def build_graph(self, only_forward=False):
x_tensor = fluid.layers.data(
name='x_tensor',
shape=[self.x_tensor_dim],
dtype='float32',
lod_level=1)
x_tensor.stop_gradient = False
static_input_tensor = fluid.layers.data(
name='static_input_tensor',
shape=[self.static_input_tensor_dim],
dtype='float32',
lod_level=1)
static_input_tensor.stop_gradient = False
if only_forward:
static_input_out_array = self._program.global_block().create_var(
name='static_input_out_array',
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype='float32')
static_input_out_array.stop_gradient = True
rnn = fluid.layers.DynamicRNN()
with rnn.block():
step_x = rnn.step_input(x_tensor)
step_static_input = rnn.static_input(static_input_tensor)
if only_forward:
fluid.layers.array_write(
x=step_static_input,
i=rnn.step_idx,
array=static_input_out_array)
last = fluid.layers.sequence_pool(
input=step_static_input, pool_type='last')
projected = fluid.layers.fc(input=[step_x, last],
size=self.output_dim)
rnn.output(projected)
if only_forward:
static_input_step_outs = []
step_idx = fluid.layers.fill_constant(
shape=[1], dtype='int64', value=0)
step_idx.stop_gradient = True
for i in xrange(self._max_sequence_len):
step_out = fluid.layers.array_read(static_input_out_array,
step_idx)
step_out.stop_gradient = True
static_input_step_outs.append(step_out)
fluid.layers.increment(x=step_idx, value=1.0, in_place=True)
if only_forward:
return static_input_step_outs
last = fluid.layers.sequence_pool(input=rnn(), pool_type='last')
loss = fluid.layers.mean(last)
append_backward(loss)
static_input_grad = self._program.global_block().var(
framework.grad_var_name('static_input_tensor'))
return static_input_grad, loss
def build_graph(self, only_forward=False):
x_tensor = fluid.layers.data(name='x_tensor',
shape=[self.x_tensor_dim],
dtype='float32',
lod_level=1)
x_tensor.stop_gradient = False
static_input_tensor = fluid.layers.data(
name='static_input_tensor',
shape=[self.static_input_tensor_dim],
dtype='float32',
lod_level=1)
static_input_tensor.stop_gradient = False
if only_forward:
static_input_out_array = self._program.global_block().create_var(
name='static_input_out_array',
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype='float32')
static_input_out_array.stop_gradient = True
rnn = fluid.layers.DynamicRNN()
with rnn.block():
step_x = rnn.step_input(x_tensor)
step_static_input = rnn.static_input(static_input_tensor)
if only_forward:
fluid.layers.array_write(x=step_static_input,
i=rnn.step_idx,
array=static_input_out_array)
last = fluid.layers.sequence_pool(input=step_static_input,
pool_type='last')
projected = fluid.layers.fc(input=[step_x, last],
size=self.output_dim)
rnn.output(projected)
if only_forward:
static_input_step_outs = []
step_idx = fluid.layers.fill_constant(shape=[1],
dtype='int64',
value=0)
step_idx.stop_gradient = True
for i in xrange(self._max_sequence_len):
step_out = fluid.layers.array_read(static_input_out_array,
step_idx)
step_out.stop_gradient = True
static_input_step_outs.append(step_out)
fluid.layers.increment(x=step_idx, value=1.0, in_place=True)
if only_forward:
return static_input_step_outs
last = fluid.layers.sequence_pool(input=rnn(), pool_type='last')
loss = fluid.layers.mean(last)
append_backward(loss)
static_input_grad = self._program.global_block().var(
framework.grad_var_name('static_input_tensor'))
return static_input_grad, loss
def __set_tensor__(name, data=None):
out_tensor = scope.find_var(name).get_tensor()
grad_tensor = scope.var(grad_var_name(name)).get_tensor()
out_dtype = out_tensor.dtype()
if data is None:
if out_dtype == core.VarDesc.VarType.FP64:
data = np.ones(out_tensor.shape(), dtype=np.float64)
elif out_dtype == core.VarDesc.VarType.FP32:
data = np.ones(out_tensor.shape(), dtype=np.float32)
else:
raise ValueError("Not supported data type " + str(out_dtype))
grad_tensor.set(data, place)
def __set_tensor__(name, data=None):
out_tensor = scope.find_var(name).get_tensor()
grad_tensor = scope.var(grad_var_name(name)).get_tensor()
out_dtype = out_tensor.dtype()
if data is None:
if out_dtype == core.VarDesc.VarType.FP64:
data = np.ones(out_tensor.shape(), dtype=np.float64)
elif out_dtype == core.VarDesc.VarType.FP32:
data = np.ones(out_tensor.shape(), dtype=np.float32)
else:
raise ValueError("Not supported data type " + str(out_dtype))
grad_tensor.set(data, place)
def backward(self):
self.feed_map = {
x: create_tensor(getattr(self.py_argsort, x), self.place)
for x in self.feed_data_field
}
fetch_list = [
self.main_program.global_block().var(grad_var_name(x))
for x in self.grad_data_field
]
exe = Executor(self.place)
out = exe.run(self.main_program,
feed=self.feed_map,
fetch_list=fetch_list,
return_numpy=False)
return out
def backward(self):
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.data_field
}
fetch_list = [
self.main_program.global_block().var(grad_var_name(x))
for x in self.data_field
]
exe = Executor(self.place)
return exe.run(self.main_program,
feed=self.feed_map,
fetch_list=fetch_list,
return_numpy=False)
def backward(self):
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.data_field
}
fetch_list = [
self.main_program.global_block().var(grad_var_name(x))
for x in self.data_field
]
gc_vars = core._get_eager_deletion_vars(
self.main_program.desc, [var.name for var in fetch_list])
self.assertEqual(len(gc_vars), self.main_program.num_blocks)
exe = Executor(self.place)
return exe.run(self.main_program,
feed=self.feed_map,
fetch_list=fetch_list,
return_numpy=False)
def backward(self):
self.feed_map = {
x: create_tensor(getattr(self.py_rnn, x), self.place)
for x in self.data_field
}
fetch_list = [
self.main_program.global_block().var(grad_var_name(x))
for x in self.data_field
]
build_strategy = fluid.BuildStrategy()
build_strategy.enable_inplace = True
exec_strategy = fluid.ExecutionStrategy()
parallel_exe = fluid.ParallelExecutor(use_cuda=False,
loss_name=self.output.name,
main_program=self.main_program,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
return parallel_exe.run(feed=self.feed_map,
fetch_list=fetch_list,
return_numpy=False)
