From f6432604701171dc9685a74bf4a33d4b6f9f3595 Mon Sep 17 00:00:00 2001 From: Jiabin Yang Date: Tue, 25 Jun 2019 15:20:35 +0800 Subject: [PATCH] Revert "Cherry pick install check for multi gpu" (#18312) * Revert "Cherry pick install check for multi gpu (#18245)" This reverts commit d0219002a8774dc59eec33147dfa4ec8dbf39a08. * test=release/1.5, ci start --- python/paddle/fluid/install_check.py | 152 +++--------------- .../fluid/tests/unittests/CMakeLists.txt | 4 - 2 files changed, 24 insertions(+), 132 deletions(-) diff --git a/python/paddle/fluid/install_check.py b/python/paddle/fluid/install_check.py index 4f2cd2f0d86..ce21d575348 100644 --- a/python/paddle/fluid/install_check.py +++ b/python/paddle/fluid/install_check.py @@ -12,50 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os -from . import core - - -def process_env(): - env = os.environ - device_list = [] - if env.get('CUDA_VISIBLE_DEVICES') is not None: - cuda_devices = env['CUDA_VISIBLE_DEVICES'] - if cuda_devices == "" or len(cuda_devices) == 0: - os.environ['CUDA_VISIBLE_DEVICES'] = "0,1" - device_list = [0, 1] - elif len(cuda_devices) == 1: - device_list.append(0) - elif len(cuda_devices) > 1: - for i in range(len(cuda_devices.split(","))): - device_list.append(i) - return device_list - else: - if core.get_cuda_device_count() > 1: - os.environ['CUDA_VISIBLE_DEVICES'] = "0,1" - return [0, 1] - else: - os.environ['CUDA_VISIBLE_DEVICES'] = "0" - return [0] - - -device_list = [] -if core.is_compiled_with_cuda(): - device_list = process_env() -else: - device_list = [0, 1] # for CPU 0,1 - -from .framework import Program, program_guard, unique_name +from .framework import Program, program_guard, unique_name, default_startup_program from .param_attr import ParamAttr from .initializer import Constant from . import layers from . import backward from .dygraph import Layer, nn from . import executor -from . import optimizer + from . import core -from . import compiler -import logging import numpy as np __all__ = ['run_check'] @@ -80,94 +45,25 @@ def run_check(): This func should not be called only if you need to verify installation ''' print("Running Verify Fluid Program ... ") - use_cuda = False if not core.is_compiled_with_cuda() else True - place = core.CPUPlace() if not core.is_compiled_with_cuda( - ) else core.CUDAPlace(0) - np_inp_single = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) - inp = [] - for i in range(len(device_list)): - inp.append(np_inp_single) - np_inp_muti = np.array(inp) - np_inp_muti = np_inp_muti.reshape(len(device_list), 2, 2) - - def test_parallerl_exe(): - train_prog = Program() - startup_prog = Program() - scope = core.Scope() - if not use_cuda: - os.environ['CPU_NUM'] = "2" - with executor.scope_guard(scope): - with program_guard(train_prog, startup_prog): - with unique_name.guard(): - places = [] - build_strategy = compiler.BuildStrategy() - build_strategy.enable_inplace = True - build_strategy.memory_optimize = True - inp = layers.data(name="inp", shape=[2, 2]) - simple_layer = SimpleLayer("simple_layer") - out = simple_layer(inp) - exe = executor.Executor(place) - if use_cuda: - for i in device_list: - places.append(core.CUDAPlace(i)) - else: - places = [core.CPUPlace(), core.CPUPlace()] - loss = layers.mean(out) - loss.persistable = True - optimizer.SGD(learning_rate=0.01).minimize(loss) - startup_prog.random_seed = 1 - compiled_prog = compiler.CompiledProgram( - train_prog).with_data_parallel( - build_strategy=build_strategy, - loss_name=loss.name, - places=places) - exe.run(startup_prog) - - exe.run(compiled_prog, - feed={inp.name: np_inp_muti}, - fetch_list=[loss.name]) - - def test_simple_exe(): - train_prog = Program() - startup_prog = Program() - scope = core.Scope() - if not use_cuda: - os.environ['CPU_NUM'] = "1" - with executor.scope_guard(scope): - with program_guard(train_prog, startup_prog): - with unique_name.guard(): - inp0 = layers.data( - name="inp", shape=[2, 2], append_batch_size=False) - simple_layer0 = SimpleLayer("simple_layer") - out0 = simple_layer0(inp0) - param_grads = backward.append_backward( - out0, parameter_list=[simple_layer0._fc1._w.name])[0] - exe0 = executor.Executor(core.CPUPlace() - if not core.is_compiled_with_cuda() - else core.CUDAPlace(0)) - exe0.run(startup_prog) - exe0.run(feed={inp0.name: np_inp_single}, - fetch_list=[out0.name, param_grads[1].name]) - - test_simple_exe() - - print("Your Paddle Fluid works well on SINGLE GPU or CPU.") - try: - test_parallerl_exe() - print("Your Paddle Fluid works well on MUTIPLE GPU or CPU.") - print( - "Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now" - ) - except Exception as e: - logging.warning( - "Your Paddle Fluid has some problem with multiple GPU. This may be caused by:" - "\n 1. There is only 1 GPU visible on your Device;" - "\n 2. No.1 or No.2 GPU or both of them are occupied now" - "\n 3. Wrong installation of NVIDIA-NCCL2, please follow instruction on https://github.com/NVIDIA/nccl-tests " - "\n to test your NCCL, or reinstall it following https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html" - ) - - print("\n Original Error is: {}".format(e)) - print( - "Your Paddle Fluid is installed successfully ONLY for SINGLE GPU or CPU! " - "\n Let's start deep Learning with Paddle Fluid now") + prog = Program() + startup_prog = Program() + scope = core.Scope() + with executor.scope_guard(scope): + with program_guard(prog, startup_prog): + with unique_name.guard(): + np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) + inp = layers.data( + name="inp", shape=[2, 2], append_batch_size=False) + simple_layer = SimpleLayer("simple_layer") + out = simple_layer(inp) + param_grads = backward.append_backward( + out, parameter_list=[simple_layer._fc1._w.name])[0] + exe = executor.Executor(core.CPUPlace( + ) if not core.is_compiled_with_cuda() else core.CUDAPlace(0)) + exe.run(default_startup_program()) + exe.run(feed={inp.name: np_inp}, + fetch_list=[out.name, param_grads[1].name]) + + print( + "Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now!" + ) diff --git a/python/paddle/fluid/tests/unittests/CMakeLists.txt b/python/paddle/fluid/tests/unittests/CMakeLists.txt index ebcad7d5e51..15569b339df 100644 --- a/python/paddle/fluid/tests/unittests/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/CMakeLists.txt @@ -116,7 +116,6 @@ list(REMOVE_ITEM TEST_OPS test_imperative_mnist) list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer) list(REMOVE_ITEM TEST_OPS test_layers) list(REMOVE_ITEM TEST_OPS test_imperative_ocr_attention_model) -list(REMOVE_ITEM TEST_OPS test_install_check) # Some ops need to check results when gc is enabled # 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 py_test_modules(test_imperative_se_resnext MODULES test_imperative_se_resnext ENVS FLAGS_cudnn_deterministic=1 SERIAL) set_tests_properties(test_imperative_se_resnext PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE") -py_test_modules(test_install_check MODULES test_install_check ENVS - FLAGS_cudnn_deterministic=1 SERIAL) -set_tests_properties(test_install_check PROPERTIES LABELS "RUN_TYPE=DIST") if(WITH_DISTRIBUTE) py_test_modules(test_dist_train MODULES test_dist_train) -- GitLab