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[2.0 API] device guard (#30307)

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* add 2.0 API: device_guard
revert-31068-fix_conv3d_windows
Zhang Ting 5 years ago committed by GitHub
parent 7a0a576e51
commit 66c514ce83
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 78 additions and 102 deletions
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27
python/paddle/fluid/framework.py
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@ -5740,27 +5740,28 @@ def device_guard(device=None):
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
support_gpu = fluid.is_compiled_with_cuda()
place = fluid.CPUPlace()
paddle.enable_static()
support_gpu = paddle.is_compiled_with_cuda()
place = paddle.CPUPlace()
if support_gpu:
place = fluid.CUDAPlace(0)
place = paddle.CUDAPlace(0)
# if GPU is supported, the three OPs below will be automatically assigned to CUDAPlace(0)
data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32')
data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32')
shape = fluid.layers.shape(data2)
data1 = paddle.full(shape=[1, 3, 8, 8], fill_value=0.5, dtype='float32')
data2 = paddle.full(shape=[1, 3, 64], fill_value=0.5, dtype='float32')
shape = paddle.shape(data2)
with fluid.device_guard("cpu"):
with paddle.static.device_guard("cpu"):
# Ops created here will be placed on CPUPlace
shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4])
with fluid.device_guard('gpu'):
shape = paddle.slice(shape, axes=[0], starts=[0], ends=[4])
with paddle.static.device_guard('gpu'):
# if GPU is supported, OPs created here will be placed on CUDAPlace(0), otherwise on CPUPlace
out = fluid.layers.crop_tensor(data1, shape=shape)
out = paddle.reshape(data1, shape=shape)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
result = exe.run(fetch_list=[out])
"""

152
python/paddle/fluid/tests/unittests/test_device_guard.py
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@ -18,17 +18,18 @@ import unittest
from op_test import OpTest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import warnings
def execute(main_program, startup_program):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if paddle.is_compiled_with_cuda():
place = paddle.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = fluid.Executor(place)
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup_program)
exe.run(main_program)
@ -43,18 +44,17 @@ def get_vaild_warning_num(warning, w):
class TestDeviceGuard(unittest.TestCase):
def test_device_guard(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
data1 = fluid.layers.fill_constant(
shape=[1, 3, 8, 8], value=0.5, dtype='float32')
data2 = fluid.layers.fill_constant(
shape=[1, 3, 5, 5], value=0.5, dtype='float32')
shape = fluid.layers.shape(data2)
with fluid.device_guard("cpu"):
shape = fluid.layers.slice(
shape, axes=[0], starts=[0], ends=[4])
with fluid.device_guard("gpu"):
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
data1 = paddle.full(
shape=[1, 3, 8, 8], fill_value=0.5, dtype='float32')
data2 = paddle.full(
shape=[1, 3, 5, 5], fill_value=0.5, dtype='float32')
shape = paddle.shape(data2)
with paddle.static.device_guard("cpu"):
shape = paddle.slice(shape, axes=[0], starts=[0], ends=[4])
with paddle.static.device_guard("gpu"):
out = fluid.layers.crop_tensor(data1, shape=shape)
# check if the device attr is set correctly
all_ops = main_program.global_block().ops
@ -68,18 +68,17 @@ class TestDeviceGuard(unittest.TestCase):
execute(main_program, startup_program)
def test_device_guard_with_id(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
data1 = fluid.layers.fill_constant(
shape=[1, 3, 8, 8], value=0.5, dtype='float32')
data2 = fluid.layers.fill_constant(
shape=[1, 3, 5, 5], value=0.5, dtype='float32')
shape = fluid.layers.shape(data2)
with fluid.device_guard("cpu"):
shape = fluid.layers.slice(
shape, axes=[0], starts=[0], ends=[4])
with fluid.device_guard("gpu:1"):
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
data1 = paddle.full(
shape=[1, 3, 8, 8], fill_value=0.5, dtype='float32')
data2 = paddle.full(
shape=[1, 3, 5, 5], fill_value=0.5, dtype='float32')
shape = paddle.shape(data2)
with paddle.static.device_guard("cpu"):
shape = paddle.slice(shape, axes=[0], starts=[0], ends=[4])
with paddle.static.device_guard("gpu:1"):
out = fluid.layers.crop_tensor(data1, shape=shape)
# check if the device attr is set correctly
all_ops = main_program.global_block().ops
@ -93,23 +92,22 @@ class TestDeviceGuard(unittest.TestCase):
execute(main_program, startup_program)
def test_cpu_only_op(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
x = fluid.layers.fill_constant(
shape=[2, 255, 13, 13], value=0.3, dtype='float32')
gt_box = fluid.layers.fill_constant(
shape=[2, 6, 4], value=0.5, dtype='float32')
gt_label = fluid.layers.fill_constant(
shape=[2, 6], value=1.0, dtype='int32')
gt_score = fluid.layers.fill_constant(
shape=[2, 6], value=0.5, dtype='float32')
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
x = paddle.full(
shape=[2, 255, 13, 13], fill_value=0.3, dtype='float32')
gt_box = paddle.full(
shape=[2, 6, 4], fill_value=0.5, dtype='float32')
gt_label = paddle.full(shape=[2, 6], fill_value=1.0, dtype='int32')
gt_score = paddle.full(
shape=[2, 6], fill_value=0.5, dtype='float32')
anchors = [
10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156,
198, 373, 326
]
anchor_mask = [0, 1, 2]
with fluid.device_guard("gpu"):
with paddle.static.device_guard("gpu"):
# yolov3_loss only has cpu kernel, so its cpu kernel will be executed
loss = fluid.layers.yolov3_loss(
x=x,
@ -125,20 +123,19 @@ class TestDeviceGuard(unittest.TestCase):
execute(main_program, startup_program)
def test_without_kernel_op(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
loop_len = fluid.layers.fill_constant(
shape=[1], dtype='int64', value=10)
cond = fluid.layers.less_than(x=i, y=loop_len)
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
i = paddle.full(shape=[1], dtype='int64', fill_value=0)
loop_len = paddle.full(shape=[1], dtype='int64', fill_value=10)
cond = paddle.less_than(x=i, y=loop_len)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with fluid.device_guard("cpu"):
with paddle.static.device_guard("cpu"):
while_op = fluid.layers.While(cond=cond)
with while_op.block():
i = fluid.layers.increment(x=i, value=1, in_place=True)
i = paddle.increment(x=i, value=1)
fluid.layers.less_than(x=i, y=loop_len, cond=cond)
warning = "The Op(while) is not support to set device."
@ -155,55 +152,32 @@ class TestDeviceGuard(unittest.TestCase):
def test_error(self):
def device_attr():
with fluid.device_guard("cpu1"):
out = fluid.layers.fill_constant(
shape=[1], value=0.2, dtype='float32')
with paddle.static.device_guard("cpu1"):
out = paddle.full(shape=[1], fill_value=0.2, dtype='float32')
def device_attr2():
with fluid.device_guard("cpu:1"):
out = fluid.layers.fill_constant(
shape=[1], value=0.2, dtype='float32')
with paddle.static.device_guard("cpu:1"):
out = paddle.full(shape=[1], fill_value=0.2, dtype='float32')
self.assertRaises(ValueError, device_attr)
self.assertRaises(ValueError, device_attr2)
def test_warning(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with fluid.device_guard("gpu"):
x = fluid.layers.fill_constant(
shape=[1], value=3.0, dtype='float32', force_cpu=True)
y = fluid.layers.fill_constant(
shape=[1], value=4.0, dtype='float32')
result = fluid.layers.less_than(x=x, y=y, force_cpu=False)
warning = "\'device_guard\' has higher priority when they are used at the same time."
warning_num = get_vaild_warning_num(warning, w)
assert warning_num == 2
all_ops = main_program.global_block().ops
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
for op in all_ops:
self.assertEqual(op.desc.attr(device_attr_name), "gpu")
# check if op_descs have op_device attr
def test_op_descs_device_attr(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
data1 = fluid.layers.data(name="data_1", shape=[2], dtype="float32")
data2 = fluid.layers.data(name="data_2", shape=[2], dtype="float32")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
fc1 = fluid.layers.fc(input=data1, size=10)
fc2 = fluid.layers.fc(input=fc1, size=10)
with fluid.device_guard("gpu"):
out = fluid.layers.softmax_with_cross_entropy(
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
data1 = paddle.static.data(
name="data_1", shape=[4, 2], dtype="float32")
label = paddle.static.data(
name="label", shape=[4, 1], dtype="int64")
fc1 = paddle.static.nn.fc(x=data1, size=10)
fc2 = paddle.static.nn.fc(x=fc1, size=10)
with paddle.static.device_guard("gpu"):
out = paddle.nn.functional.softmax_with_cross_entropy(
logits=fc1 + fc2, label=label)
loss = fluid.layers.mean(out)
opt = fluid.optimizer.SGDOptimizer(0.1)
loss = paddle.mean(out)
opt = paddle.optimizer.SGD(0.1)
opt.minimize(loss)
all_ops = main_program.global_block().ops

1
python/paddle/static/__init__.py
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@ -72,6 +72,7 @@ from ..fluid.compiler import CompiledProgram #DEFINE_ALIAS
from ..fluid.compiler import ExecutionStrategy #DEFINE_ALIAS
from ..fluid.framework import default_main_program #DEFINE_ALIAS
from ..fluid.framework import default_startup_program #DEFINE_ALIAS
from ..fluid.framework import device_guard #DEFINE_ALIAS
from ..fluid.framework import Program #DEFINE_ALIAS
from ..fluid.framework import name_scope #DEFINE_ALIAS
from ..fluid.framework import program_guard #DEFINE_ALIAS

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