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JiabinYangJiabinYang
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Revert "Cherry pick install check for multi gpu" (#18312)
* Revert "Cherry pick install check for multi gpu (#18245)" This reverts commit d021900. * test=release/1.5, ci start
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‎python/paddle/fluid/install_check.py

Lines changed: 24 additions & 128 deletions
Original file line numberDiff line numberDiff line change
@@ -12,50 +12,15 @@
1212
# See the License for the specific language governing permissions and
1313
# limitations under the License.
1414

15-
import os
16-
from . import core
17-
18-
19-
def process_env():
20-
env = os.environ
21-
device_list = []
22-
if env.get('CUDA_VISIBLE_DEVICES') is not None:
23-
cuda_devices = env['CUDA_VISIBLE_DEVICES']
24-
if cuda_devices == "" or len(cuda_devices) == 0:
25-
os.environ['CUDA_VISIBLE_DEVICES'] = "0,1"
26-
device_list = [0, 1]
27-
elif len(cuda_devices) == 1:
28-
device_list.append(0)
29-
elif len(cuda_devices) > 1:
30-
for i in range(len(cuda_devices.split(","))):
31-
device_list.append(i)
32-
return device_list
33-
else:
34-
if core.get_cuda_device_count() > 1:
35-
os.environ['CUDA_VISIBLE_DEVICES'] = "0,1"
36-
return [0, 1]
37-
else:
38-
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
39-
return [0]
40-
41-
42-
device_list = []
43-
if core.is_compiled_with_cuda():
44-
device_list = process_env()
45-
else:
46-
device_list = [0, 1] # for CPU 0,1
47-
48-
from .framework import Program, program_guard, unique_name
15+
from .framework import Program, program_guard, unique_name, default_startup_program
4916
from .param_attr import ParamAttr
5017
from .initializer import Constant
5118
from . import layers
5219
from . import backward
5320
from .dygraph import Layer, nn
5421
from . import executor
55-
from . import optimizer
22+
5623
from . import core
57-
from . import compiler
58-
import logging
5924
import numpy as np
6025

6126
__all__ = ['run_check']
@@ -80,94 +45,25 @@ def run_check():
8045
This func should not be called only if you need to verify installation
8146
'''
8247
print("Running Verify Fluid Program ... ")
83-
use_cuda = False if not core.is_compiled_with_cuda() else True
84-
place = core.CPUPlace() if not core.is_compiled_with_cuda(
85-
) else core.CUDAPlace(0)
86-
np_inp_single = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
87-
inp = []
88-
for i in range(len(device_list)):
89-
inp.append(np_inp_single)
90-
np_inp_muti = np.array(inp)
91-
np_inp_muti = np_inp_muti.reshape(len(device_list), 2, 2)
92-
93-
def test_parallerl_exe():
94-
train_prog = Program()
95-
startup_prog = Program()
96-
scope = core.Scope()
97-
if not use_cuda:
98-
os.environ['CPU_NUM'] = "2"
99-
with executor.scope_guard(scope):
100-
with program_guard(train_prog, startup_prog):
101-
with unique_name.guard():
102-
places = []
103-
build_strategy = compiler.BuildStrategy()
104-
build_strategy.enable_inplace = True
105-
build_strategy.memory_optimize = True
106-
inp = layers.data(name="inp", shape=[2, 2])
107-
simple_layer = SimpleLayer("simple_layer")
108-
out = simple_layer(inp)
109-
exe = executor.Executor(place)
110-
if use_cuda:
111-
for i in device_list:
112-
places.append(core.CUDAPlace(i))
113-
else:
114-
places = [core.CPUPlace(), core.CPUPlace()]
115-
loss = layers.mean(out)
116-
loss.persistable = True
117-
optimizer.SGD(learning_rate=0.01).minimize(loss)
118-
startup_prog.random_seed = 1
119-
compiled_prog = compiler.CompiledProgram(
120-
train_prog).with_data_parallel(
121-
build_strategy=build_strategy,
122-
loss_name=loss.name,
123-
places=places)
124-
exe.run(startup_prog)
125-
126-
exe.run(compiled_prog,
127-
feed={inp.name: np_inp_muti},
128-
fetch_list=[loss.name])
129-
130-
def test_simple_exe():
131-
train_prog = Program()
132-
startup_prog = Program()
133-
scope = core.Scope()
134-
if not use_cuda:
135-
os.environ['CPU_NUM'] = "1"
136-
with executor.scope_guard(scope):
137-
with program_guard(train_prog, startup_prog):
138-
with unique_name.guard():
139-
inp0 = layers.data(
140-
name="inp", shape=[2, 2], append_batch_size=False)
141-
simple_layer0 = SimpleLayer("simple_layer")
142-
out0 = simple_layer0(inp0)
143-
param_grads = backward.append_backward(
144-
out0, parameter_list=[simple_layer0._fc1._w.name])[0]
145-
exe0 = executor.Executor(core.CPUPlace()
146-
if not core.is_compiled_with_cuda()
147-
else core.CUDAPlace(0))
148-
exe0.run(startup_prog)
149-
exe0.run(feed={inp0.name: np_inp_single},
150-
fetch_list=[out0.name, param_grads[1].name])
151-
152-
test_simple_exe()
153-
154-
print("Your Paddle Fluid works well on SINGLE GPU or CPU.")
155-
try:
156-
test_parallerl_exe()
157-
print("Your Paddle Fluid works well on MUTIPLE GPU or CPU.")
158-
print(
159-
"Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now"
160-
)
161-
except Exception as e:
162-
logging.warning(
163-
"Your Paddle Fluid has some problem with multiple GPU. This may be caused by:"
164-
"\n 1. There is only 1 GPU visible on your Device;"
165-
"\n 2. No.1 or No.2 GPU or both of them are occupied now"
166-
"\n 3. Wrong installation of NVIDIA-NCCL2, please follow instruction on https://github.com/NVIDIA/nccl-tests "
167-
"\n to test your NCCL, or reinstall it following https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html"
168-
)
169-
170-
print("\n Original Error is: {}".format(e))
171-
print(
172-
"Your Paddle Fluid is installed successfully ONLY for SINGLE GPU or CPU! "
173-
"\n Let's start deep Learning with Paddle Fluid now")
48+
prog = Program()
49+
startup_prog = Program()
50+
scope = core.Scope()
51+
with executor.scope_guard(scope):
52+
with program_guard(prog, startup_prog):
53+
with unique_name.guard():
54+
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
55+
inp = layers.data(
56+
name="inp", shape=[2, 2], append_batch_size=False)
57+
simple_layer = SimpleLayer("simple_layer")
58+
out = simple_layer(inp)
59+
param_grads = backward.append_backward(
60+
out, parameter_list=[simple_layer._fc1._w.name])[0]
61+
exe = executor.Executor(core.CPUPlace(
62+
) if not core.is_compiled_with_cuda() else core.CUDAPlace(0))
63+
exe.run(default_startup_program())
64+
exe.run(feed={inp.name: np_inp},
65+
fetch_list=[out.name, param_grads[1].name])
66+
67+
print(
68+
"Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now!"
69+
)

‎python/paddle/fluid/tests/unittests/CMakeLists.txt

Lines changed: 0 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -116,7 +116,6 @@ list(REMOVE_ITEM TEST_OPS test_imperative_mnist)
116116
list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
117117
list(REMOVE_ITEM TEST_OPS test_layers)
118118
list(REMOVE_ITEM TEST_OPS test_imperative_ocr_attention_model)
119-
list(REMOVE_ITEM TEST_OPS test_install_check)
120119

121120
# Some ops need to check results when gc is enabled
122121
# Currently, only ops that register NoNeedBufferVarsInference need to do this test
@@ -173,9 +172,6 @@ py_test_modules(test_imperative_mnist_sorted_gradient MODULES test_imperative_mn
173172
py_test_modules(test_imperative_se_resnext MODULES test_imperative_se_resnext ENVS
174173
FLAGS_cudnn_deterministic=1 SERIAL)
175174
set_tests_properties(test_imperative_se_resnext PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")
176-
py_test_modules(test_install_check MODULES test_install_check ENVS
177-
FLAGS_cudnn_deterministic=1 SERIAL)
178-
set_tests_properties(test_install_check PROPERTIES LABELS "RUN_TYPE=DIST")
179175

180176
if(WITH_DISTRIBUTE)
181177
py_test_modules(test_dist_train MODULES test_dist_train)

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