuncGraphPtr &key) {
buffer_ << parameter->ToString();
auto param = parameter->cast<ParameterPtr>();
if (param->has_default()) {
auto tensor = param->default_param()->value();
if (tensor) {
auto tensor_v = param->default_param();
if (tensor_v && tensor_v->isa<tensor::Tensor>()) {
auto tensor = tensor_v->cast<tensor::TensorPtr>();
auto &shape = tensor->shape();
std::ostringstream shape_str;
std::copy(shape.begin(), shape.end(), std::ostream_iterator<int>(shape_str, ","));

7
mindspore/ccsrc/frontend/parallel/graph_util/node_info.cc
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@ -38,7 +38,12 @@ bool ParameterRequireGrad(const AnfNodePtr &node_ptr) {
if (!para_ptr->has_default()) {
return false;
}
return para_ptr->default_param()->requires_grad();
auto obj = py::cast(para_ptr->default_param());
auto param_value = py::cast<ParamValuePtr>(obj.attr("_value"));
if (param_value == nullptr) {
return false;
}
return param_value->requires_grad();
}
} // namespace parallel
} // namespace mindspore

15
mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc
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@ -41,6 +41,7 @@
#include "frontend/parallel/context.h"
#include "frontend/parallel/ops_info/tmp_identity_info.h"
#include "frontend/parallel/ops_info/reshape_info.h"
#include "frontend/parallel/graph_util/node_info.h"
#include "frontend/parallel/step_parallel.h"
#include "frontend/parallel/strategy_checkpoint/parallel_strategy_checkpoint.h"
#include "pipeline/jit/parse/python_adapter.h"
@ -122,12 +123,7 @@ std::vector<bool> ExtractInputParameterByNode(const CNodePtr &node) {
if (input->isa<Parameter>()) {
auto input_parameter = input->cast<ParameterPtr>();
if (input_parameter->has_default()) {
bool requires_grad = input_parameter->default_param()->requires_grad();
is_parameter.push_back(requires_grad);
} else {
is_parameter.push_back(false);
}
is_parameter.push_back(ParameterRequireGrad(input_parameter));
} else if (input->isa<CNode>() || IsValueNode<tensor::Tensor>(input) || IsValueNode<RefKey>(input)) {
is_parameter.push_back(false);
}
@ -798,12 +794,7 @@ void AugmentCostGraph(const std::vector<AnfNodePtr> &all_nodes) {
std::vector<bool> is_parameter;
auto casted_target_parameter = target_parameter->cast<ParameterPtr>();
MS_EXCEPTION_IF_NULL(casted_target_parameter);
if (casted_target_parameter->has_default()) {
bool requires_grad = casted_target_parameter->default_param()->requires_grad();
is_parameter.push_back(requires_grad);
} else {
is_parameter.push_back(false);
}
is_parameter.push_back(ParameterRequireGrad(casted_target_parameter));
if (tmp_identity_ptr->set_is_parameter(is_parameter) != SUCCESS) {
MS_LOG(EXCEPTION) << "Setting parameter for TmpIdentityInfo failed";
}

38
mindspore/ccsrc/frontend/parallel/step_parallel.cc
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@ -1295,11 +1295,8 @@ void CoverSliceShape(const FuncGraphPtr &root) {
g_RefMap.clear();
}
bool ParameterIsCloned(const FuncGraphPtr &root, const AnfNodePtr &parameter_node) {
MS_EXCEPTION_IF_NULL(root);
bool ParameterIsCloned(const AnfNodePtr &parameter_node) {
MS_EXCEPTION_IF_NULL(parameter_node);
FuncGraphManagerPtr manager = root->manager();
MS_EXCEPTION_IF_NULL(manager);
auto cloned_parameter = parameter_node->cast<ParameterPtr>();
MS_EXCEPTION_IF_NULL(cloned_parameter);
@ -1307,8 +1304,12 @@ bool ParameterIsCloned(const FuncGraphPtr &root, const AnfNodePtr &parameter_nod
if (!cloned_parameter->has_default()) {
return false;
}
bool cloned = cloned_parameter->default_param()->cloned();
auto obj = py::cast(cloned_parameter->default_param());
auto param_value = py::cast<ParamValuePtr>(obj.attr("_value"));
if (param_value == nullptr) {
return false;
}
bool cloned = param_value->cloned();
if (!cloned) {
return false;
}
@ -1324,12 +1325,16 @@ void SetClonedTensorShapeForOptimizer(const FuncGraphPtr &root) {
auto cloned_parameter = cloned_parameter_node->cast<ParameterPtr>();
MS_EXCEPTION_IF_NULL(cloned_parameter);
if (!ParameterIsCloned(root, cloned_parameter_node)) {
if (!ParameterIsCloned(cloned_parameter_node)) {
continue;
}
auto obj = py::cast(cloned_parameter->default_param());
auto param_value = py::cast<ParamValuePtr>(obj.attr("_value"));
if (param_value == nullptr) {
continue;
}
// get the cloned index
int32_t cloned_index = cloned_parameter->default_param()->cloned_index();
int32_t cloned_index = param_value->cloned_index();
// find the be cloned parameter
bool found_be_cloned_parameter = false;
@ -1344,12 +1349,18 @@ void SetClonedTensorShapeForOptimizer(const FuncGraphPtr &root) {
}
const auto &param_value_cloned = be_cloned_parameter->default_param();
if (!param_value_cloned->be_cloned()) {
auto obj_in = py::cast(param_value_cloned);
auto param_value_in = py::cast<ParamValuePtr>(obj_in.attr("_value"));
if (param_value_in == nullptr) {
continue;
}
if (!param_value_in->be_cloned()) {
continue;
}
// get the be cloned index
auto &be_cloned_index = param_value_cloned->be_cloned_index();
auto &be_cloned_index = param_value_in->be_cloned_index();
if (std::find(be_cloned_index.begin(), be_cloned_index.end(), cloned_index) != be_cloned_index.end()) {
found_be_cloned_parameter = true;
cloned_from_parameter = be_cloned_parameter;
@ -2103,10 +2114,7 @@ std::string NodeParameterName(const CNodePtr &node) {
if (input->isa<Parameter>()) {
auto input_parameter = input->cast<ParameterPtr>();
if (input_parameter->has_default()) {
const auto &param_value = input_parameter->default_param();
if (param_value->requires_grad()) {
return param_value->name();
}
input_parameter->name();
}
}
}

3
mindspore/ccsrc/pipeline/jit/action.cc
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@ -233,8 +233,7 @@ bool AbstractSpecializeAction(const ResourcePtr &res) {
for (const auto &param : func_graph->parameters()) {
auto param_node = std::static_pointer_cast<Parameter>(param);
if (param_node->has_default()) {
const auto &param_value = param_node->default_param();
ValuePtr value = param_value->value();
ValuePtr value = param_node->default_param();
constexpr bool broaden = true;
AbstractBasePtr ptr = abstract::FromValue(value, broaden);

2
mindspore/ccsrc/pipeline/jit/init.cc
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@ -68,6 +68,8 @@ PYBIND11_MODULE(_c_expression, m) {
py::arg("type") = py::str("onnx_ir"), "Get graph proto string by specifying ir type.")
.def("compile", &ExecutorPy::Compile, py::arg("obj"), py::arg("args"), py::arg("phase") = py::str(""),
py::arg("use_vm") = py::bool_(false), "Compile obj by executor.")
.def("updata_param_node_default_input", &ExecutorPy::UpdataParamNodeDefaultInput, py::arg("phase"),
py::arg("params"), "Fetch the inputs of Conv or Matmul for quant export.")
.def("get_parameter_layout", &ExecutorPy::GetParameterLayout, py::arg("phase") = py::str("train"),
"Get Parameter Tensor Layout Dictionary.")
.def("get_strategy", &ExecutorPy::GetCNodeStrategy, py::arg("phase") = py::str("train"),

41
mindspore/ccsrc/pipeline/jit/parse/data_converter.cc
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@ -205,41 +205,6 @@ bool ConvertMetaFuncGraph(const py::object &obj, ValuePtr *const data, bool use_
return true;
}
bool ConvertDataType(const py::object &obj, ValuePtr *const data) {
MS_LOG(DEBUG) << "Converting type object";
auto typeptr = obj.cast<TypePtr>();
if (typeptr == nullptr) {
MS_LOG(ERROR) << "Resolve TypePtr error, get ptr is null";
return false;
}
*data = typeptr;
return true;
}
bool ConvertMetaTensor(const py::object &obj, ValuePtr *const data) {
MS_LOG(DEBUG) << "Converting MetaTensor object.";
auto m_tensor = obj.cast<MetaTensorPtr>();
if (m_tensor == nullptr) {
MS_LOG(ERROR) << "Resolve MetaTensor error, get ptr is null.";
return false;
}
*data = m_tensor;
return true;
}
bool ConvertTensor(const py::object &obj, ValuePtr *const data) {
MS_LOG(DEBUG) << "Converting tensor object";
auto m_tensor = obj.cast<TensorPtr>();
if (m_tensor == nullptr) {
MS_LOG(ERROR) << "Resolve Tensor error, get ptr is null";
return false;
}
*data = m_tensor;
return true;
}
bool ConvertSlice(const py::object &obj, ValuePtr *const data) {
MS_LOG(DEBUG) << "Converting slice object";
@ -364,11 +329,11 @@ bool ConvertData(const py::object &obj, ValuePtr *const data, bool use_signature
} else if (py::isinstance<MetaFuncGraph>(obj)) {
ret = ConvertMetaFuncGraph(obj, &converted, use_signature);
} else if (py::isinstance<Type>(obj)) {
ret = ConvertDataType(obj, &converted);
converted = obj.cast<TypePtr>();
} else if (py::isinstance<Tensor>(obj)) {
ret = ConvertTensor(obj, &converted);
converted = obj.cast<TensorPtr>();
} else if (py::isinstance<MetaTensor>(obj)) {
ret = ConvertMetaTensor(obj, &converted);
converted = obj.cast<MetaTensorPtr>();
} else if (py::isinstance<EnvInstance>(obj)) {
std::shared_ptr<EnvInstance> env = obj.cast<std::shared_ptr<EnvInstance>>();
converted = env;

1
mindspore/ccsrc/pipeline/jit/parse/parse_base.h
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@ -85,7 +85,6 @@ const char PYTHON_PARSE_ANALYZE_SUPER[] = "analyze_super";
const char PYTHON_PARSE_CLASS_SLICE[] = "create_slice_obj";
const char PYTHON_PARSE_CLASS_ELLIPSIS[] = "create_ellipsis_obj";
const char PYTHON_MOD_GET_DEFAULT_INPUT[] = "get_default_input";
// define the common name
const char NAMED_PRIMITIVE_LEN[] = "len";

5
mindspore/ccsrc/pipeline/jit/parse/resolve.cc
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@ -103,10 +103,9 @@ AnfNodePtr ResolveParameterObj(const FuncGraphPtr &func_graph, const py::object
}
if (para_node == nullptr) {
auto node = top_graph->AddWeightParameter(param_name);
auto param_value = py::cast<ParamValuePtr>(python_adapter::GetPyObjAttr(obj, "_value"));
node->set_default_param(param_value);
auto value = py::cast<tensor::MetaTensorPtr>(obj);
node->set_default_param(value);
// set_abstract for parameter
ValuePtr value = param_value->value();
constexpr bool broaden = true;
node->set_abstract(abstract::FromValue(value, broaden));
para_node = node;

24
mindspore/ccsrc/pipeline/jit/pipeline.cc
View File

@ -719,7 +719,11 @@ void ProcessVmArgInner(const py::tuple &args, const ResourcePtr &res, VectorRef
if (!param_ptr->has_default()) {
MS_LOG(EXCEPTION) << "Parameter[" << i << "] has no default param";
}
arg_list->push_back(param_ptr->default_param()->value());
if (!param_ptr->default_param()->isa<Tensor>()) {
MS_LOG(EXCEPTION) << "Parameter[" << param_ptr->ToString()
<< "] is not initialized, need to call `.init_data()`";
}
arg_list->push_back(param_ptr->default_param());
}
}
}
@ -782,6 +786,24 @@ FuncGraphPtr ExecutorPy::BuildGraph(const py::dict &init_params, const std::stri
#endif
}
void ExecutorPy::UpdataParamNodeDefaultInput(const std::string &phase,
const std::unordered_map<std::string, tensor::TensorPtr> &params_value) {
FuncGraphPtr func_graph = info_[phase]->resource->func_graph();
MS_EXCEPTION_IF_NULL(func_graph);
MS_LOG(DEBUG) << "UpdataParamNodeDefaultInput for func graph(" << func_graph->ToString() << ") phase(" << phase
<< ")!";
auto &params = func_graph->parameters();
for (const auto &param : params) {
MS_EXCEPTION_IF_NULL(param);
auto param_cast = param->cast<ParameterPtr>();
MS_EXCEPTION_IF_NULL(param_cast);
auto iter = params_value.find(param_cast->name());
if (iter != params_value.end()) {
param_cast->set_default_param(iter->second);
}
}
}
void ExecutorPy::RunInitGraph(const py::dict &init_params, const std::string &phase) {
#if ENABLE_GE
RunGEInitGraph(init_params, phase);

2
mindspore/ccsrc/pipeline/jit/pipeline.h
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@ -88,6 +88,8 @@ class ExecutorPy : public std::enable_shared_from_this<ExecutorPy> {
FuncGraphPtr BuildGraph(const py::dict &init_params, const std::string &phase,
const py::object &broadcast_params = {});
void UpdataParamNodeDefaultInput(const std::string &phase,
const std::unordered_map<std::string, tensor::TensorPtr> &params);
void RunInitGraph(const py::dict &init_params, const std::string &phase);
py::dict GetParameterLayout(const std::string &phase);
py::dict GetCNodeStrategy(const std::string &phase);

17
mindspore/ccsrc/pipeline/pynative/pynative_execute.cc
View File

@ -146,12 +146,6 @@ static std::string GetOpId(const OpExecInfoPtr &op_exec_info) {
return id;
}
py::object GetTupleObj(const py::object &obj) {
py::module mod = parse::python_adapter::GetPyModule(parse::PYTHON_MOD_PARSE_MODULE);
py::object obj_tuple = parse::python_adapter::CallPyModFn(mod, parse::PYTHON_MOD_GET_DEFAULT_INPUT, obj);
return obj_tuple;
}
std::map<SignatureEnumDType, std::vector<size_t>> GetTypeIndex(const std::vector<SignatureEnumDType> &dtypes) {
std::map<SignatureEnumDType, std::vector<size_t>> type_indexes;
for (size_t i = 0; i < dtypes.size(); ++i) {
@ -242,7 +236,7 @@ py::tuple ConvertInputs(const PrimitivePyPtr &prim, const py::list &args, py::tu
py::tuple input_mask(args.size());
for (size_t i = 0; i < args.size(); ++i) {
input_mask[i] = py::hasattr(args[i], "__parameter__");
py_args[i] = GetTupleObj(args[i]);
py_args[i] = args[i];
}
auto signature = prim->signatures();
std::vector<SignatureEnumDType> dtypes;
@ -366,9 +360,6 @@ py::object RunOpInVM(const OpExecInfoPtr &op_exec_info, PynativeStatusCode *stat
py::tuple result(op_inputs.size());
for (size_t i = 0; i < op_inputs.size(); i++) {
py::object input = op_inputs[i];
if (py::hasattr(input, "__parameter__")) {
input = py::getattr(input, "data");
}
auto tensor = py::cast<tensor::TensorPtr>(input);
auto new_tensor = std::make_shared<tensor::Tensor>(tensor->data_type(), tensor->shape(), tensor->data_ptr());
new_tensor->set_device_address(tensor->device_address());
@ -878,8 +869,7 @@ AnfNodePtr PynativeExecutor::GetInput(const py::object &obj, const py::object &o
if (graph_info_map_[df_builder_].param_map.count(obj_id) == 0) {
auto free_param = df_builder_->add_parameter();
free_param->set_name(param_name);
auto free_param_new = py::cast<ParamValuePtr>(obj.attr("_value"));
free_param->set_default_param(free_param_new);
free_param->set_default_param(py::cast<tensor::TensorPtr>(obj));
free_param->debug_info()->set_name(param_name);
MS_LOG(DEBUG) << "Top graph set free parameter " << obj_id;
graph_info_map_[df_builder_].param_map[obj_id] = free_param;
@ -1074,8 +1064,7 @@ abstract::AbstractBasePtrList PynativeExecutor::GetArgsSpec(const py::args &args
for (const auto &param : df_builder_->parameters()) {
auto param_node = std::static_pointer_cast<Parameter>(param);
if (param_node->has_default()) {
const auto &param_value = param_node->default_param();
ValuePtr value = param_value->value();
ValuePtr value = param_node->default_param();
AbstractBasePtr ptr = abstract::FromValue(value, true);
if (ptr == nullptr) {
MS_LOG(EXCEPTION) << "Args convert error";

2
mindspore/ccsrc/transform/onnx/ir_exporter.cc
View File

@ -187,7 +187,7 @@ void IrExportBuilder::BuildParameters(const FuncGraphPtr &func_graph, onnx::Grap
onnx::TensorProto *initializer_proto = graph_proto->add_initializer();
initializer_proto->set_name(param_name);
SetParamToTensorProto(param, initializer_proto);
auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param->default_param()->value());
auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param->default_param());
if (tensor) {
initializer_proto->set_raw_data(tensor->data_c(), tensor->data().nbytes());
}

2
mindspore/ccsrc/transform/onnx/onnx_exporter.cc
View File

@ -449,7 +449,7 @@ void OnnxExporter::ExportParameters(const FuncGraphPtr &func_graph, onnx::GraphP
initializer_proto->set_name(param_ptr->ToString());
SetTensorProtoInfo(param_ptr, initializer_proto);
// set value for initializer
auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_ptr->default_param()->value());
auto tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_ptr->default_param());
if (tensor) {
initializer_proto->set_raw_data(tensor->data_c(), tensor->data().nbytes());
}

2
mindspore/ccsrc/utils/callbacks_ge.cc
View File

@ -52,7 +52,7 @@ bool GetParameterShape(const FuncGraphPtr &graph, const std::string &param_name,
if (param_node->name() == param_name) {
TensorPtr tensor;
if (param_node->has_default()) {
tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_node->default_param()->value());
tensor = std::dynamic_pointer_cast<tensor::Tensor>(param_node->default_param());
}
if (tensor == nullptr) {
shape->push_back(ONE_SHAPE);

2
mindspore/ccsrc/utils/convert_utils.cc
View File

@ -448,7 +448,7 @@ bool IsGraphOutputValueNodeOrParameter(const AnfNodePtr &output, const py::tuple
if (!param->has_default()) {
MS_LOG(EXCEPTION) << "Can not determine value of Parameter " << index << " (" << param->name() << ")";
}
auto tensor = param->default_param()->value();
auto tensor = param->default_param();
*ret_val = py::cast(tensor);
}
return true;

5
mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc
View File

@ -124,10 +124,7 @@ bool MSANFModelParser::BuildParameterForFuncGraph(const ParameterPtr &node, cons
MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret;
}
auto param_value = std::make_shared<ParamValue>();
MS_EXCEPTION_IF_NULL(param_value);
param_value->set_value(tensor_info);
node->set_default_param(param_value);
node->set_default_param(tensor_info);
}
anfnode_build_map_[value_proto.name()] = node;
return true;

5
mindspore/ccsrc/utils/param_value_py.cc
View File

@ -24,22 +24,19 @@ REGISTER_PYBIND_DEFINE(ParamValue, ([](const py::module *m) {
(void)py::class_<ParamValue, ParamValuePtr>(*m, "ParamValue")
.def(py::init())
.def("clone", &ParamValue::Clone)
.def_property("data", &ParamValue::value, &ParamValue::set_value)
.def_property("name", &ParamValue::name, &ParamValue::set_name)
.def_property("requires_grad", &ParamValue::requires_grad, &ParamValue::set_requires_grad)
.def_property("layerwise_parallel", &ParamValue::layerwise_parallel,
&ParamValue::set_layerwise_parallel)
.def(py::pickle(
[](const ParamValue &p) { // __getstate__
return py::make_tuple(py::cast(p.value()), p.name(), p.requires_grad(),
p.layerwise_parallel());
return py::make_tuple(p.name(), p.requires_grad(), p.layerwise_parallel());
},
[](const py::tuple &t) { // __setstate__
if (t.size() != 6) {
std::runtime_error("Invalid state for ParamValue!");
}
ParamValuePtr p = std::make_shared<ParamValue>();
p->set_value(t[0].cast<tensor::TensorPtr>());
p->set_name(t[1].cast<std::string>());
p->set_requires_grad(t[2].cast<bool>());
p->set_layerwise_parallel(t[3].cast<bool>());

2
mindspore/ccsrc/utils/tensor_py.cc
View File

@ -372,7 +372,7 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) {
.def(py::pickle(
[](const Tensor &t) { // __getstate__
/* Return a tuple that fully encodes the state of the object */
return py::make_tuple(TensorPy::AsNumpy(t));
return py::make_tuple(TensorPy::SyncAsNumpy(t));
},
[](const py::tuple &t) { // __setstate__
if (t.size() != 1) {

42
mindspore/common/api.py
View File

@ -255,7 +255,6 @@ def ms_function(fn=None, obj=None, input_signature=None):
process_obj = obj
if args and not isinstance(args[0], MsTensor) and hasattr(args[0], func.__name__):
process_obj = args[0]
args = (x.default_input if hasattr(x, 'default_input') else x for x in args)
return _MindSporeFunction(func, input_signature, process_obj)(*args)
return staging_specialize
@ -354,28 +353,8 @@ class _Executor:
raise RuntimeError("Failure to init and dataset subgraph!")
return True
def _build_data_graph(self, obj, params, phase):
if params is None:
self._executor.build_data_graph(obj.parameters_dict(), phase, obj.parameters_broadcast_dict())
elif isinstance(params, OrderedDict):
self._executor.build_data_graph(params, phase)
else:
raise TypeError('Parameters need OrderedDict type, but got {}'.
format(type(params)))
def _params_init_data(self, obj, params, auto_parallel_mode=False):
"""Init parameters' data."""
if params is not None:
for key, param in params.items():
if not auto_parallel_mode:
param.init_data()
elif key not in obj.parameter_layout_dict:
logger.debug("Layout dict does not contain the key %s.", key)
param.init_data(set_sliced=True)
else:
layout = obj.parameter_layout_dict[key]
param.init_data(layout, set_sliced=True)
obj.init_parameters_data(auto_parallel_mode=auto_parallel_mode)
def _build_data_graph(self, obj, phase):
self._executor.build_data_graph(obj.parameters_dict(), phase, obj.parameters_broadcast_dict())
def _set_dataset_mode(self, args_list):
"""set dataset mode."""
@ -386,7 +365,7 @@ class _Executor:
else:
_set_dataset_mode_config('normal')
def compile(self, obj, *args, phase='predict', params=None, do_convert=True, auto_parallel_mode=False):
def compile(self, obj, *args, phase='predict', do_convert=True, auto_parallel_mode=False):
"""
Compiles graph.
@ -394,7 +373,6 @@ class _Executor:
obj (Function/Cell): The function or cell instance need compile.
args (tuple): Function or cell input arguments.
phase (str): The name of compile phase. Default: 'predict'.
params (OrderedDict): The parameters dictionary used for init data graph. Default: None.
do_convert (bool): When set to True, convert ME graph to GE graph after compiling graph.
auto_parallel_mode: When set to True, use auto parallel mode to compile graph.
@ -435,10 +413,8 @@ class _Executor:
if auto_parallel_mode:
obj.parameter_layout_dict = self._executor.get_parameter_layout(phase)
self._params_init_data(obj, params, auto_parallel_mode)
if not enable_debug_runtime or enable_ge:
if auto_parallel_mode:
obj.load_parameter_slice(params)
replace = obj.init_parameters_data(auto_parallel_mode=auto_parallel_mode)
self._updata_param_node_default_input(phase, replace)
# set parallel inputs in sink mode
if auto_parallel_mode and (args and isinstance(args[0], Tensor) and args[0].virtual_flag):
@ -446,16 +422,20 @@ class _Executor:
# the following GE init process is not needed when use vm or ms backend
if enable_ge:
self._build_data_graph(obj, params, phase)
self._build_data_graph(obj, phase)
if "export" not in phase:
init_phase = "init_subgraph" + "." + str(obj.create_time)
_exec_init_graph(obj, init_phase)
elif not enable_ge and "export" in phase:
self._build_data_graph(obj, params, phase)
self._build_data_graph(obj, phase)
return phase, True
def _updata_param_node_default_input(self, phase, replace):
new_param = {x.name: replace[x] for x in replace if id(x) != id(replace[x])}
return self._executor.updata_param_node_default_input(phase, new_param)
def _get_strategy(self, obj):
real_phase = self.phase_prefix + obj.phase + '.' + str(obj.create_time)
return self._executor.get_strategy(real_phase)

220
mindspore/common/parameter.py
View File

@ -14,7 +14,6 @@
# ============================================================================
"""Parameter for cell."""
import numbers
from copy import copy
from mindspore import context
from .._c_expression import ParamValue
@ -37,10 +36,17 @@ def _check_type(x):
return True
class Parameter:
class Parameter(MetaTensor):
"""
Parameter types of cell models.
After initialized `Parameter` is a subtype of `Tensor`.
In graph mode, if init `Parameter` by a `Initializer`, the type of Parameter will be a `MetaTensor`
not a `Tensor`. `MetaTensor` only save the shape type info of a tensor with no memory usage. The shape
can be change while compile for auto-parallel. Call `init_data` will return a Tensor Parameter with
initialized data.
Note:
Each parameter of Cell is represented by Parameter class.
@ -52,23 +58,85 @@ class Parameter:
layerwise_parallel (bool): A kind of model parallel mode. When layerwise_parallel is true in paralle mode,
broadcast and gradients communication would not be applied on parameters. Default: False.
"""
__base_type__ = {}
def __new__(cls, default_input, name, *args, **kwargs):
input_class, *class_init_args = Parameter._get_parameter_new_args(default_input)
new_type = Parameter._get_base_class(input_class)
obj = input_class.__new__(new_type)
input_class.__init__(obj, *class_init_args)
# it's better to make the Initializer a kind of metatensor.
obj.init_mode = None
if isinstance(default_input, Initializer):
obj.init_mode = default_input
return obj
def __reduce_ex__(self, _):
data = self
if self.init_mode is not None:
data = self.init_mode
else:
# cast to break deep infinit loop while deepcopy
data = Tensor(self)
return (
Parameter, (data, self.name, self.requires_grad, self.layerwise_parallel))
def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False):
self._value = ParamValue()
self.set_parameter_data(default_input)
self.name = name
self.requires_grad = requires_grad
self.layerwise_parallel = layerwise_parallel
# this flag for tensor copy data.
self.init_flag = False
# this flag is for ge variable copy data.
self._is_init = False
self._inited_param = None
self._sliced = False
self.is_param_ps = False
self._cast_type = None
self.init_in_server = False
if context.get_context("mode") == context.PYNATIVE_MODE:
self.init_data()
@staticmethod
def _get_base_class(input_class):
input_class_name = f'Parameter{input_class.__name__}'
if input_class_name in Parameter.__base_type__:
new_type = Parameter.__base_type__[input_class_name]
else:
new_type = type(input_class_name, (Parameter, input_class), {})
Parameter.__base_type__[input_class_name] = new_type
return new_type
@staticmethod
def _get_parameter_new_args(data):
"""Set `default_input` of current `Parameter`."""
if isinstance(data, bool):
raise ValueError('Parameter data can not be `bool`')
if isinstance(data, Initializer):
if context.get_context("mode") == context.PYNATIVE_MODE:
# always init data while in pynative mode.
data = data.to_tensor()
else:
return (MetaTensor, data.dtype, data.shape)
if isinstance(data, Tensor):
# make a copy of Tensor to init the parameter
return (Tensor, data.asnumpy(),)
if isinstance(data, int):
return (Tensor, data, mstype.int32)
if isinstance(data, float):
return (Tensor, data, mstype.float32)
return (Tensor, data)
def __str__(self):
value_str = MetaTensor.__repr__(self)
if isinstance(self, Tensor):
value_str = Tensor.__repr__(self)
return f'Parameter (name={self._value.name}, value={value_str})'
def __repr__(self):
format_str = 'Parameter (name={name})'
return format_str.format(name=self._value.name)
value_str = MetaTensor.__repr__(self)
if isinstance(self, Tensor):
value_str = Tensor.__repr__(self)
return f'Parameter (name={self._value.name}, value={value_str})'
def __parameter__(self):
"""For parse check."""
@ -77,6 +145,13 @@ class Parameter:
self.is_param_ps = True
self.init_in_server = init_in_server
@property
def inited_param(self):
"""Get the new parameter after call the init_data."""
return self._inited_param
@property
def name(self):
"""Get the name of the parameter."""
@ -157,15 +232,11 @@ class Parameter:
x._value.name = prefix + '.' + self._value.name
x.is_init = False
if init != 'same':
shape = self.default_input.shape
dtype = self.default_input.dtype
if isinstance(init, (str, Initializer, numbers.Number)):
x.init_mode = initializer(init, shape=shape, dtype=dtype)
x.default_input = MetaTensor(dtype, shape)
if context.get_context("mode") == context.PYNATIVE_MODE:
x.init_data()
else:
x.default_input = initializer(init, shape=shape, dtype=dtype)
shape = self.shape
dtype = self.dtype
x.default_input = initializer(init, shape=shape, dtype=dtype)
if context.get_context("mode") == context.PYNATIVE_MODE:
x.init_data()
return x
@property
@ -195,50 +266,65 @@ class Parameter:
@property
def default_input(self):
return self._data
return self
@default_input.setter
def default_input(self, data):
self._data = data
self._value.data = data
self.set_parameter_data(data)
def __add__(self, other):
return self.default_input + other
def _update_tensor_data(self, data):
"Update the parameter by a Tensor."
if isinstance(self, Tensor):
# for Tensor same shape:
return self.assign_value(data)
# create a new tensor
return Parameter(data, self.name, self.requires_grad)
def __sub__(self, other):
return self.default_input - other
def set_parameter_data(self, data, slice_shape=False):
"""
Set `default_input` of current `Parameter`.
def __mul__(self, other):
return self.default_input * other
Args:
data (Union[Tensor, Initializer]): new data.
slice_shape (bool): If slice the Parameter. Default: False.
def __truediv__(self, other):
return self.default_input / other
Retruns:
Parameter, the parameter after set data.
"""
if not isinstance(data, (MetaTensor, Initializer)):
raise ValueError(f"Parameter data must be `Initializer` or a kind of `MetaTensor` "
f"(like `Tensor` or `MetaTensor`). But with type {type(data)}.")
# both not init.
is_incoming_tensor = isinstance(data, Tensor)
is_current_tensor = isinstance(self, Tensor)
def __setitem__(self, index, value):
default_input = self.default_input
default_input[index] = value
return self
def set_parameter_data(self, data):
"""Set `default_input` of current `Parameter`."""
if isinstance(data, bool):
raise ValueError('Parameter data can not be `bool`')
if isinstance(data, Tensor):
# make a copy of Tensor to init the parameter
data = Tensor(data.asnumpy())
data.init_flag = False
elif isinstance(data, Initializer):
self.init_mode = data
data = MetaTensor(self.init_mode.dtype, self.init_mode.shape)
elif isinstance(data, int):
data = Tensor(data, dtype=mstype.int32)
elif isinstance(data, float):
data = Tensor(data, dtype=mstype.float32)
if is_incoming_tensor and not is_current_tensor:
raise TypeError("Parameter is a `MetaTensor` and not initializered, `data` for `set_parameter_data`"
"should be a Initializer. If you want to update it by Tensor, call method"
"`init_parameters_data` of `Cell` to init and replace all the Parameter of"
"network, then call this method.")
if tuple(self.shape) != tuple(data.shape):
# If Slice create Parameter shape can be change.
if slice_shape:
self._update_tensor_data(data)
self.sliced = True
else:
raise ValueError(f"Can not change the shape of Parameter which has been initialized."
f" Current shape is {self.shape}, and incoming is {data.shape}.")
if self.dtype != data.dtype:
raise ValueError(f"Can not change the Parameter dtype. Current dtype is {self.set_dtype}"
f", and incoming is {data.dtype}. Use .set_dtype(xxx) to change the dtype.")
if isinstance(data, Initializer):
# The parameter has been initializered, directly update by the data
if is_current_tensor:
self._update_tensor_data(data.to_tensor())
else:
self.init_mode = data
elif is_incoming_tensor or is_current_tensor:
self._update_tensor_data(data)
else:
data = Tensor(data)
data.init_flag = False
self.default_input = data
raise ValueError(f"Not support to update the Parameter by {data}")
return self
def init_data(self, layout=None, set_sliced=False):
"""
@ -252,31 +338,37 @@ class Parameter:
- slice_shape (list[int]): Shape of slice.
set_sliced (bool): True if should set parameter sliced after init the data of initializer.
Default: False.
Returns:
Parameter, Parameter after init data.
"""
if isinstance(self.default_input, Tensor):
# skip if data already initialized.
return
if self.init_mode is None:
return self
if self.inited_param is not None:
return self.inited_param
if layout is not None:
if not isinstance(layout, list):
raise TypeError("The layout should be list! layout is {}."
.format(layout))
raise TypeError("The layout should be list! layout is {}.".format(layout))
if len(layout) < 3:
raise ValueError("The length of layout must be larger than 3! layout is {}."
.format(layout))
raise ValueError("The length of layout must be larger than 3! layout is {}.".format(layout))
slice_index = int(_get_slice_index(layout[0], layout[1]))
if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)):
self.default_input = self.init_mode.to_tensor(0, [1])
data = self.init_mode.to_tensor(0, [1])
else:
self.default_input = self.init_mode.to_tensor(slice_index, layout[2])
data = self.init_mode.to_tensor(slice_index, layout[2])
else:
if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)):
self.default_input = self.init_mode.to_tensor(0, [1])
data = self.init_mode.to_tensor(0, [1])
else:
self.default_input = self.init_mode.to_tensor()
data = self.init_mode.to_tensor()
self.init_mode = None
obj = self._update_tensor_data(data)
if id(obj) != id(self):
self._inited_param = obj
obj.init_mode = None
if set_sliced:
self.sliced = True
obj.sliced = True
return obj
class ParameterTuple(tuple):

2
mindspore/common/tensor.py
View File

@ -75,7 +75,7 @@ class Tensor(Tensor_):
self._virtual_flag = False
def __repr__(self):
return str(self.__str__())
return str(Tensor_.__str__(self))
def __add__(self, other):
out = tensor_operator_registry.get('__add__')(self, other)

6
mindspore/core/ir/anf.h
View File

@ -283,11 +283,11 @@ class Parameter : public ANode {
std::string fullname_with_scope() override { return name(); };
bool has_default() const { return has_default_; }
void set_default_param(ParamValuePtr param) {
void set_default_param(ValuePtr param) {
default_param_ = param;
has_default_ = true;
}
ParamValuePtr default_param() const { return default_param_; }
ValuePtr default_param() const { return default_param_; }
bool operator==(const AnfNode &other) const override {
if (!other.isa<Parameter>()) {
@ -303,7 +303,7 @@ class Parameter : public ANode {
private:
std::string name_;
bool has_default_;
ParamValuePtr default_param_;
ValuePtr default_param_;
};
using ParameterPtr = std::shared_ptr<Parameter>;

4
mindspore/core/ir/param_value.h
View File

@ -33,9 +33,6 @@ class ParamValue {
virtual ~ParamValue() = default;
tensor::MetaTensorPtr value() const { return value_; }
void set_value(const tensor::MetaTensorPtr &value) { value_ = value; }
const std::string &name() const { return name_; }
void set_name(const std::string &name) { name_ = name; }
@ -72,7 +69,6 @@ class ParamValue {
}
private:
tensor::MetaTensorPtr value_;
std::string name_{"Parameter"};
bool requires_grad_{true};
bool layerwise_parallel_{false};

6
mindspore/lite/src/param_value_lite.h
View File

@ -36,7 +36,7 @@ struct AnfQuantParam {
int32_t numBits;
AnfQuantParam() : scale(1.0), zeroPoint(0), min(0.0), max(0.0), narrowRange(false), numBits(8), inited(false) {}
};
class ParamValueLite : public ParamValue {
class ParamValueLite : public Value {
public:
ParamValueLite() : tensor_addr_(nullptr), tensor_size_(0) {}
virtual ~ParamValueLite() = default;
@ -65,6 +65,10 @@ class ParamValueLite : public ParamValue {
quant_params_.emplace_back(std::move(quant_param));
}
bool operator==(const Value &other) const override {
this == &other;
}
private:
void *tensor_addr_;
size_t tensor_size_;

75
mindspore/nn/cell.py
View File

@ -229,7 +229,6 @@ class Cell:
for item in inputs:
if isinstance(item, numpy.ndarray):
raise TypeError("cell inputs should not be numpy array.")
self.init_parameters_data()
orign_grad = []
if self.requires_grad is True:
_pynative_exec.set_grad_flag(True)
@ -350,19 +349,8 @@ class Cell:
params (dict): The parameters dictionary used for init data graph.
"""
if params is None:
for key in self.parameters_dict():
tensor = self.parameters_dict()[key].data
if key not in self.parameter_layout_dict:
logger.info("layout dict does not contain the key %s", key)
continue
if self.parameters_dict()[key].sliced:
logger.debug("Param %s is already sliced.", key)
continue
layout = self.parameter_layout_dict[key]
new_tensor = _load_tensor_by_layout(tensor, layout)
self.parameters_dict()[key].set_parameter_data(new_tensor)
self.parameters_dict()[key].sliced = True
elif isinstance(params, OrderedDict):
params = self.parameters_dict()
if isinstance(params, OrderedDict):
for key in params:
tensor = params[key].data
if key not in self.parameter_layout_dict:
@ -373,8 +361,7 @@ class Cell:
continue
layout = self.parameter_layout_dict[key]
new_tensor = _load_tensor_by_layout(tensor, layout)
params[key].set_parameter_data(new_tensor)
params[key].sliced = True
params[key].set_parameter_data(new_tensor, True)
else:
raise TypeError('Parameters need OrderedDict type, but got {}'.
format(type(params)))
@ -545,17 +532,46 @@ class Cell:
"""
raise NotImplementedError
def init_parameters_data(self, recurse=True, auto_parallel_mode=False):
"""Init parameters' data."""
for param in self.get_parameters(expand=recurse):
if not auto_parallel_mode:
param.init_data()
elif param.name not in self.parameter_layout_dict:
logger.debug("Layout dict does not contain the key %s.", param.name)
param.init_data(set_sliced=True)
else:
layout = self.parameter_layout_dict[param.name]
param.init_data(layout, set_sliced=True)
def init_parameters_data(self, auto_parallel_mode=False):
"""
Init all parameters' data and replace the original saved parameters in cell.
Args:
auto_parallel_mode (bool): If running in auto_parallel_mode.
Returns:
Dict[Parameter, Parameter], returns a dict of original parameter and replaced parameter.
"""
replace = dict()
def _updata(param):
if param in replace:
return replace[param]
layout = None
set_sliced = False
if auto_parallel_mode:
set_sliced = True
if param.name not in self.parameter_layout_dict:
logger.debug("Layout dict does not contain the key %s.", param.name)
else:
layout = self.parameter_layout_dict[param.name]
new_p = param.init_data(layout, set_sliced=set_sliced)
replace[param] = new_p
return new_p
# replace all original usage.
cells = self.cells_and_names()
for _, cell in cells:
params = cell._params.items()
for param_name, param in params:
cell._params[param_name] = _updata(param)
cell_dict = cell.__dict__
for key in cell_dict:
if isinstance(cell_dict[key], ParameterTuple):
param_tuple = cell_dict[key]
new_param_tuple = []
for param in param_tuple:
new_param_tuple.append(_updata(param))
cell.__dict__[key] = ParameterTuple(new_param_tuple)
return replace
def parameters_dict(self, recurse=True):
"""
@ -682,9 +698,10 @@ class Cell:
for cell_name, cell in cells:
params = cell._params.items()
for par_name, par in params:
if par and par not in params_set:
if par.inited_param is not None:
par = par.inited_param
if par is not None and par not in params_set:
params_set.add(par)
par_new_name = par_name
if cell_name:
par_new_name = cell_name + '.' + par_new_name

2
mindspore/nn/optim/optimizer.py
View File

@ -90,7 +90,7 @@ class Optimizer(Cell):
def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0):
super(Optimizer, self).__init__(auto_prefix=False)
if parameters and not isinstance(parameters, list):
if parameters is not None and not isinstance(parameters, list):
parameters = list(parameters)
if not parameters:

5
mindspore/train/serialization.py
View File

@ -295,7 +295,6 @@ def load_param_into_net(net, parameter_dict):
logger.error("Failed to combine the net and the parameters.")
msg = ("Argument parameter_dict element should be a Parameter, but got {}.".format(type(new_param)))
raise TypeError(msg)
param.init_data()
_update_param(param, new_param)
else:
param_not_load.append(param.name)
@ -362,15 +361,13 @@ def _exec_save_checkpoint(train_network, ckpt_file_name, integrated_save=True, a
integrated_save (bool): Whether to integrated save in automatic model parallel scene.
async_save (bool): Whether asynchronous execute save checkpoint into file. Default: False.
"""
train_network.init_parameters_data()
param_dict = {}
for _, param in train_network.parameters_and_names():
param_dict[param.name] = param
param_list = []
for (key, value) in param_dict.items():
each_param = {"name": key}
value.init_data()
if isinstance(value.data, Tensor):
param_data = value.data
else:

1
model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py
View File

@ -263,6 +263,7 @@ class MobileNetV2(nn.Cell):
Examples:
>>> _initialize_weights()
"""
self.init_parameters_data()
for _, m in self.cells_and_names():
if isinstance(m, (nn.Conv2d, DepthwiseConv)):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

2
model_zoo/official/cv/mobilenetv2_quant/src/mobilenetV2.py
View File

@ -196,6 +196,7 @@ class mobilenetV2(nn.Cell):
self.head = nn.SequentialCell(head)
# init weights
self.init_parameters_data()
self._initialize_weights()
def construct(self, x):
@ -215,6 +216,7 @@ class mobilenetV2(nn.Cell):
Examples:
>>> _initialize_weights()
"""
self.init_parameters_data()
for _, m in self.cells_and_names():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

2
model_zoo/official/cv/mobilenetv3/src/mobilenetV3.py
View File

@ -200,6 +200,7 @@ class ResUnit(nn.Cell):
self.add = P.TensorAdd() if self.use_short_cut_conv else None
def construct(self, x):
"""construct"""
if self.first_conv:
out = self.expand(x)
else:
@ -289,6 +290,7 @@ class MobileNetV3(nn.Cell):
kernel_size=1, has_bias=True, pad_mode='pad')
self.squeeze = P.Squeeze(axis=(2, 3))
self.init_parameters_data()
self._initialize_weights()
def construct(self, x):

2
tests/st/networks/models/bert/test_bert_graph_kernel.py
View File

@ -171,9 +171,9 @@ def test_bert_tdt():
netwithgrads.set_train(True)
model = Model(netwithgrads)
callback = ModelCallback()
netwithloss.init_parameters_data()
params = netwithloss.trainable_params()
for param in params:
param.init_data()
value = param.default_input
name = param.name
if isinstance(value, Tensor):

4
tests/st/networks/models/bert/test_bert_tdt_lossscale.py
View File

@ -207,9 +207,9 @@ def test_bert_percision():
netwithgrads.set_train(True)
model = Model(netwithgrads)
callback = ModelCallback()
netwithloss.init_parameters_data()
params = netwithloss.trainable_params()
for param in params:
param.init_data()
value = param.default_input
name = param.name
if isinstance(value, Tensor):
@ -279,9 +279,9 @@ def test_bert_performance():
netwithgrads.set_train(True)
model = Model(netwithgrads)
callback = ModelCallback()
netwithloss.init_parameters_data()
params = netwithloss.trainable_params()
for param in params:
param.init_data()
value = param.default_input
name = param.name
if isinstance(value, Tensor):

1
tests/st/networks/models/deeplabv3/test_deeplabv3.py
View File

@ -63,6 +63,7 @@ class LossCallBack(Callback):
str(cb_params.net_outputs)))
def model_fine_tune(train_net, fix_weight_layer):
train_net.init_parameters_data()
for para in train_net.trainable_params():
para.set_parameter_data(Tensor(np.ones(para.data.shape).astype(np.float32) * 0.02))
if fix_weight_layer in para.name:

11
tests/st/networks/models/resnet50/test_resnet50_imagenet.py
View File

@ -174,9 +174,14 @@ def train_process(q, device_id, epoch_size, device_num, enable_hccl):
steps_per_epoch=step_size, lr_decay_mode=config.lr_decay_mode))
# optimizer
decayed_params = list(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'bias' not in x.name,
net.trainable_params()))
no_decayed_params = [param for param in net.trainable_params() if param not in decayed_params]
decayed_params = []
no_decayed_params = []
for param in net.trainable_params():
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
decayed_params.append(param)
else:
no_decayed_params.append(param)
group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
{'params': no_decayed_params, 'weight_decay': 0.0},
{'order_params': net.trainable_params()}]

3
tests/ut/cpp/pre_activate/ascend/enhancer/insert_memcpy_async_for_hccl_op_test.cc
View File

@ -107,7 +107,6 @@ TEST_F(TestHWInsertMemcpyForHccl, test_cond2) {
for (auto p : kg->parameters()) {
auto param = p->cast<ParameterPtr>();
EXPECT_NE(param, nullptr);
param->set_default_param(std::make_shared<ParamValue>());
}
auto optimizer = std::make_shared<opt::GraphOptimizer>();
@ -157,7 +156,6 @@ TEST_F(TestHWInsertMemcpyForHccl, test_cond4) {
for (auto p : kg->parameters()) {
auto param = p->cast<ParameterPtr>();
EXPECT_NE(param, nullptr);
param->set_default_param(std::make_shared<ParamValue>());
}
auto optimizer = std::make_shared<opt::GraphOptimizer>();
@ -185,7 +183,6 @@ TEST_F(TestHWInsertMemcpyForHccl, test_cond5) {
for (auto p : kg->parameters()) {
auto param = p->cast<ParameterPtr>();
EXPECT_NE(param, nullptr);
param->set_default_param(std::make_shared<ParamValue>());
}
auto optimizer = std::make_shared<opt::GraphOptimizer>();

2
tests/ut/cpp/session/anf_runtime_algorithm_test.cc
View File

@ -766,7 +766,7 @@ TEST_F(AnfRuntimeAlgorithmTest, IsParameterWeight) {
auto kernel_graph = std::make_shared<KernelGraph>();
auto parameter_node = kernel_graph->add_parameter();
MS_EXCEPTION_IF_NULL(parameter_node);
auto param_value_new = std::make_shared<ParamValue>();
auto param_value_new = std::make_shared<tensor::Tensor>(int64_t(0), kInt32);
parameter_node->set_default_param(param_value_new);
EXPECT_TRUE(AnfAlgo::IsParameterWeight(parameter_node));
EXPECT_THROW(AnfAlgo::IsParameterWeight(nullptr), std::runtime_error);

2
tests/ut/cpp/session/kernel_graph_test.cc
View File

@ -82,7 +82,7 @@ TEST_F(KernelGraphTest, NewParameter) {
// test weight parameter node as input
auto weight_parameter_node = anf_graph->add_parameter();
MS_EXCEPTION_IF_NULL(weight_parameter_node);
auto param_value_new = std::make_shared<ParamValue>();
auto param_value_new = std::make_shared<tensor::Tensor>(kNumberTypeFloat32, shape);
weight_parameter_node->set_default_param(param_value_new);
weight_parameter_node->set_abstract(x_abstract);
auto new_weight_parameter_node = kernel_graph->NewParameter(weight_parameter_node);

2
tests/ut/python/ir/test_tensor.py
View File

@ -225,7 +225,7 @@ def test_div():
@non_graph_engine
def test_parameter():
x = Parameter(initializer(1, [1], ms.float32), name="beta1_power")
x.init_data()
x = x.init_data()
z = x / 2
print(z)

25
tests/ut/python/nn/test_parameter.py
View File

@ -139,14 +139,31 @@ def test_parameter_lazy_init():
# Call init_data() without set default_input.
para = Parameter(initializer('ones', [1, 2, 3], mstype.float32), 'test1')
assert not isinstance(para.default_input, Tensor)
para.init_data()
para = para.init_data()
assert isinstance(para.default_input, Tensor)
assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2, 3)))
# Call init_data() after default_input is set.
para = Parameter(initializer('ones', [1, 2, 3], mstype.float32), 'test2')
assert not isinstance(para.default_input, Tensor)
para.default_input = Tensor(np.zeros((1, 2, 3)))
assert np.array_equal(para.default_input.asnumpy(), np.zeros((1, 2, 3)))
para.init_data() # expect no effect.
# expect type error when not init
with pytest.raises(TypeError):
para.default_input = Tensor(np.zeros((1, 2, 3)))
# init then assign
para = para.init_data()
# check the type
with pytest.raises(ValueError):
para.default_input = Tensor(np.zeros((1, 2, 3)))
# check the shape
with pytest.raises(ValueError):
para.default_input = Tensor(np.zeros((1, 2)))
# expect change ok
para.default_input = Tensor(np.zeros((1, 2, 3)).astype(np.float32))
assert np.array_equal(para.default_input.asnumpy(), np.zeros((1, 2, 3)))
para.default_input = initializer('ones', [1, 2, 3], mstype.float32)
assert isinstance(para.default_input, Tensor)
# same object and has inited
assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2, 3)))
# expect no effect.
para.init_data()
assert np.array_equal(para.default_input.asnumpy(), np.ones((1, 2, 3)))

9
tests/ut/python/train/quant/test_quant.py
View File

@ -69,8 +69,7 @@ def test_qat_lenet():
net = qat.convert_quant_network(
net, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
# should load the checkpoint. mock here
for param in net.get_parameters():
param.init_data()
net.init_parameters_data()
qat.export(net, img, file_name="quant.pb")
@ -80,8 +79,7 @@ def test_qat_mobile_per_channel_tf():
img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
network = qat.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
# should load the checkpoint. mock here
for param in network.get_parameters():
param.init_data()
network.init_parameters_data()
qat.export(network, img, file_name="quant.pb")
@pytest.mark.skip(reason="no `te.lang.cce` in ut env")
@ -90,6 +88,5 @@ def test_qat_mobile_per_channel_ff():
img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
network = qat.convert_quant_network(network, bn_fold=True, per_channel=[False, False], symmetric=[True, False])
# should load the checkpoint. mock here
for param in network.get_parameters():
param.init_data()
network.init_parameters_data()
qat.export(network, img, file_name="quant.pb")