提交 f0df62f1 编写于 作者: S sneaxiy

add more unittest case

test=develop
上级 f6741df4
......@@ -35,6 +35,9 @@ size_t AppendPythonCallableObjectAndReturnId(const py::object &py_obj) {
return g_py_callables.size() - 1;
}
// Return py::object* instead of py::object
// Returning py::object would cause reference count increasing
// but without GIL, reference count in Python may not be safe
static py::object *GetPythonCallableObject(size_t i) {
PADDLE_ENFORCE_LT(i, g_py_callables.size(), "Invalid python callable id");
return &g_py_callables[i];
......@@ -47,7 +50,7 @@ static std::string PythonObjectToString(const py::object &py_callable) {
static void CallPythonFunc(py::object *callable,
const std::vector<framework::LoDTensor> &ins,
std::vector<framework::LoDTensor *> *out) {
std::vector<framework::LoDTensor *> *outs) {
py::gil_scoped_acquire guard;
py::tuple in_args(ins.size());
for (size_t i = 0; i < ins.size(); ++i) {
......@@ -57,8 +60,8 @@ static void CallPythonFunc(py::object *callable,
auto ret = (*callable)(*in_args);
auto ret_tuple = py::cast<py::tuple>(ret);
size_t ret_num = py::len(ret_tuple);
size_t out_num = out->size();
if (ret_num != out_num) {
size_t out_num = outs->size();
if (UNLIKELY(ret_num != out_num)) {
// Python function has no return values or returns None
// In this case, ret_num = 1 && ret[0] == None && out_num should be 0
// Otherwise, ret_num must be equal to out_num
......@@ -69,17 +72,18 @@ static void CallPythonFunc(py::object *callable,
}
for (size_t i = 0; i < out_num; ++i) {
if ((*out)[i] == nullptr) {
auto *out = (*outs)[i];
if (out == nullptr) {
continue;
}
try {
auto *out_tensor = py::cast<framework::LoDTensor *>(ret_tuple[i]);
PADDLE_ENFORCE_NOT_NULL(out_tensor,
auto *py_out_tensor = py::cast<framework::LoDTensor *>(ret_tuple[i]);
PADDLE_ENFORCE_NOT_NULL(py_out_tensor,
"Output tensor %d should not be nullptr", i);
(*out)[i]->set_lod(out_tensor->lod());
(*out)[i]->ShareDataWith(*out_tensor);
out->set_lod(py_out_tensor->lod());
out->ShareDataWith(*py_out_tensor);
} catch (py::cast_error &) {
PADDLE_THROW("Output %d is not LoDTensor", i);
PADDLE_THROW("The %d-th output must be LoDTensor", i);
}
}
}
......@@ -94,6 +98,10 @@ class PyFuncOpShapeInference : public framework::InferShapeBase {
PADDLE_ENFORCE_GE(ctx->Attrs().Get<int>(kForwardPythonCallableId), 0,
"Function id cannot be less than 0");
// Transverse all outputs
// If name of any output ends with @GRAD,
// set its shape, dtype, lod_level, type to be the same as
// the correponding forward variable
auto *op = boost::get<const framework::OpDesc *>(ctx->GetOp());
auto *block = op->Block();
const std::string kGradVarSuffix = framework::kGradVarSuffix;
......@@ -115,7 +123,7 @@ class PyFuncOpShapeInference : public framework::InferShapeBase {
auto *in_var_desc = block->FindVarRecursive(fwd_var_name);
PADDLE_ENFORCE_NOT_NULL(in_var_desc, "Forward variable %s not found",
fwd_var_name);
VLOG(10) << "Infer shape of Out(" << out_name << ") as Input("
VLOG(10) << "Infer shape of Output(" << out_name << ") as Input("
<< in_var_desc->Name() << ")";
out_var_desc->SetShape(in_var_desc->GetShape());
out_var_desc->SetDataType(in_var_desc->GetDataType());
......@@ -135,7 +143,7 @@ class PyFuncOpMaker : public framework::OpProtoAndCheckerMaker {
"Index of registered forward Python function.")
.SetDefault(0);
AddAttr<int>(kBackwardPythonCallableId,
"Index of registered backward Python function")
"Index of registered backward Python function.")
.SetDefault(-1);
AddAttr<std::vector<std::string>>(kPyFuncBackwardSkipVars,
"Unused forward in/out in backward op")
......@@ -170,8 +178,7 @@ class PyFuncOpGradDescMaker : public framework::GradOpDescMakerBase {
auto fwd_outs = Output("Out");
// For memory reused, some inputs/output in forward part may be not needed
// in backward part
// Just skip these vars
// in backward part. Skipping these vars helps to save memory
auto &backward_skip_var_list = boost::get<std::vector<std::string>>(
fwd_attrs.at(kPyFuncBackwardSkipVars));
std::unordered_set<std::string> backward_skip_var_set(
......
......@@ -104,7 +104,7 @@ PYBIND11_MODULE(core, m) {
BindException(&m);
m.def(
"append_python_callable_object_and_return_id",
"_append_python_callable_object_and_return_id",
[](py::object py_obj) -> size_t {
return paddle::operators::AppendPythonCallableObjectAndReturnId(py_obj);
});
......
......@@ -9137,8 +9137,13 @@ class PyFuncRegistry(object):
self._func = func
# find named args using reflection
self._named_args = inspect.getargspec(self._func)[0]
self._id = core.append_python_callable_object_and_return_id(self)
args = inspect.getargspec(self._func)
if len(args[0]) == 0 and args[1] is None and args[2] is None:
# Function with no inputs
self._named_args = None
else:
self._named_args = args[0]
self._id = core._append_python_callable_object_and_return_id(self)
'''
Why record self here?
......@@ -9168,13 +9173,16 @@ class PyFuncRegistry(object):
return self._id
def __call__(self, *args):
if self._named_args is None:
func_ret = self._func()
else:
kwargs = dict()
idx = 0
for arg in self._named_args:
kwargs[arg] = args[idx]
idx += 1
func_ret = self._func(*args[idx:], **kwargs)
if not isinstance(func_ret, (list, tuple)):
func_ret = (func_ret, )
......@@ -9207,14 +9215,18 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
User should set the right data type and shape of :code:`out` before
calling this function. However, data types and shapes of gradients of
:code:`out` and :code:`x` would be infered automatically.
:code:`out` and :code:`x` would be inferred automatically.
The orders of inputs of :code:`backward_func` would be: forward input
:code:`x`, forward output :code:`out` and backward input gradient of
Input orders of :code:`backward_func` would be: forward inputs
:code:`x`, forward outputs :code:`out` and backward input gradients of
:code:`out`. If some variables of :code:`out` have no gradient, the input
tensor would be None in Python side. If some variables of :code:`in` have
no gradient, users should return None.
This function can also be used to debug the running network. User can
add a :code:`py_func` operator without output, and print input
:code:`x` inside :code:`func`.
Args:
func (callable): forward Python function.
x (Variable|list(Variable)|tuple(Variable)): inputs of :code:`func`.
......
......@@ -25,6 +25,14 @@ if fluid.core.is_compiled_with_cuda():
os.environ['CPU_NUM'] = str(dev_cnt)
def dummy_func_with_no_input():
return float(1.0)
def dummy_func_with_no_output(x):
pass
def tanh(x):
return np.tanh(x)
......@@ -86,13 +94,20 @@ def simple_fc_net(img, label, use_py_func_op):
else:
loss = fluid.default_main_program().current_block().create_var(
name='loss', dtype='float32', shape=[-1, 1])
fluid.layers.py_func(
loss = fluid.layers.py_func(
func=cross_entropy,
x=[prediction, label],
out=loss,
backward_func=cross_entropy_grad,
skip_vars_in_backward_input=loss)
dummy_var = fluid.default_main_program().current_block().create_var(
name='test_tmp_var', dtype='float32', shape=[1])
fluid.layers.py_func(
func=dummy_func_with_no_input, x=None, out=dummy_var)
fluid.layers.py_func(func=dummy_func_with_no_output, x=loss, out=None)
loss = fluid.layers.mean(loss)
return loss
......
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