# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import tempfile import paddle import paddle.profiler as profiler import paddle.profiler.utils as utils import paddle.nn as nn import paddle.nn.functional as F from paddle.io import Dataset, DataLoader class TestProfiler(unittest.TestCase): def test_profiler(self): def my_trace_back(prof): profiler.export_chrome_tracing('./test_profiler_chrometracing/')( prof) profiler.export_protobuf('./test_profiler_pb/')(prof) x_value = np.random.randn(2, 3, 3) x = paddle.to_tensor(x_value, stop_gradient=False, place=paddle.CPUPlace()) y = x / 2.0 ones_like_y = paddle.ones_like(y) with profiler.Profiler(targets=[profiler.ProfilerTarget.CPU], ) as prof: y = x / 2.0 prof = None self.assertEqual(utils._is_profiler_used, False) with profiler.RecordEvent(name='test'): y = x / 2.0 with profiler.Profiler(targets=[profiler.ProfilerTarget.CPU], scheduler=(1, 2)) as prof: self.assertEqual(utils._is_profiler_used, True) with profiler.RecordEvent(name='test'): y = x / 2.0 prof = None with profiler.Profiler(targets=[profiler.ProfilerTarget.CPU], scheduler=profiler.make_scheduler(closed=0, ready=1, record=1, repeat=1), on_trace_ready=my_trace_back) as prof: y = x / 2.0 prof = None with profiler.Profiler(targets=[profiler.ProfilerTarget.CPU], scheduler=profiler.make_scheduler(closed=0, ready=0, record=2, repeat=1), on_trace_ready=my_trace_back) as prof: for i in range(3): y = x / 2.0 prof.step() prof = None with profiler.Profiler( targets=[profiler.ProfilerTarget.CPU], scheduler=lambda x: profiler.ProfilerState.RECORD_AND_RETURN, on_trace_ready=my_trace_back) as prof: for i in range(2): y = x / 2.0 prof.step() def my_sheduler(num_step): if num_step % 5 < 2: return profiler.ProfilerState.RECORD_AND_RETURN elif num_step % 5 < 3: return profiler.ProfilerState.READY elif num_step % 5 < 4: return profiler.ProfilerState.RECORD else: return profiler.ProfilerState.CLOSED def my_sheduler1(num_step): if num_step % 5 < 2: return profiler.ProfilerState.RECORD elif num_step % 5 < 3: return profiler.ProfilerState.READY elif num_step % 5 < 4: return profiler.ProfilerState.RECORD else: return profiler.ProfilerState.CLOSED prof = None with profiler.Profiler( targets=[profiler.ProfilerTarget.CPU], scheduler=lambda x: profiler.ProfilerState.RECORD_AND_RETURN, on_trace_ready=my_trace_back) as prof: for i in range(2): y = x / 2.0 prof.step() prof = None with profiler.Profiler(targets=[profiler.ProfilerTarget.CPU], scheduler=my_sheduler, on_trace_ready=my_trace_back) as prof: for i in range(5): y = x / 2.0 prof.step() prof = None with profiler.Profiler(targets=[profiler.ProfilerTarget.CPU], scheduler=my_sheduler1) as prof: for i in range(5): y = x / 2.0 prof.step() prof = None with profiler.Profiler(targets=[profiler.ProfilerTarget.CPU], scheduler=profiler.make_scheduler(closed=1, ready=1, record=2, repeat=1, skip_first=1), on_trace_ready=my_trace_back) as prof: for i in range(5): y = x / 2.0 paddle.grad(outputs=y, inputs=[x], grad_outputs=ones_like_y) prof.step() prof.export(path='./test_profiler_pb.pb', format='pb') prof.summary() result = profiler.utils.load_profiler_result('./test_profiler_pb.pb') prof = None dataset = RandomDataset(10 * 4) simple_net = SimpleNet() opt = paddle.optimizer.SGD(learning_rate=1e-3, parameters=simple_net.parameters()) loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True, num_workers=2) prof = profiler.Profiler(on_trace_ready=lambda prof: None) prof.start() for i, (image, label) in enumerate(loader()): out = simple_net(image) loss = F.cross_entropy(out, label) avg_loss = paddle.mean(loss) avg_loss.backward() opt.minimize(avg_loss) simple_net.clear_gradients() prof.step() prof.stop() prof.summary() prof = None dataset = RandomDataset(10 * 4) simple_net = SimpleNet() loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True) opt = paddle.optimizer.Adam(learning_rate=1e-3, parameters=simple_net.parameters()) prof = profiler.Profiler(on_trace_ready=lambda prof: None) prof.start() for i, (image, label) in enumerate(loader()): out = simple_net(image) loss = F.cross_entropy(out, label) avg_loss = paddle.mean(loss) avg_loss.backward() opt.step() simple_net.clear_gradients() prof.step() prof.stop() class TestNvprof(unittest.TestCase): def test_nvprof(self): for i in range(10): paddle.fluid.profiler._nvprof_range(i, 10, 20) x_value = np.random.randn(2, 3, 3) x = paddle.to_tensor(x_value, stop_gradient=False, place=paddle.CPUPlace()) y = x / 2.0 class TestGetProfiler(unittest.TestCase): def test_getprofiler(self): config_content = ''' { "targets": ["CPU"], "scheduler": [3,4], "on_trace_ready": { "export_chrome_tracing":{ "module": "paddle.profiler", "use_direct": false, "args": [], "kwargs": { "dir_name": "testdebug/" } } }, "timer_only": false } ''' filehandle = tempfile.NamedTemporaryFile(mode='w') filehandle.write(config_content) filehandle.flush() import paddle.profiler.profiler as profiler profiler = profiler.get_profiler(filehandle.name) x_value = np.random.randn(2, 3, 3) x = paddle.to_tensor(x_value, stop_gradient=False, place=paddle.CPUPlace()) with profiler: for i in range(5): y = x / 2.0 ones_like_y = paddle.ones_like(y) profiler.step() # below tests are just for coverage, wrong config # test use_direct config_content = ''' { "targets": ["Cpu", "Gpu"], "scheduler": { "make_scheduler":{ "module": "paddle.profiler", "use_direct": true, "args": [], "kwargs": {} } }, "on_trace_ready": { "export_chrome_tracing":{ "module": "paddle.profiler1", "use_direct": true, "args": [], "kwargs": { } } }, "timer_only": false } ''' filehandle = tempfile.NamedTemporaryFile(mode='w') filehandle.write(config_content) filehandle.flush() import paddle.profiler.profiler as profiler try: profiler = profiler.get_profiler(filehandle.name) except: pass # test scheduler config_content = ''' { "targets": ["Cpu", "Gpu"], "scheduler": { "make_scheduler":{ "module": "paddle.profiler", "use_direct": false, "args": [], "kwargs": { "closed": 1, "ready": 1, "record": 2 } } }, "on_trace_ready": { "export_chrome_tracing":{ "module": "paddle.profiler", "use_direct": true, "args": [], "kwargs": { } } }, "timer_only": false } ''' filehandle = tempfile.NamedTemporaryFile(mode='w') filehandle.write(config_content) filehandle.flush() import paddle.profiler.profiler as profiler profiler = profiler.get_profiler(filehandle.name) # test exception config_content = ''' { "targets": [1], "scheduler": { "make_scheduler1":{ "module": "paddle.profiler", "use_direct": false, "args": [], "kwargs": { "closed": 1, "ready": 1, "record": 2 } } }, "on_trace_ready": { "export_chrome_tracing1":{ "module": "paddle.profiler", "use_direct": false, "args": [], "kwargs": { "dir_name": "testdebug/" } } }, "timer_only": 1 } ''' filehandle = tempfile.NamedTemporaryFile(mode='w') filehandle.write(config_content) filehandle.flush() import paddle.profiler.profiler as profiler profiler = profiler.get_profiler(filehandle.name) # test path error import paddle.profiler.profiler as profiler profiler = profiler.get_profiler('nopath.json') class RandomDataset(Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([100]).astype('float32') label = np.random.randint(0, 10 - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples class SimpleNet(nn.Layer): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(100, 10) def forward(self, image, label=None): return self.fc(image) class TestTimerOnly(unittest.TestCase): def test_with_dataloader(self): def train(step_num_samples=None): dataset = RandomDataset(20 * 4) simple_net = SimpleNet() opt = paddle.optimizer.SGD(learning_rate=1e-3, parameters=simple_net.parameters()) loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True, num_workers=2) step_info = '' p = profiler.Profiler(timer_only=True) p.start() for i, (image, label) in enumerate(loader()): out = simple_net(image) loss = F.cross_entropy(out, label) avg_loss = paddle.mean(loss) avg_loss.backward() opt.minimize(avg_loss) simple_net.clear_gradients() p.step(num_samples=step_num_samples) if i % 10 == 0: step_info = p.step_info() print("Iter {}: {}".format(i, step_info)) p.stop() return step_info step_info = train(step_num_samples=None) self.assertTrue('steps/s' in step_info) step_info = train(step_num_samples=4) self.assertTrue('samples/s' in step_info) def test_without_dataloader(self): x = paddle.to_tensor(np.random.randn(10, 10)) y = paddle.to_tensor(np.random.randn(10, 10)) p = profiler.Profiler(timer_only=True) p.start() step_info = '' for i in range(20): out = x + y p.step() p.stop() if __name__ == '__main__': unittest.main()