Distributed Automatic SParsity with Fleet (#33558)

paddle/fluid/framework/distributed_strategy.proto

 // Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+// Copyright (c) 2021 NVIDIA Corporation. 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.
@@ -189,6 +190,7 @@ message DistributedStrategy {
   optional bool without_graph_optimization = 30 [ default = false ];
   optional int32 fuse_grad_size_in_num = 31 [ default = 1 ];
   optional bool calc_comm_same_stream = 32 [ default = false ];
+  optional bool asp = 33 [ default = false ];
 
   optional RecomputeConfig recompute_configs = 101;
   optional AMPConfig amp_configs = 102;

python/paddle/distributed/fleet/base/distributed_strategy.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 NVIDIA Corporation. 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.
@@ -446,6 +447,31 @@ class DistributedStrategy(object):
         check_configs_key(self.strategy.amp_configs, configs, "amp_configs")
         assign_configs_value(self.strategy.amp_configs, configs)
 
+    @property
+    def asp(self):
+        """
+        Indicating whether we are using automatic sparsity training
+        Default Value: False
+
+        Examples:
+
+          .. code-block:: python
+
+            import paddle.distributed.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.asp = True # by default this is false
+
+        """
+        return self.strategy.asp
+
+    @asp.setter
+    @is_strict_auto
+    def asp(self, flag):
+        if isinstance(flag, bool):
+            self.strategy.asp = flag
+        else:
+            print("WARNING: asp should have value of bool type")
+
     @property
     def recompute(self):
         """

python/paddle/distributed/fleet/meta_optimizers/__init__.py

-#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 NVIDIA Corporation. 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.
@@ -12,6 +13,7 @@
 # See the License for the specific language governing permissions and
 
 from .amp_optimizer import AMPOptimizer
+from .asp_optimizer import ASPOptimizer
 from .recompute_optimizer import RecomputeOptimizer
 from .gradient_merge_optimizer import GradientMergeOptimizer
 from .graph_execution_optimizer import GraphExecutionOptimizer

python/paddle/distributed/fleet/meta_optimizers/asp_optimizer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 NVIDIA Corporation. 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
+
+from paddle.fluid.contrib.sparsity.asp import ASPHelper
+from .meta_optimizer_base import MetaOptimizerBase
+
+__all__ = []
+
+
+class ASPOptimizer(MetaOptimizerBase):
+    def __init__(self, optimizer):
+        super(ASPOptimizer, self).__init__(optimizer)
+        self.inner_opt = optimizer
+        # we do not allow meta optimizer to be inner optimizer currently
+        self.meta_optimizers_white_list = [
+            "AMPOptimizer", "LarsOptimizer", "LambOptimizer",
+            "GraphExecutionOptimizer", "RecomputeOptimizer",
+            "GradientMergeOptimizer"
+        ]
+        self.meta_optimizers_black_list = []
+
+    def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
+                        user_defined_strategy):
+        super(ASPOptimizer, self)._set_basic_info(
+            loss, role_maker, user_defined_optimizer, user_defined_strategy)
+
+    def _can_apply(self):
+        if not self.role_maker._is_collective:
+            return False
+
+        if self.user_defined_strategy.asp:
+            return True
+
+        return False
+
+    def _disable_strategy(self, dist_strategy):
+        dist_strategy.asp = False
+
+    def _enable_strategy(self, dist_strategy, context):
+        dist_strategy.asp = True
+
+    def minimize_impl(self,
+                      loss,
+                      startup_program=None,
+                      parameter_list=None,
+                      no_grad_set=None):
+
+        optimize_ops, params_grads = ASPHelper._minimize(
+            self.inner_opt,
+            loss,
+            startup_program=startup_program,
+            parameter_list=parameter_list,
+            no_grad_set=no_grad_set)
+
+        return optimize_ops, params_grads

python/paddle/fluid/contrib/sparsity/asp.py

@@ -64,12 +64,15 @@ def decorate(optimizer):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
             from paddle.fluid.contrib import sparsity
 
             main_program = fluid.Program()
             startup_program = fluid.Program()
 
+            paddle.enable_static()
+
             with fluid.program_guard(main_program, startup_program):
                 input_data = fluid.layers.data(name='data', shape=[None, 128])
                 label = fluid.layers.data(name='label', shape=[None, 10])
@@ -78,17 +81,13 @@ def decorate(optimizer):
                 loss = fluid.layers.mean(fluid.layers.square_error_cost(prob, label))
 
                 optimizer = fluid.optimizer.SGD(learning_rate=0.1)
-
                 optimizer = sparsity.decorate(optimizer)
-                optimizer.minimize(loss, startup_program)
+                # if do sparse training with Fleet, please replace above decorate with:
+                # strategy = paddle.distributed.fleet.DistributedStrategy()
+                # strategy.asp = True
+                # optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
 
-            # When apply distributed training with Fleet
-            import paddle.distributed.fleet as fleet
-
-            optimizer = fluid.optimizer.SGD(learning_rate=0.1)
-            optimizer = sparsity.decorate(optimizer) # Need to be called before `fleet.distributed_optimizer`
-            optimizer = fleet.distributed_optimizer(optimizer)
-            optimizer.minimize(loss, startup_program)
+                optimizer.minimize(loss, startup_program)
     """
     return ASPHelper.decorate(optimizer)
 
@@ -126,23 +125,38 @@ def prune_model(place,
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            import paddle.fluid.core as core
             from paddle.fluid.contrib import sparsity
 
+            paddle.enable_static()
+
             main_program = fluid.Program()
             startup_program = fluid.Program()
 
-            place = fluid.CUDAPlace(0)
+            place = paddle.CPUPlace()
+            if core.is_compiled_with_cuda():
+                place = paddle.CUDAPlace(0)
 
             with fluid.program_guard(main_program, startup_program):
                 input_data = fluid.layers.data(name='data', shape=[None, 128])
                 label = fluid.layers.data(name='label', shape=[None, 10])
-                hidden = fluid.layers.fc(input=input_data, num_flatten_dims=-1, size=32, act=None)
+                hidden = fluid.layers.fc(input=input_data, num_flatten_dims=-1, size=32, act=None, name="need_sparse")
+                hidden = fluid.layers.fc(input=hidden, num_flatten_dims=-1, size=32, act=None, name="need_dense")
                 prob = fluid.layers.fc(input=hidden, num_flatten_dims=-1, size=10, act=None)
                 loss = fluid.layers.mean(fluid.layers.square_error_cost(prob, label))
 
-                optimizer = decorate(fluid.optimizer.SGD(learning_rate=0.1))
-                optimizer.minimize(optimizer, loss, main_program, startup_program)
+                # Setup exluded layers out from ASP workflow.
+                # Please note, excluded_layers must be set before calling `optimizer.minimize()`.
+                sparsity.set_excluded_layers(main_program, ["need_dense"])
+
+                optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+                optimizer = fluid.contrib.mixed_precision.decorator.decorate(optimizer )
+                # Calling sparsity.decorate() to wrap minimize() in optimizer, which 
+                # will insert necessary masking operations for ASP workflow.
+                optimizer = sparsity.decorate(optimizer)
+                optimizer.minimize(loss, startup_program)
 
             exe = fluid.Executor(place)
             exe.run(startup_program)

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

 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
+list(REMOVE_ITEM TEST_OPS "test_fleet_with_asp"})
+list(REMOVE_ITEM TEST_OPS "test_fleet_with_asp_amp"})
+
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
+
+if(WITH_DISTRIBUTE)
+    py_test_modules(test_fleet_with_asp MODULES test_fleet_with_asp ENVS ${dist_ENVS})
+    py_test_modules(test_fleet_with_asp_amp MODULES test_fleet_with_asp_amp ENVS ${dist_ENVS})
+endif()

python/paddle/fluid/tests/unittests/asp/test_fleet_with_asp.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 NVIDIA Corporation.  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 paddle.distributed.fleet as fleet
+import paddle.distributed.fleet.base.role_maker as role_maker
+import unittest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import os
+from paddle.fluid.contrib import sparsity
+from paddle.fluid.contrib.sparsity.asp import ASPHelper
+import numpy as np
+cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
+if cuda_visible_devices is None or cuda_visible_devices == "":
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+else:
+    os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices.split(',')[0]
+
+paddle.enable_static()
+
+
+class TestFleetWithASP(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36213"
+        os.environ["PADDLE_CURRENT_ENDPOINTS"] = "127.0.0.1:36213"
+        os.environ["PADDLE_TRAINERS_NUM"] = "1"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+
+    def net(self, main_prog, startup_prog):
+        with fluid.program_guard(main_prog, startup_prog):
+            input_x = paddle.static.data(
+                name="x", shape=[-1, 32], dtype='float32')
+            input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
+
+            fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
+            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
+            cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
+            avg_cost = paddle.mean(x=cost)
+
+            strategy = paddle.distributed.fleet.DistributedStrategy()
+            strategy.asp = True
+        return avg_cost, strategy, input_x, input_y
+
+    def test_with_asp(self):
+        fleet.init(is_collective=True)
+        train_prog, startup_prog = fluid.Program(), fluid.Program()
+        avg_cost, strategy, input_x, input_y = self.net(train_prog,
+                                                        startup_prog)
+
+        with fluid.program_guard(train_prog, startup_prog):
+            optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.01)
+            optimizer = fleet.distributed_optimizer(
+                optimizer, strategy=strategy)
+            optimizer.minimize(avg_cost)
+
+        place = fluid.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+
+        exe = fluid.Executor(place)
+        feeder = fluid.DataFeeder(feed_list=[input_x, input_y], place=place)
+        exe.run(startup_prog)
+
+        sparsity.prune_model(place, train_prog)
+
+        data = (np.random.randn(64, 32), np.random.randint(2, size=(64, 1)))
+        exe.run(train_prog, feed=feeder.feed([data]))
+
+        for param in train_prog.global_block().all_parameters():
+            if ASPHelper._is_supported_layer(train_prog, param.name):
+                mat = np.array(fluid.global_scope().find_var(param.name)
+                               .get_tensor())
+                self.assertTrue(sparsity.check_sparsity(mat.T, n=2, m=4))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/asp/test_fleet_with_asp_amp.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 NVIDIA Corporation.  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 paddle.distributed.fleet as fleet
+import paddle.distributed.fleet.base.role_maker as role_maker
+import unittest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import os
+from paddle.fluid.contrib import sparsity
+from paddle.fluid.contrib.sparsity.asp import ASPHelper
+import numpy as np
+cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
+if cuda_visible_devices is None or cuda_visible_devices == "":
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+else:
+    os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices.split(',')[0]
+
+paddle.enable_static()
+
+
+class TestFleetWithASP(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36213"
+        os.environ["PADDLE_CURRENT_ENDPOINTS"] = "127.0.0.1:36213"
+        os.environ["PADDLE_TRAINERS_NUM"] = "1"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+
+    def net(self, main_prog, startup_prog):
+        with fluid.program_guard(main_prog, startup_prog):
+            input_x = paddle.static.data(
+                name="x", shape=[-1, 32], dtype='float32')
+            input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
+
+            fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
+            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
+            cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
+            avg_cost = paddle.mean(x=cost)
+
+            strategy = paddle.distributed.fleet.DistributedStrategy()
+            strategy.asp = True
+        return avg_cost, strategy, input_x, input_y
+
+    def test_with_asp_and_amp(self):
+        fleet.init(is_collective=True)
+        train_prog, startup_prog = fluid.Program(), fluid.Program()
+        avg_cost, strategy, input_x, input_y = self.net(train_prog,
+                                                        startup_prog)
+        strategy.amp = True
+
+        with fluid.program_guard(train_prog, startup_prog):
+            optimizer = paddle.optimizer.SGD(learning_rate=0.01)
+            optimizer = fleet.distributed_optimizer(
+                optimizer, strategy=strategy)
+            optimizer.minimize(avg_cost)
+
+        place = fluid.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+
+        exe = fluid.Executor(place)
+        feeder = fluid.DataFeeder(feed_list=[input_x, input_y], place=place)
+        exe.run(startup_prog)
+
+        optimizer.amp_init(place)
+
+        sparsity.prune_model(place, train_prog)
+
+        data = (np.random.randn(64, 32), np.random.randint(2, size=(64, 1)))
+        exe.run(train_prog, feed=feeder.feed([data]))
+
+        for param in train_prog.global_block().all_parameters():
+            if ASPHelper._is_supported_layer(train_prog, param.name):
+                mat = np.array(fluid.global_scope().find_var(param.name)
+                               .get_tensor())
+                self.assertTrue(sparsity.check_sparsity(mat.T, n=2, m=4))
+
+    def test_with_asp_and_pure_fp16(self):
+        fleet.init(is_collective=True)
+        train_prog, startup_prog = fluid.Program(), fluid.Program()
+        with paddle.static.amp.fp16_guard():
+            avg_cost, strategy, \
+                input_x, input_y = self.net(train_prog,
+                                            startup_prog)
+        strategy.amp = True
+        strategy.amp_configs = {'use_pure_fp16': True}
+
+        with fluid.program_guard(train_prog, startup_prog):
+            with paddle.static.amp.fp16_guard():
+                optimizer = optimizer = paddle.optimizer.Momentum(
+                    learning_rate=0.01, multi_precision=True)
+                optimizer = fleet.distributed_optimizer(
+                    optimizer, strategy=strategy)
+                optimizer.minimize(avg_cost)
+
+        place = fluid.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+
+        exe = fluid.Executor(place)
+        feeder = fluid.DataFeeder(feed_list=[input_x, input_y], place=place)
+        exe.run(startup_prog)
+
+        optimizer.amp_init(place)
+
+        sparsity.prune_model(place, train_prog)
+
+        data = (np.random.randn(64, 32), np.random.randint(2, size=(64, 1)))
+        exe.run(train_prog, feed=feeder.feed([data]))
+
+        for param in train_prog.global_block().all_parameters():
+            if ASPHelper._is_supported_layer(train_prog, param.name):
+                mat = np.array(fluid.global_scope().find_var(param.name)
+                               .get_tensor())
+                self.assertTrue(sparsity.check_sparsity(mat.T, n=2, m=4))
+
+
+if __name__ == "__main__":
+    unittest.main()