未验证 提交 f23691db 编写于 作者: Y Yang yaming 提交者: GitHub

Merge pull request #7434 from pkuyym/fix-7195

Add static_input for DynamicRNN
......@@ -1291,6 +1291,26 @@ class DynamicRNN(object):
outputs={'Out': input_array})
return array_read(array=input_array, i=self.step_idx)
def static_input(self, x):
self._assert_in_rnn_block_("static_input")
if not isinstance(x, Variable):
raise TypeError(
"static_input() can only take a Variable as its input")
if self.lod_rank_table is None:
raise RuntimeError(
"static_input() must be called after step_input().")
parent_block = self._parent_block_()
x_reordered = parent_block.create_var(
name=unique_name("dynamic_rnn_static_input_reordered"),
type=core.VarDesc.VarType.LOD_TENSOR,
dtype=x.dtype)
parent_block.append_op(
type='reorder_lod_tensor_by_rank',
inputs={'X': [x],
'RankTable': [self.lod_rank_table]},
outputs={'Out': [x_reordered]})
return shrink_memory(x_reordered, self.step_idx, self.lod_rank_table)
@contextlib.contextmanager
def block(self):
if self.status != DynamicRNN.BEFORE_RNN:
......
import unittest
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid as fluid
from paddle.v2.fluid.backward import append_backward
import paddle.v2.fluid.framework as framework
from paddle.v2.fluid.framework import Program, switch_main_program
import bisect
import numpy as np
fluid.default_startup_program().random_seed = 1
class TestDyRnnStaticInput(unittest.TestCase):
def setUp(self):
self._delta = 0.005
self._max_sequence_len = 3
self._program = Program()
switch_main_program(self._program)
self.output_dim = 10
self.place = core.CPUPlace()
self.prepare_x_tensor()
self.prepare_static_input_tensor()
self.exe = fluid.Executor(self.place)
def prepare_x_tensor(self):
self.x_tensor_dim = 10
lod = [[0, 2, 3, 6]]
shape = [lod[0][-1], self.x_tensor_dim]
self.x_tensor_data = np.random.random(shape).astype('float32')
self.x_tensor = core.LoDTensor()
self.x_tensor.set_lod(lod)
self.x_tensor.set(self.x_tensor_data, self.place)
def prepare_static_input_tensor(self):
self.static_input_tensor_dim = 4
lod = [[0, 1, 3, 6]]
shape = [lod[0][-1], self.static_input_tensor_dim]
self.static_input_data = np.random.random(shape).astype('float32')
self.static_input_tensor = core.LoDTensor()
self.static_input_tensor.set_lod(lod)
self.static_input_tensor.set(self.static_input_data, self.place)
def fetch_value(self, var):
fetch_outs = self.exe.run(feed={
'x_tensor': self.x_tensor,
'static_input_tensor': self.static_input_tensor
},
fetch_list=[var],
return_numpy=False)
return self._lodtensor_to_ndarray(fetch_outs[0])
def _lodtensor_to_ndarray(self, lod_tensor):
dims = lod_tensor.get_dims()
ndarray = np.zeros(shape=dims).astype('float32')
for i in xrange(np.product(dims)):
ndarray.ravel()[i] = lod_tensor.get_float_element(i)
return ndarray, lod_tensor.lod()
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(x=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 get_seq_len_from_lod(self, lod):
return [lod[0][i + 1] - lod[0][i] for i in xrange(len(lod[0]) - 1)]
def get_expected_static_step_outs(self):
x_lod = self.x_tensor.lod()
x_seq_len = self.get_seq_len_from_lod(x_lod)
x_seq_len_sorted = sorted(x_seq_len)
x_sorted_indices = np.argsort(x_seq_len)[::-1]
static_lod = self.static_input_tensor.lod()
static_sliced = [
self.static_input_data[static_lod[0][i]:static_lod[0][i + 1]]
for i in xrange(len(static_lod[0]) - 1)
]
static_seq_len = self.get_seq_len_from_lod(static_lod)
static_reordered = []
for i in xrange(len(x_sorted_indices)):
static_reordered.extend(static_sliced[x_sorted_indices[i]].tolist())
static_seq_len_reordered = [
static_seq_len[x_sorted_indices[i]]
for i in xrange(len(x_sorted_indices))
]
static_step_outs = []
static_step_lods = []
for i in xrange(self._max_sequence_len):
end = len(x_seq_len) - bisect.bisect_left(x_seq_len_sorted, i + 1)
lod = [0]
for i in xrange(end):
lod.append(static_seq_len_reordered[i] + lod[-1])
static_step_lods.append([lod])
end = lod[-1]
static_step_outs.append(
np.array(static_reordered[:end]).astype('float32'))
return static_step_outs, static_step_lods
def test_step_out(self):
static_step_outs = self.build_graph(only_forward=True)
self.exe.run(framework.default_startup_program())
expected_outs, expected_lods = self.get_expected_static_step_outs()
for i in xrange(self._max_sequence_len):
step_out, lod = self.fetch_value(static_step_outs[i])
self.assertTrue(np.allclose(step_out, expected_outs[i]))
self.assertTrue(np.allclose(lod, expected_lods[i]))
def test_network_gradient(self):
static_input_grad, loss = self.build_graph()
self.exe.run(framework.default_startup_program())
actual_gradients, actual_lod = self.fetch_value(static_input_grad)
static_input_shape = self.static_input_tensor.get_dims()
numeric_gradients = np.zeros(shape=static_input_shape).astype('float32')
# calculate numeric gradients
tensor_size = np.product(static_input_shape)
for i in xrange(tensor_size):
origin = self.static_input_tensor.get_float_element(i)
x_pos = origin + self._delta
self.static_input_tensor.set_float_element(i, x_pos)
y_pos = self.fetch_value(loss)[0][0]
x_neg = origin - self._delta
self.static_input_tensor.set_float_element(i, x_neg)
y_neg = self.fetch_value(loss)[0][0]
self.static_input_tensor.set_float_element(i, origin)
numeric_gradients.ravel()[i] = (y_pos - y_neg) / self._delta / 2
self.assertTrue(np.allclose(actual_gradients, numeric_gradients, 0.001))
self.assertTrue(np.allclose(actual_lod, self.static_input_tensor.lod()))
if __name__ == '__main__':
unittest.main()
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