def test_simple_routine(self):
ch = fluid.make_channel(
dtype=core.VarDesc.VarType.BOOL, name="CreateChannel")
with fluid.Go():
fluid.channel_send(ch, True)
result = fluid.channel_recv(ch)
fluid.channel_close(ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
outs = exe.run(fetch_list=[result])
self.assertEqual(outs[0], True)
def test_simple_routine(self):
ch = fluid.make_channel(dtype=core.VarDesc.VarType.BOOL,
name="CreateChannel")
with fluid.Go():
fluid.channel_send(ch, True)
result = fluid.channel_recv(ch)
fluid.channel_close(ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
outs = exe.run(fetch_list=[result])
self.assertEqual(outs[0], True)
def test_daisy_chain(self):
'''
Mimics classic Daisy-chain test: https://talks.golang.org/2012/concurrency.slide#39
'''
n = 100
leftmost = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
left = leftmost
# TODO(thuan): Use fluid.While() after scope capture is implemented.
# https://github.com/PaddlePaddle/Paddle/issues/8502
for i in range(n):
right = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
one_tensor = self._create_one_dim_tensor(1)
result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
result, status = fluid.channel_recv(right, result)
one_added = fluid.layers.elementwise_add(x=one_tensor,
y=result)
fluid.channel_send(left, one_added)
left = right
# Trigger the channel propagation by sending a "1" to rightmost channel
with fluid.Go():
one_tensor = self._create_one_dim_tensor(1)
fluid.channel_send(right, one_tensor)
leftmost_result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
leftmost_result, status = fluid.channel_recv(leftmost, leftmost_result)
cpu = core.CPUPlace()
exe = Executor(cpu)
leftmost_data = exe.run(fetch_list=[leftmost_result])
# The leftmost_data should be equal to the number of channels + 1
self.assertEqual(leftmost_data[0][0], n + 1)
def test_daisy_chain(self):
'''
Mimics classic Daisy-chain test: https://talks.golang.org/2012/concurrency.slide#39
'''
n = 100
leftmost = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
left = leftmost
# TODO(thuan): Use fluid.While() after scope capture is implemented.
# https://github.com/PaddlePaddle/Paddle/issues/8502
for i in range(n):
right = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
one_tensor = self._create_one_dim_tensor(1)
result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
result, status = fluid.channel_recv(right, result)
one_added = fluid.layers.elementwise_add(x=one_tensor, y=result)
fluid.channel_send(left, one_added)
left = right
# Trigger the channel propagation by sending a "1" to rightmost channel
with fluid.Go():
one_tensor = self._create_one_dim_tensor(1)
fluid.channel_send(right, one_tensor)
leftmost_result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
leftmost_result, status = fluid.channel_recv(leftmost, leftmost_result)
cpu = core.CPUPlace()
exe = Executor(cpu)
leftmost_data = exe.run(fetch_list=[leftmost_result])
# The leftmost_data should be equal to the number of channels + 1
self.assertEqual(leftmost_data[0][0], n + 1)
def test_simple_routine(self):
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
# Create LOD_TENSOR<INT64> and put it into the scope. This placeholder
# variable will be filled in and returned by fluid.channel_recv
result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
with fluid.Go():
input_value = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.FP64, value=1234)
fluid.channel_send(ch, input_value)
result, status = fluid.channel_recv(ch, result)
fluid.channel_close(ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
outs = exe.run(fetch_list=[result])
self.assertEqual(outs[0], 1234)
def test_simple_routine(self):
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
# Create LOD_TENSOR<INT64> and put it into the scope. This placeholder
# variable will be filled in and returned by fluid.channel_recv
result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
with fluid.Go():
input_value = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.FP64,
value=1234)
fluid.channel_send(ch, input_value)
result, status = fluid.channel_recv(ch, result)
fluid.channel_close(ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
outs = exe.run(fetch_list=[result])
self.assertEqual(outs[0], 1234)
def pong(ch1, ch2):
fluid.channel_recv(ch1, ping_result)
fluid.channel_send(ch2, ping_result, is_copy=True)
def ping(ch, message):
fluid.channel_send(ch, message, is_copy=True)
def test_fibonacci(self):
"""
Mimics Fibonacci Go example: https://tour.golang.org/concurrency/5
"""
with framework.program_guard(framework.Program()):
quit_ch_input_var = self._create_persistable_tensor(
'quit_ch_input', core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT32)
quit_ch_input = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0,
out=quit_ch_input_var)
result = self._create_persistable_tensor(
'result', core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT32)
fill_constant(shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0,
out=result)
x = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0)
y = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=1)
while_cond = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.BOOL,
value=True)
while_false = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.BOOL,
value=False)
x_tmp = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0)
def fibonacci(channel, quit_channel):
while_op = While(cond=while_cond)
with while_op.block():
result2 = fill_constant(shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0)
with fluid.Select() as select:
with select.case(fluid.channel_send,
channel,
x,
is_copy=True):
assign(input=x, output=x_tmp)
assign(input=y, output=x)
assign(elementwise_add(x=x_tmp, y=y), output=y)
with select.case(fluid.channel_recv, quit_channel,
result2):
# Quit
helper = layer_helper.LayerHelper('assign')
helper.append_op(type='assign',
inputs={'X': [while_false]},
outputs={'Out': [while_cond]})
ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
for i in xrange(10):
fluid.channel_recv(ch1, result)
Print(result)
fluid.channel_send(quit_ch, quit_ch_input)
fibonacci(ch1, quit_ch)
fluid.channel_close(ch1)
fluid.channel_close(quit_ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe_result = exe.run(fetch_list=[result])
self.assertEqual(exe_result[0][0], 34)
def pong(ch1, ch2):
fluid.channel_recv(ch1, ping_result)
fluid.channel_send(ch2, ping_result, is_copy=True)
def ping(ch, message):
fluid.channel_send(ch, message, is_copy=True)
def test_fibonacci(self):
"""
Mimics Fibonacci Go example: https://tour.golang.org/concurrency/5
"""
with framework.program_guard(framework.Program()):
quit_ch_input_var = self._create_persistable_tensor(
'quit_ch_input', core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT32)
quit_ch_input = fill_constant(
shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0,
out=quit_ch_input_var)
result = self._create_persistable_tensor(
'result', core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT32)
fill_constant(
shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0,
out=result)
x = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
y = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=1)
while_cond = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.BOOL, value=True)
while_false = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.BOOL, value=False)
x_tmp = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
def fibonacci(channel, quit_channel):
while_op = While(cond=while_cond)
with while_op.block():
result2 = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
with fluid.Select() as select:
with select.case(
fluid.channel_send, channel, x, is_copy=True):
assign(input=x, output=x_tmp)
assign(input=y, output=x)
assign(elementwise_add(x=x_tmp, y=y), output=y)
with select.case(fluid.channel_recv, quit_channel,
result2):
# Quit
helper = layer_helper.LayerHelper('assign')
helper.append_op(
type='assign',
inputs={'X': [while_false]},
outputs={'Out': [while_cond]})
ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
for i in xrange(10):
fluid.channel_recv(ch1, result)
Print(result)
fluid.channel_send(quit_ch, quit_ch_input)
fibonacci(ch1, quit_ch)
fluid.channel_close(ch1)
fluid.channel_close(quit_ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe_result = exe.run(fetch_list=[result])
self.assertEqual(exe_result[0][0], 34)