def build_graph_with_sub_graph(self):
def linear_fc(num):
data = fluid.layers.data(name='image',
shape=[1, 32, 32],
dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
hidden = fluid.layers.fc(hidden, size=128, act='relu')
loss = fluid.layers.cross_entropy(input=hidden, label=label)
loss = fluid.layers.mean(loss)
return loss
main_program = Program()
startup_program = Program()
def true_func():
return linear_fc(3)
def false_func():
return linear_fc(5)
with program_guard(main_program, startup_program):
x = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
y = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
pred = layers.less_than(y, x)
out = layers.cond(pred, true_func, false_func)
core_graph = core.Graph(main_program.desc)
# We should create graph for test, otherwise it will throw a
# error that it cannot find the node of "STEP_COUNTER"
graph = IrGraph(core_graph, for_test=True)
sub_graph = graph.get_sub_graph(0)
all_sub_graphs = graph.all_sub_graphs(
for_test=True) # same reason for subgraph
# Should return graph and sub_graphs at the same time. If only return sub_graph, the graph will
# be destructed and the sub_graphs will be empty.
return graph, all_sub_graphs
def save_quantized_model(self,
model,
path,
input_spec=None,
onnx_format=False,
**config):
"""
Save the quantized model for the inference.
Args:
model (Layer): The model to be saved.
path (str): The path prefix to save model. The format is
``dirname/file_prefix`` or ``file_prefix``.
input_spec (list[InputSpec|Tensor], optional): Describes the input
of the saved model's forward method, which can be described by
InputSpec or example Tensor. If None, all input variables of
the original Layer's forward method would be the inputs of
the saved model. Default None.
onnx_format (bool, optional): Whether to export the quantized model
with format of ONNX. Default is False.
**configs (dict, optional): Other save configuration options for
compatibility. We do not recommend using these configurations,
they may be removed in the future. If not necessary, DO NOT use
them. Default None.
The following options are currently supported:
(1) output_spec (list[Tensor]): Selects the output targets of
the saved model. By default, all return variables of original
Layer's forward method are kept as the output of the saved model.
If the provided ``output_spec`` list is not all output variables,
the saved model will be pruned according to the given
``output_spec`` list.
Returns:
None
"""
assert isinstance(model, dygraph.Layer), \
"The model must be the instance of dygraph.Layer."
paddle.jit.save(layer=model,
path=path,
input_spec=input_spec,
**config)
is_dynamic_mode = False
if paddle.in_dynamic_mode():
is_dynamic_mode = True
paddle.enable_static()
place = core.CPUPlace()
scope = global_scope()
exe = Executor(place)
dirname = os.path.dirname(path)
basename = os.path.basename(path)
model_filename = basename + INFER_MODEL_SUFFIX
params_filename = basename + INFER_PARAMS_SUFFIX
[infer_program, feed_target_names, fetch_targets
] = (load_inference_model(dirname=dirname,
executor=exe,
model_filename=model_filename,
params_filename=params_filename))
self._gather_scales(infer_program, scope, fetch_targets)
# Remove `moving_average_abs_max_scale` node in sub graphs.
graph = IrGraph(core.Graph(infer_program.desc), for_test=False)
for sub_graph in graph.all_sub_graphs():
for _op in sub_graph.all_op_nodes():
if _op.name() == "moving_average_abs_max_scale":
sub_graph.safe_remove_nodes(_op)
sub_graph.resolve_hazard()
infer_program = graph.to_program()
self._set_skip_quant_attr(infer_program)
clip_extra = False
if onnx_format:
graph = IrGraph(core.Graph(infer_program.desc), for_test=False)
transform_pass = ReplaceFakeQuantDequantPass(scope, place)
transform_pass.apply(graph)
quant_weight_pass = QuantWeightPass(scope, place)
quant_weight_pass.apply(graph)
infer_program = graph.to_program()
clip_extra = True
save_inference_model(dirname=dirname,
feeded_var_names=feed_target_names,
target_vars=fetch_targets,
executor=exe,
main_program=infer_program.clone(),
model_filename=model_filename,
params_filename=params_filename,
clip_extra=clip_extra)
if is_dynamic_mode:
paddle.disable_static()