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Paddle
未验证 提交 69e82d83 编写于 作者: R ronnywang 提交者： GitHub
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cherry pick 43890 (#43892) 

* cherry pick 43890
上级 dc12605d
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Showing with 830 addition and 603 deletion
paddle/fluid/pybind/pybind.cc
浏览文件 @ 69e82d83
......@@ -372,7 +372,8 @@ static T PyObjectCast(PyObject *obj) {
} catch (py::cast_error &) {
PADDLE_THROW(platform::errors::InvalidArgument(
"Python object is not type of %s, the real type is %s",
typeid(T).name(), obj->ob_type->tp_name));
typeid(T).name(),
obj->ob_type->tp_name));
}
}
......@@ -431,7 +432,8 @@ static std::vector<std::string> inline GetNameList(
}
static void inline CreateVariableIfNotExit(
const py::handle &py_handle, const framework::Scope &scope,
const py::handle &py_handle,
const framework::Scope &scope,
const framework::Executor *exe = nullptr) {
std::vector<std::string> vec_res;
......@@ -469,7 +471,8 @@ static void inline CreateVariableIfNotExit(
PyObject *py_var_desc =
PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kVarDescField);
PADDLE_ENFORCE_NOT_NULL(
py_var_desc, platform::errors::InvalidArgument(
py_var_desc,
platform::errors::InvalidArgument(
"The var_desc of parameter to set is None"));
auto var_desc = PyObjectCast<framework::VarDesc>(py_var_desc);
Py_DECREF(py_var_desc);
......@@ -505,7 +508,8 @@ static void AssertStaticGraphAndDygraphGradMakerNoDiff() {
}
}
}
PADDLE_ENFORCE_EQ(ops.empty(), true,
PADDLE_ENFORCE_EQ(ops.empty(),
true,
platform::errors::Unimplemented(
"OperatorWithKernel [%s] have only static graph grad "
"maker or have only dygraph grad maker, which is not "
......@@ -527,8 +531,10 @@ static int GetNCCLVersion() {
#endif
template <typename PlaceType>
static void TensorCopyFrom(framework::Tensor *dst, const framework::Tensor &src,
const PlaceType &place, int64_t batch_size) {
static void TensorCopyFrom(framework::Tensor *dst,
const framework::Tensor &src,
const PlaceType &place,
int64_t batch_size) {
if (batch_size < 0) {
framework::TensorCopy(src, place, dst);
} else {
......@@ -612,7 +618,8 @@ PYBIND11_MODULE(core_noavx, m) {
PyCapsule_GetPointer(dltensor->ptr(), "dltensor"));
PADDLE_ENFORCE_NOT_NULL(
dmt, platform::errors::InvalidArgument(
dmt,
platform::errors::InvalidArgument(
"from_dlpack received an invalid capsule. "
"Note that a DLPack tensor can be consumed only once."));
......@@ -632,7 +639,8 @@ PYBIND11_MODULE(core_noavx, m) {
});
m.def("_create_loaded_parameter",
[](const py::handle &vec_var_list, const Scope &scope,
[](const py::handle &vec_var_list,
const Scope &scope,
const Executor *executor) {
CreateVariableIfNotExit(vec_var_list, scope, executor);
});
......@@ -670,8 +678,9 @@ PYBIND11_MODULE(core_noavx, m) {
<< ", sci_mode=" << print_opt.sci_mode;
});
m.def("broadcast_shape", [](const std::vector<int64_t> &x_dim,
const std::vector<int64_t> &y_dim) {
m.def(
"broadcast_shape",
[](const std::vector<int64_t> &x_dim, const std::vector<int64_t> &y_dim) {
return phi::vectorize(operators::details::BroadcastTwoDims(
phi::make_ddim(x_dim), phi::make_ddim(y_dim), -1));
});
......@@ -685,7 +694,8 @@ PYBIND11_MODULE(core_noavx, m) {
m.def("_get_use_default_grad_op_desc_maker_ops",
[] { return OpInfoMap::Instance().GetUseDefaultGradOpDescMakerOps(); });
m.def("_get_all_register_op_kernels",
m.def(
"_get_all_register_op_kernels",
[](const std::string &lib) {
std::unordered_map<std::string, std::vector<std::string>>
all_kernels_info;
......@@ -712,8 +722,7 @@ PYBIND11_MODULE(core_noavx, m) {
for (auto &info_pair : kernel_pair.second) {
framework::OpKernelType kernel_type =
framework::TransPhiKernelKeyToOpKernelType(info_pair.first);
auto kernel_type_str =
framework::KernelTypeToString(kernel_type);
auto kernel_type_str = framework::KernelTypeToString(kernel_type);
if (all_kernels_info.count(op_type)) {
if (std::find(all_kernels_info[op_type].begin(),
all_kernels_info[op_type].end(),
......@@ -796,14 +805,22 @@ PYBIND11_MODULE(core_noavx, m) {
self.EmplaceBackOutput(std::move(CastPyArg2Tensor(obj, 1)));
}
})
.def("add_attr", [](paddle::CustomOpKernelContext &self,
bool attr) { self.EmplaceBackAttr(attr); })
.def("add_attr", [](paddle::CustomOpKernelContext &self,
int attr) { self.EmplaceBackAttr(attr); })
.def("add_attr", [](paddle::CustomOpKernelContext &self,
float attr) { self.EmplaceBackAttr(attr); })
.def("add_attr", [](paddle::CustomOpKernelContext &self,
int64_t attr) { self.EmplaceBackAttr(attr); })
.def("add_attr",
[](paddle::CustomOpKernelContext &self, bool attr) {
self.EmplaceBackAttr(attr);
})
.def("add_attr",
[](paddle::CustomOpKernelContext &self, int attr) {
self.EmplaceBackAttr(attr);
})
.def("add_attr",
[](paddle::CustomOpKernelContext &self, float attr) {
self.EmplaceBackAttr(attr);
})
.def("add_attr",
[](paddle::CustomOpKernelContext &self, int64_t attr) {
self.EmplaceBackAttr(attr);
})
.def("add_attr",
[](paddle::CustomOpKernelContext &self, const std::string &attr) {
self.EmplaceBackAttr(attr);
......@@ -817,13 +834,14 @@ PYBIND11_MODULE(core_noavx, m) {
.def("add_attr",
[](paddle::CustomOpKernelContext &self,
const std::vector<int64_t> &attr) { self.EmplaceBackAttr(attr); })
.def("add_attr", [](paddle::CustomOpKernelContext &self,
.def("add_attr",
[](paddle::CustomOpKernelContext &self,
const std::vector<std::string> &attr) {
self.EmplaceBackAttr(attr);
});
py::class_<framework::Tensor> framework_tensor(m, "Tensor",
py::buffer_protocol());
py::class_<framework::Tensor> framework_tensor(
m, "Tensor", py::buffer_protocol());
g_framework_tensor_pytype =
reinterpret_cast<PyTypeObject *>(framework_tensor.ptr());
framework_tensor
......@@ -898,70 +916,118 @@ PYBIND11_MODULE(core_noavx, m) {
self.mutable_data<float>(place);
})
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::CPUPlace &place,
[](framework::Tensor &self,
paddle::platform::CPUPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(
self.mutable_data(place, framework::TransToPhiDataType(type)));
})
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::XPUPlace &place,
[](framework::Tensor &self,
paddle::platform::XPUPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(
self.mutable_data(place, framework::TransToPhiDataType(type)));
})
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::CUDAPlace &place,
[](framework::Tensor &self,
paddle::platform::CUDAPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(
self.mutable_data(place, framework::TransToPhiDataType(type)));
})
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::CUDAPinnedPlace &place,
[](framework::Tensor &self,
paddle::platform::CUDAPinnedPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(
self.mutable_data(place, framework::TransToPhiDataType(type)));
})
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::MLUPlace &place,
[](framework::Tensor &self,
paddle::platform::MLUPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(
self.mutable_data(place, framework::TransToPhiDataType(type)));
})
.def("_clear", &framework::Tensor::clear)
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::NPUPlace &place,
[](framework::Tensor &self,
paddle::platform::NPUPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(
self.mutable_data(place, framework::TransToPhiDataType(type)));
})
.def("_copy_from", &TensorCopyFrom<paddle::platform::CPUPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::XPUPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::CUDAPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::NPUPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::CUDAPinnedPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::MLUPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::Place>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("set", SetTensorFromPyArray<paddle::platform::CPUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::XPUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::CUDAPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::NPUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::IPUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::MLUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::CUDAPinnedPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false,
.def("_copy_from",
&TensorCopyFrom<paddle::platform::CPUPlace>,
py::arg("tensor"),
py::arg("place"),
py::arg("batch_size") = -1)
.def("_copy_from",
&TensorCopyFrom<paddle::platform::XPUPlace>,
py::arg("tensor"),
py::arg("place"),
py::arg("batch_size") = -1)
.def("_copy_from",
&TensorCopyFrom<paddle::platform::CUDAPlace>,
py::arg("tensor"),
py::arg("place"),
py::arg("batch_size") = -1)
.def("_copy_from",
&TensorCopyFrom<paddle::platform::NPUPlace>,
py::arg("tensor"),
py::arg("place"),
py::arg("batch_size") = -1)
.def("_copy_from",
&TensorCopyFrom<paddle::platform::CUDAPinnedPlace>,
py::arg("tensor"),
py::arg("place"),
py::arg("batch_size") = -1)
.def("_copy_from",
&TensorCopyFrom<paddle::platform::MLUPlace>,
py::arg("tensor"),
py::arg("place"),
py::arg("batch_size") = -1)
.def("_copy_from",
&TensorCopyFrom<paddle::platform::Place>,
py::arg("tensor"),
py::arg("place"),
py::arg("batch_size") = -1)
.def("set",
SetTensorFromPyArray<paddle::platform::CPUPlace>,
py::arg("array"),
py::arg("place"),
py::arg("zero_copy") = false)
.def("set",
SetTensorFromPyArray<paddle::platform::XPUPlace>,
py::arg("array"),
py::arg("place"),
py::arg("zero_copy") = false)
.def("set",
SetTensorFromPyArray<paddle::platform::CUDAPlace>,
py::arg("array"),
py::arg("place"),
py::arg("zero_copy") = false)
.def("set",
SetTensorFromPyArray<paddle::platform::NPUPlace>,
py::arg("array"),
py::arg("place"),
py::arg("zero_copy") = false)
.def("set",
SetTensorFromPyArray<paddle::platform::IPUPlace>,
py::arg("array"),
py::arg("place"),
py::arg("zero_copy") = false)
.def("set",
SetTensorFromPyArray<paddle::platform::MLUPlace>,
py::arg("array"),
py::arg("place"),
py::arg("zero_copy") = false)
.def("set",
SetTensorFromPyArray<paddle::platform::CUDAPinnedPlace>,
py::arg("array"),
py::arg("place"),
py::arg("zero_copy") = false,
R"DOC(
Set the data of Tensor on place with given numpy array.
......@@ -985,7 +1051,8 @@ PYBIND11_MODULE(core_noavx, m) {
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
)DOC")
.def("shape",
.def(
"shape",
[](framework::Tensor &self) { return vectorize(self.dims()); },
R"DOC(
Return the shape of Tensor.
......@@ -1046,9 +1113,9 @@ PYBIND11_MODULE(core_noavx, m) {
ostr << self;
return ostr.str();
}) /* ------ End of original Tensor ------ */
.def(
"__init__",
[](framework::Tensor &instance, const std::vector<std::vector<size_t>>
.def("__init__",
[](framework::Tensor &instance,
const std::vector<std::vector<size_t>>
&recursive_sequence_lengths) {
LoD new_lod;
new_lod.reserve(recursive_sequence_lengths.size());
......@@ -1057,7 +1124,8 @@ PYBIND11_MODULE(core_noavx, m) {
std::back_inserter(new_lod));
LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
PADDLE_ENFORCE_EQ(
CheckLoD(new_offset_lod, -1), true,
CheckLoD(new_offset_lod, -1),
true,
platform::errors::InvalidArgument(
"The provided recursive_sequence_lengths info is "
"invalid, "
......@@ -1075,7 +1143,8 @@ PYBIND11_MODULE(core_noavx, m) {
// avoid misuse.
// The discussion is here:
// https://github.com/PaddlePaddle/Paddle/issues/10855
.def("set_lod",
.def(
"set_lod",
[](framework::Tensor &self,
const std::vector<std::vector<size_t>> &lod) {
// the input lod is offset-based level-of-detail info
......@@ -1083,12 +1152,14 @@ PYBIND11_MODULE(core_noavx, m) {
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
PADDLE_ENFORCE_EQ(
CheckLoD(new_lod, vectorize(self.dims()).front()), true,
CheckLoD(new_lod, vectorize(self.dims()).front()),
true,
platform::errors::InvalidArgument(
"The provided LoD is invalid, the LoD is %s", new_lod));
self.set_lod(new_lod);
},
py::arg("lod"), R"DOC(
py::arg("lod"),
R"DOC(
Set LoD of the Tensor.
Args:
......@@ -1108,8 +1179,10 @@ PYBIND11_MODULE(core_noavx, m) {
t.set_lod([[0, 2, 5]])
print(t.lod()) # [[0, 2, 5]]
)DOC")
.def("set_recursive_sequence_lengths",
[](framework::Tensor &self, const std::vector<std::vector<size_t>>
.def(
"set_recursive_sequence_lengths",
[](framework::Tensor &self,
const std::vector<std::vector<size_t>>
&recursive_sequence_lengths) {
// the input recursive_sequence_lengths is length-based
// level-of-detail info
......@@ -1120,7 +1193,8 @@ PYBIND11_MODULE(core_noavx, m) {
std::back_inserter(new_lod));
LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
PADDLE_ENFORCE_EQ(
CheckLoD(new_offset_lod, vectorize(self.dims()).front()), true,
CheckLoD(new_offset_lod, vectorize(self.dims()).front()),
true,
platform::errors::InvalidArgument(
"The provided recursive_sequence_lengths info is "
"invalid, "
......@@ -1129,7 +1203,8 @@ PYBIND11_MODULE(core_noavx, m) {
new_lod));
self.set_lod(new_offset_lod);
},
py::arg("recursive_sequence_lengths"), R"DOC(
py::arg("recursive_sequence_lengths"),
R"DOC(
Set LoD of the Tensor according to recursive sequence lengths.
For example, if recursive_sequence_lengths=[[2, 3]], which means
......@@ -1154,7 +1229,8 @@ PYBIND11_MODULE(core_noavx, m) {
print(t.recursive_sequence_lengths()) # [[2, 3]]
print(t.lod()) # [[0, 2, 5]]
)DOC")
.def("lod",
.def(
"lod",
[](framework::Tensor &self) -> std::vector<std::vector<size_t>> {
// output the offset-based lod info
LoD lod = self.lod();
......@@ -1181,7 +1257,8 @@ PYBIND11_MODULE(core_noavx, m) {
print(t.lod()) # [[0, 2, 5]]
)DOC")
// Set above comments of set_lod.
.def("recursive_sequence_lengths",
.def(
"recursive_sequence_lengths",
[](framework::Tensor &self) -> std::vector<std::vector<size_t>> {
// output the length-based lod info
LoD lod = phi::ConvertToLengthBasedLoD(self.lod());
......@@ -1208,7 +1285,8 @@ PYBIND11_MODULE(core_noavx, m) {
t.set_recursive_sequence_lengths([[2, 3]])
print(t.recursive_sequence_lengths()) # [[2, 3]]
)DOC")
.def("has_valid_recursive_sequence_lengths",
.def(
"has_valid_recursive_sequence_lengths",
[](framework::Tensor &self) -> bool {
// Check that the lod info is valid and match the outermost
// dimension of the Tensor data
......@@ -1594,11 +1672,13 @@ PYBIND11_MODULE(core_noavx, m) {
new (&instance) phi::SelectedRows();
})
.def("__init__",
[](phi::SelectedRows &instance, const std::vector<int64_t> rows,
[](phi::SelectedRows &instance,
const std::vector<int64_t> rows,
const int64_t &height) {
new (&instance) phi::SelectedRows(rows, height);
})
.def("get_tensor",
.def(
"get_tensor",
[](phi::SelectedRows &self) { return self.mutable_value(); },
py::return_value_policy::reference)
.def("numel",
......@@ -1642,7 +1722,8 @@ All parameter, weight, gradient are variables in Paddle.
})
.def("get_float",
[](const Variable &var) -> float { return var.Get<float>(); })
.def("get_tensor",
.def(
"get_tensor",
[](Variable &self) -> LoDTensor * {
return self.GetMutable<LoDTensor>();
},
......@@ -1655,50 +1736,61 @@ All parameter, weight, gradient are variables in Paddle.
[](Variable &self, Strings str_list) {
*self.GetMutable<Strings>() = str_list;
})
.def("set_vocab", [](Variable &self,
Vocab vocab) { *self.GetMutable<Vocab>() = vocab; })
.def("get_string_tensor",
.def("set_vocab",
[](Variable &self, Vocab vocab) {
*self.GetMutable<Vocab>() = vocab;
})
.def(
"get_string_tensor",
[](Variable &self) { return self.GetMutable<Strings>(); },
py::return_value_policy::reference)
.def("get_map_tensor",
.def(
"get_map_tensor",
[](Variable &self) { return self.GetMutable<Vocab>(); },
py::return_value_policy::reference)
.def("get_lod_rank_table",
.def(
"get_lod_rank_table",
[](Variable &self) { return self.GetMutable<LoDRankTable>(); },
py::return_value_policy::reference)
.def("get_selected_rows",
.def(
"get_selected_rows",
[](Variable &self) -> phi::SelectedRows * {
return self.GetMutable<phi::SelectedRows>();
},
py::return_value_policy::reference)
.def("get_lod_tensor_array",
.def(
"get_lod_tensor_array",
[](Variable &self) { return self.GetMutable<LoDTensorArray>(); },
py::return_value_policy::reference)
.def("get_fetch_list",
.def(
"get_fetch_list",
[](Variable &self) { return self.GetMutable<FetchList>(); },
py::return_value_policy::reference)
#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
.def("get_communicator",
.def(
"get_communicator",
[](Variable &self) -> platform::Communicator * {
return self.GetMutable<platform::Communicator>();
},
py::return_value_policy::reference)
#endif
.def("get_reader",
.def(
"get_reader",
[](Variable &self) -> framework::ReaderHolder * {
PADDLE_ENFORCE_EQ(
self.IsType<framework::ReaderHolder>(), true,
PADDLE_ENFORCE_EQ(self.IsType<framework::ReaderHolder>(),
true,
platform::errors::InvalidArgument(
"The variable is not type of ReaderHolder."));
return self.GetMutable<framework::ReaderHolder>();
},
py::return_value_policy::reference)
.def("get_scope",
.def(
"get_scope",
[](Variable &self) -> Scope * {
auto scope_vec =
self.GetMutable<std::vector<framework::Scope *>>();
auto scope_vec = self.GetMutable<std::vector<framework::Scope *>>();
PADDLE_ENFORCE_GT(
scope_vec->size(), 0,
scope_vec->size(),
0,
platform::errors::InvalidArgument(
"The size of scope_vec should be greater than 0"));
return scope_vec->front();
......@@ -1736,7 +1828,8 @@ All parameter, weight, gradient are variables in Paddle.
_Scope
.def("_remove_from_pool",
[](Scope &self) { ScopePool::Instance().Remove(&self); })
.def("var",
.def(
"var",
[](Scope &self, const std::string &name) -> Variable * {
return self.Var(name);
},
......@@ -1755,7 +1848,9 @@ All parameter, weight, gradient are variables in Paddle.
out (core.Variable): the found or created variable.
)DOC",
py::return_value_policy::reference)
.def("find_var", &Scope::FindVar, py::arg("name"),
.def("find_var",
&Scope::FindVar,
py::arg("name"),
R"DOC(
Find variable named :code:`name` in the current scope or
its parent scope. Return None if not found.
......@@ -1768,7 +1863,9 @@ All parameter, weight, gradient are variables in Paddle.
)DOC",
py::return_value_policy::reference)
.def("size", &Scope::Size)
.def("erase", &Scope::EraseVars, py::arg("names"),
.def("erase",
&Scope::EraseVars,
py::arg("names"),
R"DOC(
Find variable named :code:`name` in the current scope or
its parent scope. Return None if not found.
......@@ -1780,7 +1877,9 @@ All parameter, weight, gradient are variables in Paddle.
None
)DOC",
py::return_value_policy::reference)
.def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
.def(
"new_scope",
[](Scope &self) -> Scope * { return &self.NewScope(); },
R"DOC(
Create a new sub-scope of the current scope.
......@@ -1788,13 +1887,15 @@ All parameter, weight, gradient are variables in Paddle.
out (core._Scope): the created sub-scope.
)DOC",
py::return_value_policy::reference)
.def("drop_kids", &Scope::DropKids,
.def("drop_kids",
&Scope::DropKids,
R"DOC(
Delete all sub-scopes of the current scope.
)DOC")
.def("_kids", &Scope::kids);
m.def("Scope",
m.def(
"Scope",
[]() -> Scope * {
auto *s = new Scope();
ScopePool::Instance().Insert(std::unique_ptr<Scope>(s));
......@@ -1817,7 +1918,8 @@ All parameter, weight, gradient are variables in Paddle.
if (info.HasOpProtoAndChecker()) {
std::string str;
PADDLE_ENFORCE_EQ(
info.Proto().SerializeToString(&str), true,
info.Proto().SerializeToString(&str),
true,
platform::errors::Fatal(
"Serialize OpProto Error. This could be a bug of Paddle."));
ret_values.emplace_back(str);
......@@ -1838,18 +1940,20 @@ All parameter, weight, gradient are variables in Paddle.
}
return res;
});
m.def(
"get_grad_op_desc", [](const OpDesc &op_desc,
m.def("get_grad_op_desc",
[](const OpDesc &op_desc,
const std::unordered_set<std::string> &no_grad_set,
const std::vector<BlockDesc *> &grad_sub_block) {
std::unordered_map<std::string, std::string> grad_to_var;
std::vector<std::unique_ptr<OpDesc>> grad_op_descs =
framework::OpInfoMap::Instance()
.Get(op_desc.Type())
.GradOpMaker()(op_desc, no_grad_set, &grad_to_var,
grad_sub_block);
.GradOpMaker()(
op_desc, no_grad_set, &grad_to_var, grad_sub_block);
std::vector<OpDesc *> grad_op_desc_ptrs(grad_op_descs.size());
std::transform(grad_op_descs.begin(), grad_op_descs.end(),
std::transform(
grad_op_descs.begin(),
grad_op_descs.end(),
grad_op_desc_ptrs.begin(),
[](std::unique_ptr<OpDesc> &p) { return p.release(); });
return std::make_pair(grad_op_desc_ptrs, grad_to_var);
......@@ -1866,7 +1970,8 @@ All parameter, weight, gradient are variables in Paddle.
return framework::OpInfoMap::Instance().Get(op_type).HasInferInplace();
});
m.def("infer_no_need_buffer_slots",
[](const std::string op_type, const framework::VariableNameMap &inputs,
[](const std::string op_type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs) {
auto infer_func = framework::OpInfoMap::Instance()
......@@ -1879,7 +1984,8 @@ All parameter, weight, gradient are variables in Paddle.
return empty;
}
});
m.def("prune", [](const ProgramDesc &origin,
m.def("prune",
[](const ProgramDesc &origin,
const std::set<std::string> &feeded_var_names,
const std::vector<std::array<size_t, 2>> &targets) {
ProgramDesc prog_with_targets(origin);
......@@ -1893,7 +1999,8 @@ All parameter, weight, gradient are variables in Paddle.
return std::make_tuple(ProgramDesc(pruned_desc),
pruned_origin_block_id_map);
});
m.def("prune_backward",
m.def(
"prune_backward",
[](const framework::ProgramDesc &program) {
return PruneBackward(program);
},
......@@ -2040,12 +2147,12 @@ All parameter, weight, gradient are variables in Paddle.
#ifdef PADDLE_WITH_CUSTOM_DEVICE
device_types = phi::DeviceManager::GetAllDeviceTypes();
#else
LOG(WARNING) << string::Sprintf(
VLOG(1) << string::Sprintf(
"Cannot use get_all_device_type because you have installed"
"CPU/GPU version PaddlePaddle.\n"
"If you want to use get_all_device_type, please try to install"
"CustomDevice version "
"PaddlePaddle by: pip install paddlepaddle-core\n");
"PaddlePaddle by: pip install paddlepaddle\n");
#endif
return device_types;
});
......@@ -2054,12 +2161,12 @@ All parameter, weight, gradient are variables in Paddle.
#ifdef PADDLE_WITH_CUSTOM_DEVICE
device_types = phi::DeviceManager::GetAllCustomDeviceTypes();
#else
LOG(WARNING) << string::Sprintf(
VLOG(1) << string::Sprintf(
"Cannot use get_all_custom_device_type because you have installed"
"CPU/GPU version PaddlePaddle.\n"
"If you want to use get_all_custom_device_type, please try to "
"install CustomDevice version "
"PaddlePaddle by: pip install paddlepaddle-core\n");
"PaddlePaddle by: pip install paddlepaddle\n");
#endif
return device_types;
});
......@@ -2068,12 +2175,12 @@ All parameter, weight, gradient are variables in Paddle.
#ifdef PADDLE_WITH_CUSTOM_DEVICE
devices = phi::DeviceManager::GetAllDeviceList();
#else
LOG(WARNING) << string::Sprintf(
VLOG(1) << string::Sprintf(
"Cannot use get_available_device because you have installed"
"CPU/GPU version PaddlePaddle.\n"
"If you want to use get_available_device, please try to install"
"CustomDevice version "
"PaddlePaddle by: pip install paddlepaddle-core\n");
"PaddlePaddle by: pip install paddlepaddle\n");
#endif
return devices;
});
......@@ -2082,18 +2189,19 @@ All parameter, weight, gradient are variables in Paddle.
#ifdef PADDLE_WITH_CUSTOM_DEVICE
devices = phi::DeviceManager::GetAllCustomDeviceList();
#else
LOG(WARNING) << string::Sprintf(
VLOG(1) << string::Sprintf(
"Cannot use get_available_custom_device because you have "
"installed"
"CPU/GPU version PaddlePaddle.\n"
"If you want to use get_available_custom_device, please try to "
"install"
"CustomDevice version "
"PaddlePaddle by: pip install paddlepaddle-core\n");
"PaddlePaddle by: pip install paddlepaddle\n");
#endif
return devices;
});
py::class_<platform::CustomPlace>(m, "CustomPlace",
py::class_<platform::CustomPlace>(m,
"CustomPlace",
R"DOC(
CustomPlace is a descriptor of a device.
It represents a custom device on which a tensor will be allocated and a model will run.
......@@ -2105,7 +2213,8 @@ All parameter, weight, gradient are variables in Paddle.
fake_cpu_place = paddle.CustomPlace("FakeCPU", 0)
)DOC")
.def("__init__",
[](platform::CustomPlace &self, const std::string &device_type,
[](platform::CustomPlace &self,
const std::string &device_type,
int dev_id) {
#ifdef PADDLE_WITH_CUSTOM_DEVICE
if (UNLIKELY(dev_id < 0)) {
......@@ -2113,7 +2222,8 @@ All parameter, weight, gradient are variables in Paddle.
"Invalid CustomPlace(%s, %d), device id must be 0 "
"or "
"positive integer",
device_type, dev_id);
device_type,
dev_id);
std::exit(-1);
}
......@@ -2134,7 +2244,11 @@ All parameter, weight, gradient are variables in Paddle.
"inside "
"[0, %d), because %s "
"number on your machine is %d",
device_type, dev_id, dev_count, device_type, dev_count);
device_type,
dev_id,
dev_count,
device_type,
dev_count);
std::exit(-1);
}
}
......@@ -2144,7 +2258,8 @@ All parameter, weight, gradient are variables in Paddle.
"Invalid CustomPlace(%s, %d), the device type is "
"not registered "
"as a custom device.",
device_type, dev_id);
device_type,
dev_id);
std::exit(-1);
}
#else
......@@ -2153,7 +2268,7 @@ All parameter, weight, gradient are variables in Paddle.
"version PaddlePaddle.\n"
"If you want to use CustomDevice, please try to install"
"CustomDevice version "
"PaddlePaddle by: pip install paddlepaddle-core\n"
"PaddlePaddle by: pip install paddlepaddle\n"
"If you only have CPU, please change "
"CustomPlace(%s, %d) to be CPUPlace().\n",
device_type, dev_id);
......@@ -2215,7 +2330,8 @@ All parameter, weight, gradient are variables in Paddle.
LOG(ERROR) << string::Sprintf(
"Invalid CUDAPlace(%d), must inside [0, %d), because GPU "
"number on your machine is %d",
dev_id, platform::GetGPUDeviceCount(),
dev_id,
platform::GetGPUDeviceCount(),
platform::GetGPUDeviceCount());
std::exit(-1);
}
......@@ -2281,7 +2397,8 @@ All parameter, weight, gradient are variables in Paddle.
LOG(ERROR) << string::Sprintf(
"Invalid XPUPlace(%d), must inside [0, %d), because XPU "
"number on your machine is %d",
dev_id, platform::GetXPUDeviceCount(),
dev_id,
platform::GetXPUDeviceCount(),
platform::GetXPUDeviceCount());
std::exit(-1);
}
......@@ -2446,7 +2563,8 @@ All parameter, weight, gradient are variables in Paddle.
LOG(ERROR) << string::Sprintf(
"Invalid NPUPlace(%d), must inside [0, %d), because NPU "
"number on your machine is %d",
dev_id, platform::GetNPUDeviceCount(),
dev_id,
platform::GetNPUDeviceCount(),
platform::GetNPUDeviceCount());
std::exit(-1);
}
......@@ -2562,7 +2680,8 @@ All parameter, weight, gradient are variables in Paddle.
LOG(ERROR) << string::Sprintf(
"Invalid MLUPlace(%d), must inside [0, %d), because MLU "
"number on your machine is %d",
dev_id, platform::GetMLUDeviceCount(),
dev_id,
platform::GetMLUDeviceCount(),
platform::GetMLUDeviceCount());
std::exit(-1);
}
......@@ -2635,8 +2754,10 @@ All parameter, weight, gradient are variables in Paddle.
.def("mlu_device_id", [](platform::Place &self) { return self.device; })
.def("custom_device_id",
[](platform::Place &self) { return self.device; })
.def("set_place", [](platform::Place &self,
const platform::Place &other) { self = other; })
.def("set_place",
[](platform::Place &self, const platform::Place &other) {
self = other;
})
.def("set_place",
[](platform::Place &self, const platform::CPUPlace &cpu_place) {
self = cpu_place;
......@@ -2681,7 +2802,8 @@ All parameter, weight, gradient are variables in Paddle.
true,
platform::errors::InvalidArgument(
"Cannot parse user input to OpDesc"));
PADDLE_ENFORCE_EQ(desc.IsInitialized(), true,
PADDLE_ENFORCE_EQ(desc.IsInitialized(),
true,
platform::errors::InvalidArgument(
"The provided OpDesc is not "
"initialized, the reason is: %s",
......@@ -2689,37 +2811,43 @@ All parameter, weight, gradient are variables in Paddle.
return OpRegistry::CreateOp(desc);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
[](OperatorBase &self,
const Scope &scope,
const platform::CPUPlace &place) {
pybind11::gil_scoped_release release;
self.Run(scope, place);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
[](OperatorBase &self,
const Scope &scope,
const platform::XPUPlace &place) {
pybind11::gil_scoped_release release;
self.Run(scope, place);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
[](OperatorBase &self,
const Scope &scope,
const platform::NPUPlace &place) {
pybind11::gil_scoped_release release;
self.Run(scope, place);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
[](OperatorBase &self,
const Scope &scope,
const platform::CUDAPlace &place) {
pybind11::gil_scoped_release release;
self.Run(scope, place);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
[](OperatorBase &self,
const Scope &scope,
const platform::CUDAPinnedPlace &place) {
pybind11::gil_scoped_release release;
self.Run(scope, place);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
[](OperatorBase &self,
const Scope &scope,
const platform::MLUPlace &place) {
pybind11::gil_scoped_release release;
self.Run(scope, place);
......@@ -2745,7 +2873,8 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<framework::TrainerBase, std::shared_ptr<framework::TrainerBase>>(
m, "TrainerBase")
.def("get_worker_scope",
.def(
"get_worker_scope",
[](TrainerBase &self, int thread_id) -> Scope * {
return self.GetWorkerScope(thread_id);
},
......@@ -2758,13 +2887,17 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<framework::Executor>(m, "Executor")
.def(py::init<const platform::Place &>())
.def("close", &Executor::Close)
.def("run_from_dataset", &Executor::RunFromDataset,
.def("run_from_dataset",
&Executor::RunFromDataset,
py::call_guard<py::gil_scoped_release>())
.def("release_trainer", &Executor::ReleaseTrainer,
.def("release_trainer",
&Executor::ReleaseTrainer,
py::call_guard<py::gil_scoped_release>())
.def("init_for_dataset",
[](Executor &self, const ProgramDesc &prog,
const std::string &trainer_desc, Scope *scope,
[](Executor &self,
const ProgramDesc &prog,
const std::string &trainer_desc,
Scope *scope,
Dataset *dataset) -> std::shared_ptr<TrainerBase> {
pybind11::gil_scoped_release release;
return self.InitForDataset(prog, trainer_desc, scope, dataset);
......@@ -2775,40 +2908,62 @@ All parameter, weight, gradient are variables in Paddle.
self.RunFromDataset(trainer);
})
.def("run_prepared_ctx",
[](Executor &self, ExecutorPrepareContext *ctx, Scope *scope,
[](Executor &self,
ExecutorPrepareContext *ctx,
Scope *scope,
std::map<std::string, const LoDTensor *> *feed_targets,
std::map<std::string, FetchType *> *fetch_targets,
bool create_local_scope = true, bool create_vars = true,
bool create_local_scope = true,
bool create_vars = true,
const std::string &feed_holder_name = "feed",
const std::string &fetch_holder_name = "fetch") {
pybind11::gil_scoped_release release;
self.RunPreparedContext(ctx, scope, feed_targets, fetch_targets,
create_local_scope, create_vars,
feed_holder_name, fetch_holder_name);
self.RunPreparedContext(ctx,
scope,
feed_targets,
fetch_targets,
create_local_scope,
create_vars,
feed_holder_name,
fetch_holder_name);
})
.def("run_prepared_ctx",
[](Executor &self, ExecutorPrepareContext *ctx, Scope *scope,
bool create_local_scope = true, bool create_vars = true,
[](Executor &self,
ExecutorPrepareContext *ctx,
Scope *scope,
bool create_local_scope = true,
bool create_vars = true,
bool keep_kids = false) {
pybind11::gil_scoped_release release;
self.RunPreparedContext(ctx, scope, create_local_scope,
create_vars, keep_kids);
self.RunPreparedContext(
ctx, scope, create_local_scope, create_vars, keep_kids);
})
.def("prepare",
[](Executor &self, const ProgramDesc &program, int block_id,
[](Executor &self,
const ProgramDesc &program,
int block_id,
const std::vector<std::string> &skip_ref_cnt_vars =
std::vector<std::string>(),
bool force_disable_gc = false) {
pybind11::gil_scoped_release release;
return self.Prepare(program, block_id, skip_ref_cnt_vars,
force_disable_gc);
return self.Prepare(
program, block_id, skip_ref_cnt_vars, force_disable_gc);
})
.def("create_variables", &Executor::CreateVariables)
.def("run", [](Executor &self, const ProgramDesc &prog, Scope *scope,
int block_id, bool create_local_scope, bool create_vars,
.def("run",
[](Executor &self,
const ProgramDesc &prog,
Scope *scope,
int block_id,
bool create_local_scope,
bool create_vars,
const std::vector<std::string> &fetch_vars) {
pybind11::gil_scoped_release release;
self.Run(prog, scope, block_id, create_local_scope, create_vars,
self.Run(prog,
scope,
block_id,
create_local_scope,
create_vars,
fetch_vars);
});
......@@ -2821,8 +2976,10 @@ All parameter, weight, gradient are variables in Paddle.
});
py::class_<framework::StandaloneExecutor>(m, "StandaloneExecutor")
.def(py::init<const platform::Place &, const ProgramDesc &,
const ProgramDesc &, Scope *>())
.def(py::init<const platform::Place &,
const ProgramDesc &,
const ProgramDesc &,
Scope *>())
.def("run",
[](StandaloneExecutor &self,
const std::unordered_map<std::string, py::array> &input_dict,
......@@ -2866,11 +3023,13 @@ All parameter, weight, gradient are variables in Paddle.
return py::cast(std::move(ret));
})
.def("run",
[](StandaloneExecutor &self, std::vector<std::string> feed_names,
[](StandaloneExecutor &self,
std::vector<std::string> feed_names,
std::vector<std::string> fetch_names) {
platform::RecordEvent record_event(
"StandaloneExecutor:run",
platform::TracerEventType::UserDefined, 1);
platform::TracerEventType::UserDefined,
1);
paddle::framework::FetchList ret;
{
pybind11::gil_scoped_release release;
......@@ -2951,20 +3110,29 @@ All parameter, weight, gradient are variables in Paddle.
});
m.def("memory_stat_get_current", memory::StatGetCurrentValue);
m.def("memory_stat_get_peak", memory::StatGetPeakValue);
m.def("run_cmd",
[](const std::string &cmd, int time_out = -1,
m.def(
"run_cmd",
[](const std::string &cmd,
int time_out = -1,
int sleep_inter = -1) -> const std::string {
return paddle::framework::shell_get_command_output(cmd, time_out,
sleep_inter);
return paddle::framework::shell_get_command_output(
cmd, time_out, sleep_inter);
},
py::arg("cmd"), py::arg("time_out") = -1, py::arg("sleep_inter") = -1);
m.def("shell_execute_cmd",
[](const std::string &cmd, int time_out = 0, int sleep_inter = 0,
py::arg("cmd"),
py::arg("time_out") = -1,
py::arg("sleep_inter") = -1);
m.def(
"shell_execute_cmd",
[](const std::string &cmd,
int time_out = 0,
int sleep_inter = 0,
bool redirect_stderr = false) -> std::vector<std::string> {
return paddle::framework::shell_execute_cmd(
cmd, time_out, sleep_inter, redirect_stderr);
},
py::arg("cmd"), py::arg("time_out") = 0, py::arg("sleep_inter") = 0,
py::arg("cmd"),
py::arg("time_out") = 0,
py::arg("sleep_inter") = 0,
py::arg("redirect_stderr") = false);
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
......@@ -2979,13 +3147,16 @@ All parameter, weight, gradient are variables in Paddle.
#endif
m.def("set_feed_variable",
static_cast<void (*)(Scope *, const LoDTensor &, const std::string &,
size_t)>(&framework::SetFeedVariable));
static_cast<void (*)(
Scope *, const LoDTensor &, const std::string &, size_t)>(
&framework::SetFeedVariable));
m.def("set_feed_variable",
static_cast<void (*)(Scope *, const Strings &, const std::string &,
size_t)>(&framework::SetFeedVariable));
static_cast<void (*)(
Scope *, const Strings &, const std::string &, size_t)>(
&framework::SetFeedVariable));
m.def("get_fetch_variable",
[](const Scope &scope, const std::string &var_name,
[](const Scope &scope,
const std::string &var_name,
size_t index) -> py::object {
auto &var = framework::GetFetchVariable(scope, var_name, index);
if (data_is_lod_tensor(var)) {
......@@ -3033,26 +3204,30 @@ All parameter, weight, gradient are variables in Paddle.
pylodtensorarray
.def("__init__",
[](LoDTensorArray &instance) { new (&instance) LoDTensorArray(); })
.def("__getitem__",
.def(
"__getitem__",
[](LoDTensorArray &self, size_t i) { return &self.at(i); },
py::return_value_policy::reference)
.def("__len__", [](LoDTensorArray &self) { return self.size(); })
.def("__setitem__",
[](LoDTensorArray &self, size_t i, const LoDTensor &t) {
PADDLE_ENFORCE_LT(i, self.size(),
PADDLE_ENFORCE_LT(i,
self.size(),
platform::errors::InvalidArgument(
"The index to set is larger than the size "
"of LoDTensorArray."));
self[i].ShareDataWith(t);
self[i].set_lod(t.lod());
})
.def("append",
.def(
"append",
[](LoDTensorArray &self, const LoDTensor &t) {
self.emplace_back();
self.back().ShareDataWith(t);
self.back().set_lod(t.lod());
},
py::arg("tensor"), R"DOC(
py::arg("tensor"),
R"DOC(
Append a LoDensor to LoDTensorArray.
Args:
......@@ -3072,7 +3247,8 @@ All parameter, weight, gradient are variables in Paddle.
t.set(np.ndarray([5, 30]), fluid.CPUPlace())
arr.append(t)
)DOC")
.def("_move_to_list",
.def(
"_move_to_list",
[](LoDTensorArray &self) -> py::list {
py::list res(self.size());
for (size_t i = 0; i < self.size(); ++i) {
......@@ -3086,7 +3262,8 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<FetchList>(m, "FetchList", R"DOC( FetchList is a
vector of boost::variant<LoDTensor, LoDTensorArray>.
)DOC")
.def("_move_to_list",
.def(
"_move_to_list",
[](FetchList &self) -> py::list {
py::list res(self.size());
for (size_t i = 0; i < self.size(); ++i) {
......@@ -3107,7 +3284,8 @@ All parameter, weight, gradient are variables in Paddle.
},
py::return_value_policy::take_ownership)
.def("append",
.def(
"append",
[](FetchList &self, const LoDTensor &t) {
self.emplace_back();
auto &lod_tensor = BOOST_GET(LoDTensor, self.back());
......@@ -3116,7 +3294,8 @@ All parameter, weight, gradient are variables in Paddle.
},
py::arg("var"))
.def("append",
.def(
"append",
[](FetchList &self, const LoDTensorArray &t) {
self.emplace_back();
auto &lod_tensor_array = BOOST_GET(LoDTensorArray, self.back());
......@@ -3130,7 +3309,8 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<FetchUnmergedList>(m, "FetchUnmergedList", R"DOC(
FetchUnmergedList is 2-D array of FetchType(boost::variant(LoDTensor, LoDTensorArray)).
)DOC")
.def("_move_to_list",
.def(
"_move_to_list",
[](FetchUnmergedList &self) -> py::list {
py::list res(self.size());
for (size_t i = 0; i < self.size(); ++i) {
......@@ -3168,7 +3348,8 @@ All parameter, weight, gradient are variables in Paddle.
}
platform::EmptyCache();
});
m.def("get_device_properties",
m.def(
"get_device_properties",
[](int id) -> const gpuDeviceProp & {
return platform::GetDeviceProperties(id);
},
......@@ -3283,16 +3464,18 @@ All parameter, weight, gradient are variables in Paddle.
m.def("reset_profiler", platform::ResetProfiler);
m.def("register_pass", [](const std::string &pass_type, py::object callable) {
PADDLE_ENFORCE_EQ(
framework::ir::PassRegistry::Instance().Has(pass_type), false,
framework::ir::PassRegistry::Instance().Has(pass_type),
false,
platform::errors::AlreadyExists("Pass '%s' is registered more than "
"once. Please use another name.",
pass_type));
callable.inc_ref();
framework::ir::PassRegistry::Instance().Insert(pass_type, [pass_type,
callable]() {
framework::ir::PassRegistry::Instance().Insert(
pass_type, [pass_type, callable]() {
py::gil_scoped_acquire guard;
std::unique_ptr<framework::ir::Pass> pass(
new framework::ir::GeneratePass(py::cast<std::string>(callable())));
new framework::ir::GeneratePass(
py::cast<std::string>(callable())));
return pass;
});
});
......@@ -3304,7 +3487,8 @@ All parameter, weight, gradient are variables in Paddle.
m.def("size_of_dtype", framework::SizeOfType);
py::class_<paddle::platform::ProfilerResult>(m, "_ProfilerResult")
.def(py::init<>())
.def("get_data", &paddle::platform::ProfilerResult::GetData,
.def("get_data",
&paddle::platform::ProfilerResult::GetData,
py::return_value_policy::automatic_reference)
.def("save", &paddle::platform::ProfilerResult::Save)
.def("get_extra_info", &paddle::platform::ProfilerResult::GetExtraInfo);
......@@ -3339,7 +3523,8 @@ All parameter, weight, gradient are variables in Paddle.
&paddle::platform::HostPythonNode::device_node_ptrs);
py::class_<paddle::platform::Profiler>(m, "_Profiler")
.def("create", &paddle::platform::Profiler::Create,
.def("create",
&paddle::platform::Profiler::Create,
py::return_value_policy::take_ownership)
.def("is_cupti_supported", &paddle::platform::Profiler::IsCuptiSupported)
.def("is_cnpapi_supported",
......@@ -3350,7 +3535,8 @@ All parameter, weight, gradient are variables in Paddle.
profiler->Prepare();
})
.def("start", &paddle::platform::Profiler::Start)
.def("stop",
.def(
"stop",
[](paddle::platform::Profiler *profiler) {
platform::DisableHostEventRecorder();
return profiler->Stop();
......@@ -3412,22 +3598,29 @@ All parameter, weight, gradient are variables in Paddle.
[](ir::Pass &self, const std::string &name, const std::string &attr) {
self.Set<std::string>(name, new std::string(attr));
})
.def("set", [](ir::Pass &self, const std::string &name,
bool val) { self.Set<bool>(name, new bool(val)); })
.def("set", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("set",
[](ir::Pass &self, const std::string &name,
[](ir::Pass &self, const std::string &name, bool val) {
self.Set<bool>(name, new bool(val));
})
.def("set",
[](ir::Pass &self, const std::string &name, int val) {
self.Set<const int>(name, new int(val));
})
.def("set",
[](ir::Pass &self,
const std::string &name,
std::vector<std::string> set) {
self.Set(name, new std::vector<std::string>(set));
})
.def("set",
[](ir::Pass &self, const std::string &name,
[](ir::Pass &self,
const std::string &name,
std::unordered_set<std::string> set) {
self.Set(name, new std::unordered_set<std::string>(set));
})
.def("set",
[](ir::Pass &self, const std::string &name,
[](ir::Pass &self,
const std::string &name,
std::unordered_set<int> set) {
self.Set(name, new std::unordered_set<int>(set));
})
......@@ -3604,7 +3797,8 @@ All parameter, weight, gradient are variables in Paddle.
},
R"DOC(This config that the this is distributed training with parameter server
)DOC")
.def_property("_dry_run",
.def_property(
"_dry_run",
[](const ExecutionStrategy &self) { return self.dry_run_; },
[](ExecutionStrategy &self, bool dry_run) {
self.dry_run_ = dry_run;
......@@ -3671,7 +3865,8 @@ All parameter, weight, gradient are variables in Paddle.
"reduce_strategy",
[](const BuildStrategy &self) { return self.reduce_; },
[](BuildStrategy &self, BuildStrategy::ReduceStrategy strategy) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3701,7 +3896,8 @@ All parameter, weight, gradient are variables in Paddle.
[](const BuildStrategy &self) { return self.gradient_scale_; },
[](BuildStrategy &self,
BuildStrategy::GradientScaleStrategy strategy) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3766,7 +3962,8 @@ All parameter, weight, gradient are variables in Paddle.
"debug_graphviz_path",
[](const BuildStrategy &self) { return self.debug_graphviz_path_; },
[](BuildStrategy &self, const std::string &path) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3793,7 +3990,8 @@ All parameter, weight, gradient are variables in Paddle.
return self.enable_sequential_execution_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3819,7 +4017,8 @@ All parameter, weight, gradient are variables in Paddle.
return self.remove_unnecessary_lock_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3856,7 +4055,8 @@ All parameter, weight, gradient are variables in Paddle.
const std::vector<std::string> &trainers_endpoints) {
self.trainers_endpoints_ = trainers_endpoints;
})
.def_property("trainer_id",
.def_property(
"trainer_id",
[](const BuildStrategy &self) { return self.trainer_id_; },
[](BuildStrategy &self, int trainer_id) {
self.trainer_id_ = trainer_id;
......@@ -3873,14 +4073,16 @@ All parameter, weight, gradient are variables in Paddle.
[](BuildStrategy &self, int bkcl_comm_num) {
self.bkcl_comm_num_ = bkcl_comm_num;
})
.def_property("use_hierarchical_allreduce",
.def_property(
"use_hierarchical_allreduce",
[](const BuildStrategy &self) {
return self.use_hierarchical_allreduce_;
},
[](BuildStrategy &self, bool use) {
self.use_hierarchical_allreduce_ = use;
})
.def_property("hierarchical_allreduce_inter_nranks",
.def_property(
"hierarchical_allreduce_inter_nranks",
[](const BuildStrategy &self) {
return self.hierarchical_allreduce_inter_nranks_;
},
......@@ -3894,7 +4096,8 @@ All parameter, weight, gradient are variables in Paddle.
return self.fuse_elewise_add_act_ops_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3919,7 +4122,8 @@ All parameter, weight, gradient are variables in Paddle.
"fuse_gemm_epilogue",
[](const BuildStrategy &self) { return self.fuse_gemm_epilogue_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3944,7 +4148,8 @@ All parameter, weight, gradient are variables in Paddle.
"fuse_bn_act_ops",
[](const BuildStrategy &self) { return self.fuse_bn_act_ops_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3969,7 +4174,8 @@ All parameter, weight, gradient are variables in Paddle.
"fuse_bn_add_act_ops",
[](const BuildStrategy &self) { return self.fuse_bn_add_act_ops_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -3994,7 +4200,8 @@ All parameter, weight, gradient are variables in Paddle.
"enable_auto_fusion",
[](const BuildStrategy &self) { return self.enable_auto_fusion_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -4022,7 +4229,8 @@ All parameter, weight, gradient are variables in Paddle.
return self.fuse_relu_depthwise_conv_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -4045,13 +4253,15 @@ All parameter, weight, gradient are variables in Paddle.
build_strategy = static.BuildStrategy()
build_strategy.fuse_relu_depthwise_conv = True
)DOC")
.def_property("fuse_broadcast_ops",
.def_property(
"fuse_broadcast_ops",
[](const BuildStrategy &self) {
return self.fuse_broadcast_ops_ == true ||
self.fuse_broadcast_ops_ == paddle::none;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, "
"cannot be configured again."));
......@@ -4075,13 +4285,15 @@ All parameter, weight, gradient are variables in Paddle.
build_strategy = static.BuildStrategy()
build_strategy.fuse_broadcast_ops = True
)DOC")
.def_property("fuse_all_optimizer_ops",
.def_property(
"fuse_all_optimizer_ops",
[](const BuildStrategy &self) {
return self.fuse_all_optimizer_ops_ == true ||
self.fuse_all_optimizer_ops_ == paddle::none;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, "
"cannot be configured again."));
......@@ -4091,7 +4303,8 @@ All parameter, weight, gradient are variables in Paddle.
"sync_batch_norm",
[](const BuildStrategy &self) { return self.sync_batch_norm_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE_NE(self.IsFinalized(), true,
PADDLE_ENFORCE_NE(self.IsFinalized(),
true,
platform::errors::PreconditionNotMet(
"BuildStrategy has been finlaized, cannot be "
"configured again."));
......@@ -4169,7 +4382,8 @@ All parameter, weight, gradient are variables in Paddle.
self.is_distribution_ = b;
#endif
})
.def_property("async_mode",
.def_property(
"async_mode",
[](const BuildStrategy &self) { return self.async_mode_; },
[](BuildStrategy &self, bool b) { self.async_mode_ = b; })
.def_property(
......@@ -4187,7 +4401,8 @@ All parameter, weight, gradient are variables in Paddle.
self.fuse_all_reduce_ops_ == paddle::none;
},
[](BuildStrategy &self, bool b) { self.fuse_all_reduce_ops_ = b; })
.def_property("enable_backward_optimizer_op_deps",
.def_property(
"enable_backward_optimizer_op_deps",
[](const BuildStrategy &self) {
return self.enable_backward_optimizer_op_deps_;
},
......@@ -4213,7 +4428,8 @@ All parameter, weight, gradient are variables in Paddle.
[](BuildStrategy &self, bool fix_op_run_order) {
self.fix_op_run_order_ = fix_op_run_order;
})
.def_property("allow_cuda_graph_capture",
.def_property(
"allow_cuda_graph_capture",
[](const BuildStrategy &self) {
return self.allow_cuda_graph_capture_;
},
......@@ -4226,7 +4442,8 @@ All parameter, weight, gradient are variables in Paddle.
new_bs.ClearFinalized();
return new_bs;
})
.def("_finalize_strategy_and_create_passes",
.def(
"_finalize_strategy_and_create_passes",
[](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
return self.CreatePassesFromStrategy(true);
},
......@@ -4241,14 +4458,19 @@ All parameter, weight, gradient are variables in Paddle.
});
pe.def(py::init<const std::vector<platform::Place> &,
const std::vector<std::string> &, const std::string &,
Scope *, std::vector<Scope *> &, const ExecutionStrategy &,
const BuildStrategy &, ir::Graph *>())
const std::vector<std::string> &,
const std::string &,
Scope *,
std::vector<Scope *> &,
const ExecutionStrategy &,
const BuildStrategy &,
ir::Graph *>())
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
// We still cannot get local_scope from this vector, since the element
// of vec<Scope*> will be freed by Python GC. We can only return Scope*
// one by one and mark them as reference.
.def("local_scopes",
.def(
"local_scopes",
[](ParallelExecutor &self) -> std::vector<Scope *> * {
return &self.GetLocalScopes();
},
......@@ -4284,7 +4506,8 @@ All parameter, weight, gradient are variables in Paddle.
std::unique_ptr<platform::ipu::IpuBackend, py::nodelete>>(
m, "IpuBackend")
// manage IpuBackend in C++
.def("get_instance",
.def(
"get_instance",
[]() {
return std::unique_ptr<platform::ipu::IpuBackend, py::nodelete>(
platform::ipu::IpuBackend::GetInstance());
......@@ -4330,7 +4553,8 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_THROW(platform::errors::Unimplemented(
"Failed to convert type: %s when set IpuStrategy "
"option: %s",
option.get_type(), option_name));
option.get_type(),
option_name));
}
self.InsertStringOption(option_name, option_val);
}
......@@ -4338,7 +4562,8 @@ All parameter, weight, gradient are variables in Paddle.
if (option_name.rfind("location_", 0) == 0) {
for (auto option : element.second.cast<py::dict>()) {
self.SetTensorLocation(
option_name, option.first.cast<std::string>(),
option_name,
option.first.cast<std::string>(),
option.second.cast<std::uint64_t>());
}
} else if (option_name == "accumulate_outer_fragment") {
......@@ -4386,17 +4611,19 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_THROW(platform::errors::Unimplemented(
"Failed to convert value type: %s when set "
"IpuStrategy option: %s",
option.second.get_type(), option_key));
option.second.get_type(),
option_key));
}
self.InsertStringPairOption(option_name, option_key,
option_val);
self.InsertStringPairOption(
option_name, option_key, option_val);
}
}
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"Invalid IpuStrategy option value type: %s, please check "
"input value for option: %s",
element.second.get_type(), option_name));
element.second.get_type(),
option_name));
}
}
})
......
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