未验证 提交 738c8464 编写于 作者: S songyouwei 提交者: GitHub

support tensor array create read write in dygraph (#23200)

* support tensor array create read write in dygraph
test=develop

* minor fix
test=develop

* support tensor_array_to_tensor
test=develop

* add while loop case and err msg
test=develop

* refine ut
test=develop
上级 f944b0f6
......@@ -1287,6 +1287,31 @@ def array_write(x, i, array=None):
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
if in_dygraph_mode():
assert isinstance(
x, Variable
), "The input data 'x' in array_write must be Variable in dygraph mode"
assert isinstance(
i, Variable
), "The index 'i' in array_write must be Variable in dygraph mode"
assert i.shape == [
1
], "The shape of index 'i' should be [1] in dygraph mode"
i = i.numpy()[0]
if array is None:
array = create_array(x.dtype)
assert isinstance(
array,
list), "The 'array' in array_write must be a list in dygraph mode"
assert i <= len(
array
), "The index 'i' should not be greater than the length of 'array' in dygraph mode"
if i < len(array):
array[i] = x
else:
array.append(x)
return array
helper = LayerHelper('array_write', **locals())
if array is None:
array = helper.create_variable(
......@@ -1322,6 +1347,9 @@ def create_array(dtype):
data = fluid.layers.create_array(dtype='float32') # Create a float32 LoDTensorArray.
"""
if in_dygraph_mode():
return []
helper = LayerHelper("array", **locals())
return helper.create_variable(
name="{0}.out".format(helper.name),
......@@ -1643,6 +1671,19 @@ def array_read(array, i):
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
if in_dygraph_mode():
assert isinstance(
array,
list), "The 'array' in array_read must be list in dygraph mode"
assert isinstance(
i, Variable
), "The index 'i' in array_read must be Variable in dygraph mode"
assert i.shape == [
1
], "The shape of index 'i' should be [1] in dygraph mode"
i = i.numpy()[0]
return array[i]
helper = LayerHelper('array_read', **locals())
if not isinstance(
array,
......@@ -1739,6 +1780,12 @@ def array_length(array):
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
if in_dygraph_mode():
assert isinstance(
array,
list), "The 'array' in array_write must be a list in dygraph mode"
return len(array)
helper = LayerHelper('array_length', **locals())
tmp = helper.create_variable_for_type_inference(dtype='int64')
tmp.stop_gradient = True
......
......@@ -381,6 +381,17 @@ def tensor_array_to_tensor(input, axis=1, name=None, use_stack=False):
fluid.layers.array_write(x1, i + 1, array)
output, output_index = fluid.layers.tensor_array_to_tensor(input=array)
"""
if in_dygraph_mode():
assert isinstance(
input, list), "The 'input' in tensor_array_to_tensor must be list"
from .nn import stack, concat
from ..dygraph import to_variable
op = stack if use_stack else concat
res = op(input, axis=axis)
sizes = to_variable(
numpy.array(list(map(lambda x: int(x.shape[axis]), input))))
return res, sizes
helper = LayerHelper('tensor_array_to_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
out_index = helper.create_variable_for_type_inference(dtype="int32")
......
......@@ -15,6 +15,7 @@
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers as layers
from paddle.fluid.executor import Executor
......@@ -23,18 +24,7 @@ from paddle.fluid.framework import default_main_program
import numpy
class TestArrayReadWrite(unittest.TestCase):
def test_read_write(self):
x = [
layers.data(
name='x0', shape=[100]), layers.data(
name='x1', shape=[100]), layers.data(
name='x2', shape=[100])
]
for each_x in x:
each_x.stop_gradient = False
def _test_read_write(x):
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = False
arr = layers.array_write(x=x[0], i=i)
......@@ -63,18 +53,30 @@ class TestArrayReadWrite(unittest.TestCase):
x_sum = layers.sums(input=[mean_x0, mean_x1, mean_x2])
scope = core.Scope()
cpu = core.CPUPlace()
return a_sum, x_sum
exe = Executor(cpu)
class TestArrayReadWrite(unittest.TestCase):
def test_read_write(self):
x = [
layers.data(
name='x0', shape=[100]), layers.data(
name='x1', shape=[100]), layers.data(
name='x2', shape=[100])
]
for each_x in x:
each_x.stop_gradient = False
tensor = numpy.random.random(size=(100, 100)).astype('float32')
a_sum, x_sum = _test_read_write(x)
place = core.CPUPlace()
exe = Executor(place)
outs = exe.run(feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=[a_sum, x_sum],
scope=scope)
scope=core.Scope())
self.assertEqual(outs[0], outs[1])
total_sum = layers.sums(input=[a_sum, x_sum])
......@@ -100,6 +102,28 @@ class TestArrayReadWrite(unittest.TestCase):
# the input gradient should also be 1
self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
with fluid.dygraph.guard(place):
tensor1 = fluid.dygraph.to_variable(tensor)
tensor2 = fluid.dygraph.to_variable(tensor)
tensor3 = fluid.dygraph.to_variable(tensor)
x_dygraph = [tensor1, tensor2, tensor3]
for each_x in x_dygraph:
each_x.stop_gradient = False
a_sum_dygraph, x_sum_dygraph = _test_read_write(x_dygraph)
self.assertEqual(a_sum_dygraph, x_sum_dygraph)
total_sum_dygraph = layers.sums(
input=[a_sum_dygraph, x_sum_dygraph])
total_sum_scaled_dygraph = layers.scale(
x=total_sum_dygraph, scale=1 / 6.0)
total_sum_scaled_dygraph.backward()
g_out_dygraph = [
item._grad_ivar().numpy().sum() for item in x_dygraph
]
g_out_sum_dygraph = numpy.array(g_out_dygraph).sum()
self.assertAlmostEqual(1.0, g_out_sum_dygraph, delta=0.1)
if __name__ == '__main__':
unittest.main()
......@@ -204,17 +204,61 @@ class TestLoDTensorArrayStack(unittest.TestCase):
class TestTensorArrayToTensorAPI(unittest.TestCase):
def test_case(self):
x0 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
x1 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
def _test_case(self, inp1, inp2):
x0 = fluid.layers.assign(inp1)
x0.stop_gradient = False
x1 = fluid.layers.assign(inp2)
x1.stop_gradient = False
i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
array = fluid.layers.create_array(dtype='float32')
fluid.layers.array_write(x0, i, array)
fluid.layers.array_write(x1, i + 1, array)
output, output_index = fluid.layers.tensor_array_to_tensor(
output_stack, output_index_stack = fluid.layers.tensor_array_to_tensor(
input=array, axis=1, use_stack=True)
output, output_index = fluid.layers.tensor_array_to_tensor(
output_concat, output_index_concat = fluid.layers.tensor_array_to_tensor(
input=array, axis=1, use_stack=False)
return output_stack, output_index_stack, output_concat, output_index_concat
def test_case(self):
inp0 = numpy.random.rand(2, 3, 4).astype("float32")
inp1 = numpy.random.rand(2, 3, 4).astype("float32")
_outs_static = self._test_case(inp0, inp1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
outs_static = exe.run(fetch_list=list(_outs_static))
with fluid.dygraph.guard(place):
outs_dynamic = self._test_case(inp0, inp1)
for s, d in zip(outs_static, outs_dynamic):
self.assertTrue(numpy.array_equal(s, d.numpy()))
def test_while_loop_case(self):
with fluid.dygraph.guard():
zero = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
ten = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
array = fluid.layers.create_array(dtype='float32')
inp0 = numpy.random.rand(2, 3, 4).astype("float32")
x0 = fluid.layers.assign(inp0)
fluid.layers.array_write(x0, zero, array)
def cond(i, end, array):
return fluid.layers.less_than(i, end)
def body(i, end, array):
prev = fluid.layers.array_read(array, i - 1)
fluid.layers.array_write(prev, i, array)
return i + 1, end, array
_, _, array = fluid.layers.while_loop(cond, body, [i, ten, array])
self.assertTrue(fluid.layers.array_length(array), 10)
last = fluid.layers.fill_constant(shape=[1], dtype='int64', value=9)
self.assertTrue(
numpy.array_equal(
fluid.layers.array_read(array, last).numpy(), inp0))
if __name__ == '__main__':
......
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