[CustomDevice] add to_static, amp ut (#46536)

* [CustomDevice] add to_static, amp ut

* update

* fix failed ut

* update

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

 if(WITH_CUSTOM_DEVICE AND NOT WITH_GPU)
   set(PLUGIN_URL https://github.com/PaddlePaddle/PaddleCustomDevice.git)
-  set(PLUGIN_TAG b9ae8452f31525d0524810461b17856838acd821)
+  set(PLUGIN_TAG 0698428ddba21e6baecb690579f37c48896f7d56)
 
   file(
     GLOB TEST_OPS
@@ -8,10 +8,10 @@ if(WITH_CUSTOM_DEVICE AND NOT WITH_GPU)
     "test_*.py")
   string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-  list(REMOVE_ITEM TEST_OPS test_collective_process_group_xccl)
   foreach(TEST_OP ${TEST_OPS})
-    py_test(${TEST_OP} SRCS ${TEST_OP}.py ENVS PLUGIN_URL=${PLUGIN_URL}
-                            PLUGIN_TAG=${PLUGIN_TAG})
+    py_test(${TEST_OP}
+            SRCS ${TEST_OP}.py ENVS FLAGS_allocator_strategy=naive_best_fit
+                 PLUGIN_URL=${PLUGIN_URL} PLUGIN_TAG=${PLUGIN_TAG})
   endforeach()
 
   bash_test_modules(
@@ -19,6 +19,7 @@ if(WITH_CUSTOM_DEVICE AND NOT WITH_GPU)
     START_BASH
     test_fleet_launch_custom_device.sh
     ENVS
+    FLAGS_allocator_strategy=naive_best_fit
     PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR}
     PLUGIN_URL=${PLUGIN_URL}
     PLUGIN_TAG=${PLUGIN_TAG})
@@ -26,4 +27,5 @@ if(WITH_CUSTOM_DEVICE AND NOT WITH_GPU)
   set_tests_properties(test_custom_cpu_plugin PROPERTIES TIMEOUT 120)
   set_tests_properties(test_custom_cpu_profiler_plugin PROPERTIES TIMEOUT 120)
   set_tests_properties(test_fleet_launch_custom_device PROPERTIES TIMEOUT 120)
+  set_tests_properties(test_custom_cpu_to_static PROPERTIES TIMEOUT 120)
 endif()

python/paddle/fluid/tests/custom_runtime/test_collective_process_group_xccl.py

@@ -18,6 +18,7 @@ import sys
 import copy
 import subprocess
 import time
+import tempfile
 
 
 def start_local_trainers(cluster,
@@ -134,18 +135,29 @@ class TestProcessGroup(TestMultipleCustomCPU):
     def setUp(self):
         # compile so and set to current path
         cur_dir = os.path.dirname(os.path.abspath(__file__))
-        cmd = 'rm -rf PaddleCustomDevice \
+        self.temp_dir = tempfile.TemporaryDirectory()
+        cmd = 'cd {} \
             && git clone {} \
-            && cd PaddleCustomDevice/backends/custom_cpu \
+            && cd PaddleCustomDevice \
+            && git fetch origin \
             && git checkout {} -b dev \
+            && cd backends/custom_cpu \
             && mkdir build && cd build && cmake .. && make -j8'.format(
-            os.getenv('PLUGIN_URL'), os.getenv('PLUGIN_TAG'))
+            self.temp_dir.name, os.getenv('PLUGIN_URL'),
+            os.getenv('PLUGIN_TAG'))
         os.system(cmd)
 
         # set environment for loading and registering compiled custom kernels
         # only valid in current process
         os.environ['CUSTOM_DEVICE_ROOT'] = os.path.join(
-            cur_dir, 'PaddleCustomDevice/backends/custom_cpu/build')
+            cur_dir, '{}/PaddleCustomDevice/backends/custom_cpu/build'.format(
+                self.temp_dir.name))
+        os.environ['FLAGS_selected_custom_cpus'] = '0,1'
+        os.environ['CUSTOM_CPU_VISIBLE_DEVICES'] = '0,1'
+        os.environ['PADDLE_XCCL_BACKEND'] = 'custom_cpu'
+
+    def tearDown(self):
+        self.temp_dir.cleanup()
 
     def test_process_group_xccl(self):
         from paddle.distributed.utils.launch_utils import find_free_ports, watch_local_trainers, get_cluster, TrainerProc

python/paddle/fluid/tests/custom_runtime/test_custom_cpu_plugin.py

@@ -17,6 +17,7 @@ import sys
 import site
 import unittest
 import numpy as np
+import tempfile
 
 
 class TestCustomCPUPlugin(unittest.TestCase):
@@ -24,18 +25,27 @@ class TestCustomCPUPlugin(unittest.TestCase):
     def setUp(self):
         # compile so and set to current path
         cur_dir = os.path.dirname(os.path.abspath(__file__))
-        cmd = 'rm -rf PaddleCustomDevice \
+        self.temp_dir = tempfile.TemporaryDirectory()
+        cmd = 'cd {} \
             && git clone {} \
-            && cd PaddleCustomDevice/backends/custom_cpu \
+            && cd PaddleCustomDevice \
+            && git fetch origin \
             && git checkout {} -b dev \
+            && cd backends/custom_cpu \
             && mkdir build && cd build && cmake .. && make -j8'.format(
-            os.getenv('PLUGIN_URL'), os.getenv('PLUGIN_TAG'))
+            self.temp_dir.name, os.getenv('PLUGIN_URL'),
+            os.getenv('PLUGIN_TAG'))
         os.system(cmd)
 
         # set environment for loading and registering compiled custom kernels
         # only valid in current process
         os.environ['CUSTOM_DEVICE_ROOT'] = os.path.join(
-            cur_dir, 'PaddleCustomDevice/backends/custom_cpu/build')
+            cur_dir, '{}/PaddleCustomDevice/backends/custom_cpu/build'.format(
+                self.temp_dir.name))
+
+    def tearDown(self):
+        self.temp_dir.cleanup()
+        del os.environ['CUSTOM_DEVICE_ROOT']
 
     def test_custom_device(self):
         import paddle
@@ -183,9 +193,6 @@ class TestCustomCPUPlugin(unittest.TestCase):
         k_t = paddle.to_tensor([3], dtype="int32")
         value_1, indices_1 = paddle.topk(data_1, k=k_t)
 
-    def tearDown(self):
-        del os.environ['CUSTOM_DEVICE_ROOT']
-
 
 if __name__ == '__main__':
     if os.name == 'nt' or sys.platform.startswith('darwin'):

python/paddle/fluid/tests/custom_runtime/test_custom_cpu_profiler_plugin.py

@@ -17,6 +17,7 @@ import sys
 import site
 import unittest
 import numpy as np
+import tempfile
 
 
 class TestCustomCPUProfilerPlugin(unittest.TestCase):
@@ -24,18 +25,27 @@ class TestCustomCPUProfilerPlugin(unittest.TestCase):
     def setUp(self):
         # compile so and set to current path
         cur_dir = os.path.dirname(os.path.abspath(__file__))
-        cmd = 'rm -rf PaddleCustomDevice \
+        self.temp_dir = tempfile.TemporaryDirectory()
+        cmd = 'cd {} \
             && git clone {} \
-            && cd PaddleCustomDevice/backends/custom_cpu \
+            && cd PaddleCustomDevice \
+            && git fetch origin \
             && git checkout {} -b dev \
+            && cd backends/custom_cpu \
             && mkdir build && cd build && cmake .. && make -j8'.format(
-            os.getenv('PLUGIN_URL'), os.getenv('PLUGIN_TAG'))
+            self.temp_dir.name, os.getenv('PLUGIN_URL'),
+            os.getenv('PLUGIN_TAG'))
         os.system(cmd)
 
         # set environment for loading and registering compiled custom kernels
         # only valid in current process
         os.environ['CUSTOM_DEVICE_ROOT'] = os.path.join(
-            cur_dir, 'PaddleCustomDevice/backends/custom_cpu/build')
+            cur_dir, '{}/PaddleCustomDevice/backends/custom_cpu/build'.format(
+                self.temp_dir.name))
+
+    def tearDown(self):
+        self.temp_dir.cleanup()
+        del os.environ['CUSTOM_DEVICE_ROOT']
 
     def test_custom_device(self):
         import paddle
@@ -59,9 +69,6 @@ class TestCustomCPUProfilerPlugin(unittest.TestCase):
         p.stop()
         p.summary()
 
-    def tearDown(self):
-        del os.environ['CUSTOM_DEVICE_ROOT']
-
 
 if __name__ == '__main__':
     if os.name == 'nt' or sys.platform.startswith('darwin'):

python/paddle/fluid/tests/custom_runtime/test_custom_cpu_to_static.py

+#   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.
+
+import os
+import sys
+import time
+import unittest
+import numpy as np
+import tempfile
+
+EPOCH_NUM = 1
+BATCH_SIZE = 1024
+
+
+def train_func_base(epoch_id, train_loader, model, cost, optimizer):
+    import paddle
+
+    total_step = len(train_loader)
+    epoch_start = time.time()
+    for batch_id, (images, labels) in enumerate(train_loader()):
+        # forward
+        outputs = model(images)
+        loss = cost(outputs, labels)
+        # backward and optimize
+        loss.backward()
+        optimizer.step()
+        optimizer.clear_grad()
+        print("Epoch [{}/{}], Step [{}/{}], Loss: {}".format(
+            epoch_id + 1, EPOCH_NUM, batch_id + 1, total_step, loss.numpy()))
+    epoch_end = time.time()
+    print(
+        f"Epoch ID: {epoch_id+1}, FP32 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
+    )
+
+
+def train_func_ampo1(epoch_id, train_loader, model, cost, optimizer, scaler):
+    import paddle
+
+    total_step = len(train_loader)
+    epoch_start = time.time()
+    for batch_id, (images, labels) in enumerate(train_loader()):
+        # forward
+        with paddle.amp.auto_cast(
+                custom_black_list={"flatten_contiguous_range", "greater_than"},
+                level='O1'):
+            outputs = model(images)
+            loss = cost(outputs, labels)
+        # backward and optimize
+        scaled = scaler.scale(loss)
+        scaled.backward()
+        scaler.minimize(optimizer, scaled)
+        optimizer.clear_grad()
+        print("Epoch [{}/{}], Step [{}/{}], Loss: {}".format(
+            epoch_id + 1, EPOCH_NUM, batch_id + 1, total_step, loss.numpy()))
+    epoch_end = time.time()
+    print(
+        f"Epoch ID: {epoch_id+1}, AMPO1 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
+    )
+
+
+def test_func(epoch_id, test_loader, model, cost):
+    import paddle
+
+    # evaluation every epoch finish
+    model.eval()
+    avg_acc = [[], []]
+    for batch_id, (images, labels) in enumerate(test_loader()):
+        # forward
+        outputs = model(images)
+        loss = cost(outputs, labels)
+        # accuracy
+        acc_top1 = paddle.metric.accuracy(input=outputs, label=labels, k=1)
+        acc_top5 = paddle.metric.accuracy(input=outputs, label=labels, k=5)
+        avg_acc[0].append(acc_top1.numpy())
+        avg_acc[1].append(acc_top5.numpy())
+    model.train()
+    print(
+        f"Epoch ID: {epoch_id+1}, Top1 accurary: {np.array(avg_acc[0]).mean()}, Top5 accurary: {np.array(avg_acc[1]).mean()}"
+    )
+
+
+class TestCustomCPUPlugin(unittest.TestCase):
+
+    def setUp(self):
+        # compile so and set to current path
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        self.temp_dir = tempfile.TemporaryDirectory()
+        cmd = 'cd {} \
+            && git clone {} \
+            && cd PaddleCustomDevice \
+            && git fetch origin \
+            && git checkout {} -b dev \
+            && cd backends/custom_cpu \
+            && mkdir build && cd build && cmake .. && make -j8'.format(
+            self.temp_dir.name, os.getenv('PLUGIN_URL'),
+            os.getenv('PLUGIN_TAG'))
+        os.system(cmd)
+
+        # set environment for loading and registering compiled custom kernels
+        # only valid in current process
+        os.environ['CUSTOM_DEVICE_ROOT'] = os.path.join(
+            cur_dir, '{}/PaddleCustomDevice/backends/custom_cpu/build'.format(
+                self.temp_dir.name))
+
+    def tearDown(self):
+        self.temp_dir.cleanup()
+
+    def test_custom_cpu_plugin(self):
+        self._test_to_static()
+        self._test_amp_o1()
+
+    def _test_to_static(self):
+        import paddle
+
+        class LeNet5(paddle.nn.Layer):
+
+            def __init__(self):
+                super(LeNet5, self).__init__()
+                self.fc = paddle.nn.Linear(in_features=1024, out_features=10)
+                self.relu = paddle.nn.ReLU()
+                self.fc1 = paddle.nn.Linear(in_features=10, out_features=10)
+
+            def forward(self, x):
+                out = paddle.flatten(x, 1)
+                out = self.fc(out)
+                out = self.relu(out)
+                out = self.fc1(out)
+                return out
+
+        # set device
+        paddle.set_device('custom_cpu')
+
+        # model
+        model = LeNet5()
+
+        # cost and optimizer
+        cost = paddle.nn.CrossEntropyLoss()
+        optimizer = paddle.optimizer.Adam(learning_rate=0.001,
+                                          parameters=model.parameters())
+
+        # convert to static model
+        build_strategy = paddle.static.BuildStrategy()
+        mnist = paddle.jit.to_static(model, build_strategy=build_strategy)
+
+        # data loader
+        transform = paddle.vision.transforms.Compose([
+            paddle.vision.transforms.Resize((32, 32)),
+            paddle.vision.transforms.ToTensor(),
+            paddle.vision.transforms.Normalize(mean=(0.1307, ), std=(0.3081, ))
+        ])
+        train_dataset = paddle.vision.datasets.MNIST(mode='train',
+                                                     transform=transform,
+                                                     download=True)
+        test_dataset = paddle.vision.datasets.MNIST(mode='test',
+                                                    transform=transform,
+                                                    download=True)
+        train_loader = paddle.io.DataLoader(train_dataset,
+                                            batch_size=BATCH_SIZE,
+                                            shuffle=True,
+                                            drop_last=True,
+                                            num_workers=2)
+        test_loader = paddle.io.DataLoader(test_dataset,
+                                           batch_size=BATCH_SIZE,
+                                           shuffle=True,
+                                           drop_last=True,
+                                           num_workers=2)
+
+        # train and eval
+        for epoch_id in range(EPOCH_NUM):
+            train_func_base(epoch_id, train_loader, model, cost, optimizer)
+            test_func(epoch_id, test_loader, model, cost)
+
+    def _test_amp_o1(self):
+        import paddle
+
+        class LeNet5(paddle.nn.Layer):
+
+            def __init__(self):
+                super(LeNet5, self).__init__()
+                self.fc = paddle.nn.Linear(in_features=1024, out_features=10)
+                self.relu = paddle.nn.ReLU()
+                self.fc1 = paddle.nn.Linear(in_features=10, out_features=10)
+
+            def forward(self, x):
+                out = paddle.flatten(x, 1)
+                out = self.fc(out)
+                out = self.relu(out)
+                out = self.fc1(out)
+                return out
+
+        # set device
+        paddle.set_device('custom_cpu')
+
+        # model
+        model = LeNet5()
+
+        # cost and optimizer
+        cost = paddle.nn.CrossEntropyLoss()
+        optimizer = paddle.optimizer.Adam(learning_rate=0.001,
+                                          parameters=model.parameters())
+
+        # convert to static model
+        scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
+        model, optimizer = paddle.amp.decorate(models=model,
+                                               optimizers=optimizer,
+                                               level='O1')
+
+        # data loader
+        transform = paddle.vision.transforms.Compose([
+            paddle.vision.transforms.Resize((32, 32)),
+            paddle.vision.transforms.ToTensor(),
+            paddle.vision.transforms.Normalize(mean=(0.1307, ), std=(0.3081, ))
+        ])
+        train_dataset = paddle.vision.datasets.MNIST(mode='train',
+                                                     transform=transform,
+                                                     download=True)
+        test_dataset = paddle.vision.datasets.MNIST(mode='test',
+                                                    transform=transform,
+                                                    download=True)
+        train_loader = paddle.io.DataLoader(train_dataset,
+                                            batch_size=BATCH_SIZE,
+                                            shuffle=True,
+                                            drop_last=True,
+                                            num_workers=2)
+        test_loader = paddle.io.DataLoader(test_dataset,
+                                           batch_size=BATCH_SIZE,
+                                           shuffle=True,
+                                           drop_last=True,
+                                           num_workers=2)
+
+        # train and eval
+        for epoch_id in range(EPOCH_NUM):
+            train_func_ampo1(epoch_id, train_loader, model, cost, optimizer,
+                             scaler)
+            test_func(epoch_id, test_loader, model, cost)
+
+
+if __name__ == '__main__':
+    if os.name == 'nt' or sys.platform.startswith('darwin'):
+        # only support Linux now
+        exit()
+    unittest.main()

python/paddle/fluid/tests/custom_runtime/test_fleet_launch_custom_device.sh

@@ -16,17 +16,20 @@
 
 set -e
 
-rm -rf PaddleCustomDevice && \
-git clone ${PLUGIN_URL} \
-&& pushd PaddleCustomDevice/backends/custom_cpu \
+temp_dir=$(mktemp --directory)
+pushd ${temp_dir} \
+&& git clone ${PLUGIN_URL} \
+&& pushd PaddleCustomDevice/ \
+&& git fetch origin \
 && git checkout ${PLUGIN_TAG} -b dev \ 
-&& mkdir build && pushd build && cmake .. && make -j8 && popd && popd
+&& pushd backends/custom_cpu \
+&& mkdir build && pushd build && cmake .. && make -j8 && popd && popd && popd && popd
 
 echo "begin test use custom_cpu"
 
 export FLAGS_selected_custom_cpus=0,1
 export CUSTOM_CPU_VISIBLE_DEVICES=0,1
-export CUSTOM_DEVICE_ROOT=PaddleCustomDevice/backends/custom_cpu/build
+export CUSTOM_DEVICE_ROOT=${temp_dir}/PaddleCustomDevice/backends/custom_cpu/build
 
 distributed_args="--devices=0,1"
 python -m paddle.distributed.fleet.launch ${distributed_args} custom_device_multi_process_collective.py fleetlaunch_custom_cpu