Dropout with seed (#21590)

* add seed op

paddle/fluid/operators/dropout_op.cc

@@ -35,12 +35,23 @@ class DropoutOp : public framework::OperatorWithKernel {
     }
     ctx->ShareLoD("X", /*->*/ "Out");
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
+  }
 };
 
 class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X", "The input of dropout op.");
+    AddInput("Seed",
+             "The seed of dropout op, it has higher priority than the attr "
+             "fix_seed and seed")
+        .AsDispensable();
     AddOutput("Out", "The output of dropout op.");
     AddOutput("Mask", "The random sampled dropout mask.").AsIntermediate();
 

paddle/fluid/operators/dropout_op.cu

@@ -67,6 +67,8 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* x = context.Input<Tensor>("X");
+    auto* seed =
+        context.HasInput("Seed") ? context.Input<Tensor>("Seed") : nullptr;
     auto* y = context.Output<Tensor>("Out");
     y->mutable_data<T>(context.GetPlace());
     float dropout_prob = context.Attr<float>("dropout_prob");
@@ -84,6 +86,20 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
       auto* mask_data = mask->mutable_data<uint8_t>(context.GetPlace());
       size_t size = framework::product(mask->dims());
       auto* x_data = x->data<T>();
+      int seed_data;
+      std::random_device rnd;
+      if (seed) {
+        if (platform::is_gpu_place(seed->place())) {
+          framework::Tensor temp;
+          TensorCopySync(*seed, platform::CPUPlace(), &temp);
+          seed_data = *(temp.data<int>());
+        } else {
+          seed_data = *(seed->data<int>());
+        }
+      } else {
+        seed_data =
+            context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
+      }
       auto* y_data = y->mutable_data<T>(context.GetPlace());
       if (dropout_prob == 1.0f) {
         PADDLE_ENFORCE_CUDA_SUCCESS(
@@ -93,14 +109,10 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
         return;
       }
 
-      std::random_device rnd;
-      int seed =
-          context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
-
       int threads = 512;
       int grid = (x_numel + threads - 1) / threads;
       RandomGenerator<T, uint8_t><<<grid, threads, 0, stream>>>(
-          size, seed, dropout_prob, x_data, mask_data, y_data,
+          size, seed_data, dropout_prob, x_data, mask_data, y_data,
           upscale_in_train);
     } else {
       auto X = EigenMatrix<T>::Reshape(*x, 1);

paddle/fluid/operators/dropout_op.h

@@ -33,6 +33,8 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* x = context.Input<Tensor>("X");
+    auto* seed =
+        context.HasInput("Seed") ? context.Input<Tensor>("Seed") : nullptr;
     auto* y = context.Output<Tensor>("Out");
     const auto* x_data = x->data<T>();
     auto* y_data = y->mutable_data<T>(context.GetPlace());
@@ -57,9 +59,14 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
       // Guarantee to use random seed in training.
       std::random_device rnd;
       std::minstd_rand engine;
-      int seed =
-          context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
-      engine.seed(seed);
+      int seed_data;
+      if (seed) {
+        seed_data = *(seed->data<int>());
+      } else {
+        seed_data =
+            context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
+      }
+      engine.seed(seed_data);
 
       std::uniform_real_distribution<float> dist(0, 1);
 

paddle/fluid/operators/seed_op.cc

+// Copyright (c) 2019 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.
+
+#include "paddle/fluid/operators/seed_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+class SeedOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    ctx->SetOutputDim("Out", {1});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(framework::proto::VarType::INT32,
+                                   platform::CPUPlace());
+  }
+};
+
+class SeedOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddOutput("Out", "The output of seed op.");
+    AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
+    AddComment(R"DOC(
+Seed Operator.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(
+    seed, ops::SeedOp, ops::SeedOpMaker,
+    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
+    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OP_CPU_KERNEL(
+    seed, ops::CPUSeedKernel<paddle::platform::CPUDeviceContext, int>);

paddle/fluid/operators/seed_op.h

+// Copyright (c) 2019 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.
+#pragma once
+
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class CPUSeedKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* out = context.Output<Tensor>("Out");
+    auto* out_data = out->mutable_data<T>(context.GetPlace());
+    int user_seed = context.Attr<int>("seed");
+
+    // NOTE: fixed seed should only be used in unittest or for debug.
+    // Guarantee to use random seed in training.
+    std::random_device rnd;
+    int seed;
+    if (user_seed != 0) {
+      seed = user_seed;
+    } else {
+      seed = rnd();
+    }
+    out_data[0] = seed;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/backward.py

@@ -56,7 +56,7 @@ class ProgramStats(object):
     def get_reserved_vars(self):
         var_name = []
         for op in self.ops:
-            if op.desc.type() == "dropout":
+            if op.desc.type() == "seed":
                 var_name.extend(op.desc.output_arg_names())
         return var_name
 
@@ -136,6 +136,42 @@ class ProgramStats(object):
         sorted_checkpoints = sorted(sorted_checkpoints, key=lambda x: x[1])
         return [x[0] for x in sorted_checkpoints]
 
+    def modify_forward_desc_for_recompute(self):
+        op_types = [op.desc.type() for op in self.ops]
+        if "dropout" not in op_types:
+            return
+
+        op_idx = 0
+        while (op_idx < len(self.ops)):
+            op = self.ops[op_idx]
+            if op.desc.type() != "dropout":
+                op_idx += 1
+                continue
+            # add a seed op so that the two dropout op can generate same output
+            op_unique_name = unique_name.generate("seed")
+            var_unique_name = unique_name.generate_with_ignorable_key(".".join(
+                [op_unique_name, 'tmp']))
+            added_var = self.block.create_var(
+                name=var_unique_name,
+                dtype='int32',
+                type=core.VarDesc.VarType.LOD_TENSOR,
+                persistable=False,
+                stop_gradient=False)
+            seed = 0 if op.attr("fix_seed") is False else int(op.attr("seed"))
+            added_op = self.block._insert_op(
+                index=op.idx,
+                type='seed',
+                inputs={},
+                outputs={'Out': [added_var]},
+                attrs={'seed': seed})
+            self.ops.insert(op_idx, added_op)
+            # modify dropout op desc so that it accept a seed var as input
+            op.desc.set_input("Seed", [var_unique_name])
+            op.desc.remove_attr("fix_seed")
+            op.desc.remove_attr("seed")
+            self.block._sync_with_cpp()
+            op_idx += 2
+
 
 def _pretty_op_desc_(op_desc, prefix):
     out_s = "%s\tname:[%s]\n%s    \tinputs:[%s]\n%s    \toutputs:[%s]" % \
@@ -589,6 +625,7 @@ def _append_backward_ops_with_checkpoints_(
         checkpoints: variables that a user defined as checkpoint for forward recomputation
 
     Algorithms:
+        0) deal with forward recomputing program descs
         1) find ops between checkpoints, i.e. recompute_segments
         2) go through all forward ops and induct all variables that will be hold in memory
             a. variables that are used across segments will be held in memory
@@ -609,10 +646,12 @@ def _append_backward_ops_with_checkpoints_(
     checkpoints_name = list(set(checkpoints_name))
     local_block = block.program._create_block()
     buffer_block = block.program._create_block()
-
-    # 1) find ops between checkpoints, i.e. recompute_segments
+    # 0) deal with forward recomputing program descs  
     program_stat = ProgramStats(block, ops)
+    program_stat.modify_forward_desc_for_recompute()
     program_stat.build_stats()
+
+    # 1) find ops between checkpoints, i.e. recompute_segments
     checkpoints_name = program_stat.sort_checkpoints(checkpoints_name)
     segments = []
 

python/paddle/fluid/tests/unittests/test_dropout_op.py

@@ -150,6 +150,27 @@ class TestDropoutOp9(OpTest):
         self.check_output()
 
 
+class TestDropoutOpWithSeed(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.inputs = {
+            "X": np.random.random((32, 64)).astype("float32"),
+            "Seed": np.asarray(
+                [125], dtype="int32")
+        }
+        self.attrs = {'dropout_prob': 0.0, }
+        self.outputs = {
+            'Out': self.inputs['X'],
+            'Mask': np.ones((32, 64)).astype('uint8')
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X'], 'Out', max_relative_error=0.05)
+
+
 class TestFP16DropoutOp(OpTest):
     def setUp(self):
         self.op_type = "dropout"

python/paddle/fluid/tests/unittests/test_optimizer.py

@@ -614,7 +614,7 @@ class TestLookaheadOptimizer(unittest.TestCase):
 
 
 class TestRecomputeOptimizer(unittest.TestCase):
-    def net(self, return_input=False):
+    def net(self, return_input=False, with_dropout=False):
         program = framework.Program()
         block = program.global_block()
         mul_x = block.create_parameter(
@@ -623,6 +623,14 @@ class TestRecomputeOptimizer(unittest.TestCase):
             dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
         mul_out = block.create_var(
             dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        if with_dropout == True:
+            mul_out_drop = block.create_var(
+                dtype="float32",
+                shape=[5, 8],
+                lod_level=0,
+                name="mul.out.dropout")
+            mul_out_mask = block.create_var(
+                dtype="uint8", shape=[5, 8], lod_level=0, name="mul.out.mask")
         b1 = block.create_parameter(
             dtype="float32", shape=[5, 8], lod_level=0, name="b1")
         b1_out = block.create_var(
@@ -639,11 +647,24 @@ class TestRecomputeOptimizer(unittest.TestCase):
                     "Y": mul_y},
             outputs={"Out": mul_out},
             attrs={"x_num_col_dims": 1})
-        block.append_op(
-            type="elementwise_add",
-            inputs={"X": mul_out,
-                    "Y": b1},
-            outputs={"Out": b1_out})
+        if with_dropout == True:
+            block.append_op(
+                type='dropout',
+                inputs={'X': [mul_out]},
+                outputs={'Out': [mul_out_drop],
+                         'Mask': [mul_out_mask]},
+                attrs={'dropout_prob': 0.5, })
+            block.append_op(
+                type="elementwise_add",
+                inputs={"X": mul_out_drop,
+                        "Y": b1},
+                outputs={"Out": b1_out})
+        else:
+            block.append_op(
+                type="elementwise_add",
+                inputs={"X": mul_out,
+                        "Y": b1},
+                outputs={"Out": b1_out})
         block.append_op(
             type="elementwise_add",
             inputs={"X": b1_out,
@@ -799,6 +820,29 @@ class TestRecomputeOptimizer(unittest.TestCase):
                 "load function is not supported by Recompute Optimizer for now",
                 cpt.get_exception_message(e))
 
+    def test_dropout(self):
+        """
+        If there are dropout layers in the forward nets, we should add a
+        seed op
+        """
+        mul_out, b1_out, b2_out, mean_out = self.net(with_dropout=True)
+        self.assertEqual(len(mean_out.block.ops), 5)
+        self.assertEqual(
+            [op.type for op in mean_out.block.ops],
+            ["mul", "dropout", "elementwise_add", "elementwise_add", "mean"])
+        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
+        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
+        recompute_optimizer._set_checkpoints([b1_out])
+        opts, params_grads = recompute_optimizer.minimize(mean_out)
+
+        self.assertEqual(len(mean_out.block.ops), 17)
+        self.assertEqual([op.type for op in mean_out.block.ops], [
+            "mul", "seed", "dropout", "elementwise_add", "elementwise_add",
+            "mean", "fill_constant", "mean_grad", "elementwise_add_grad", "mul",
+            "dropout", "elementwise_add_grad", "dropout_grad", "mul_grad",
+            "sgd", "sgd", "sgd"
+        ])
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_seed_op.py

+#   Copyright (c) 2018 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
+from op_test import OpTest
+import paddle.fluid as fluid
+
+
+class TestSeedOpFixSeed(OpTest):
+    def setUp(self):
+        self.op_type = "seed"
+        self.inputs = {}
+        self.attrs = {"seed": 123}
+        self.outputs = {"Out": np.asarray((123)).astype('int32')}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestSeedOpDiffSeed(OpTest):
+    def setUp(self):
+        self.op_type = "seed"
+        self.inputs = {}
+        self.attrs = {"seed": 0}
+        self.outputs = {"Out": np.asarray((123)).astype('int32')}
+
+    def test_check_output(self):
+        self.check_output(no_check_set=["Out"])
+
+
+if __name__ == '__main__':
+    unittest.main()