add cpu random Generator (#26013)

paddle/fluid/framework/CMakeLists.txt

@@ -268,6 +268,7 @@ cc_test(op_compatible_info_test SRCS op_compatible_info_test.cc DEPS op_compatib
 
 cc_library(save_load_util SRCS save_load_util DEPS tensor scope layer)
 cc_test(save_load_util_test SRCS save_load_util_test.cc DEPS save_load_util tensor scope layer)
+cc_library(generator SRCS generator.cc)
 
 # Get the current working branch
 execute_process(

paddle/fluid/framework/generator.cc

@@ -0,0 +1,78 @@
+/* Copyright (c) 2020 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 <deque>
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+
+#include "paddle/fluid/framework/generator.h"
+
+namespace paddle {
+namespace framework {
+
+std::shared_ptr<Generator> Generator::gen_instance_ = NULL;
+
+GeneratorState* Generator::GetState() {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  return this->state_.get();
+}
+
+void Generator::SetState(GeneratorState* state_in) {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  *this->state_ = *state_in;
+}
+
+uint64_t Generator::GetCurrentSeed() {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  return this->state_->current_seed;
+}
+
+uint64_t Generator::Seed() {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  uint64_t seed;
+  std::random_device de;
+  seed = ((((uint64_t)de()) << 32) + de()) & 0x1FFFFFFFFFFFFF;
+  this->state_->current_seed = seed;
+  std::seed_seq seq({seed});
+  this->state_->cpu_engine.seed(seq);
+
+  return this->state_->current_seed;
+}
+
+void Generator::SetCurrentSeed(uint64_t seed) {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  this->state_->current_seed = uint64_t(seed);
+  std::seed_seq seq({seed});
+  this->state_->cpu_engine.seed(seq);
+}
+
+std::mt19937_64& Generator::GetCPUEngine() {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  return this->state_->cpu_engine;
+}
+
+void Generator::SetCPUEngine(std::mt19937_64 engine) {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  this->state_->cpu_engine = std::mt19937_64(engine);
+}
+
+uint64_t Generator::Random64() {
+  std::lock_guard<std::mutex> lock(this->mutex);
+  return this->state_->cpu_engine();
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/generator.h

@@ -0,0 +1,96 @@
+/* Copyright (c) 2020 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 <stdint.h>
+#include <atomic>
+#include <deque>
+#include <iostream>  // temp for debug
+#include <memory>
+#include <mutex>  // NOLINT
+#include <random>
+#include <typeinfo>
+#include <utility>
+
+namespace paddle {
+namespace framework {
+
+struct GeneratorState {
+  int64_t device = -1;
+  uint64_t current_seed = 34342423252;
+  std::mt19937_64 cpu_engine;
+};
+
+struct Generator {
+  Generator() {
+    GeneratorState default_gen_state_cpu;
+    default_gen_state_cpu.device = -1;
+    default_gen_state_cpu.current_seed = 34342423252;
+    std::seed_seq seq({34342423252});
+    default_gen_state_cpu.cpu_engine = std::mt19937_64(seq);
+    this->state_ = std::make_shared<GeneratorState>(default_gen_state_cpu);
+  }
+  explicit Generator(GeneratorState state_in)
+      : state_{std::make_shared<GeneratorState>(state_in)} {}
+  Generator(const Generator& other)
+      : Generator(other, std::lock_guard<std::mutex>(other.mutex)) {}
+
+  // get random state
+  GeneratorState* GetState();
+  // set random state
+  void SetState(GeneratorState* state_in);
+  // get current seed
+  uint64_t GetCurrentSeed();
+  // random a seed and get
+  uint64_t Seed();
+
+  // set seed
+  void SetCurrentSeed(uint64_t seed);
+  // get cpu engine
+  std::mt19937_64& GetCPUEngine();
+  // set cpu engine
+  void SetCPUEngine(std::mt19937_64 engine);
+
+  uint64_t Random64();
+
+  bool is_init_py = false;
+
+  // CPU Generator singleton
+  static std::shared_ptr<Generator> GetInstance() {
+    if (NULL == gen_instance_) {
+      gen_instance_.reset(new paddle::framework::Generator());
+    }
+    return gen_instance_;
+  }
+
+  static std::shared_ptr<Generator> GetInstanceX() {
+    if (NULL == gen_instance_) {
+      gen_instance_.reset(new paddle::framework::Generator());
+    }
+    gen_instance_->is_init_py = true;
+    return gen_instance_;
+  }
+
+ private:
+  static std::shared_ptr<Generator> gen_instance_;
+  std::shared_ptr<GeneratorState> state_;
+  mutable std::mutex mutex;
+
+  Generator(const Generator& other, const std::lock_guard<std::mutex>&)
+      : state_(std::make_shared<GeneratorState>(*(other.state_))) {}
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/operators/CMakeLists.txt

@@ -88,7 +88,9 @@ endif()
 
 cc_library(common_infer_shape_functions SRCS common_infer_shape_functions.cc DEPS operator)
 
-set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor device_memory_aligment)
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows
+lod_tensor maxouting unpooling pooling lod_rank_table context_project
+sequence_pooling executor device_memory_aligment generator)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler sample_prob tree2col)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions beam_search fc matrix_inverse)

paddle/fluid/operators/gaussian_random_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <random>
+
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/fill_constant_op.h"
 #ifdef PADDLE_WITH_MKLDNN
@@ -30,13 +31,13 @@ class CPUGaussianRandomKernel : public framework::OpKernel<T> {
     float mean = context.Attr<float>("mean");
     float std = context.Attr<float>("std");
     auto* tensor = context.Output<framework::Tensor>("Out");
-
     unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
     std::minstd_rand engine;
     if (seed == 0) {
       seed = std::random_device()();
     }
     engine.seed(seed);
+
     std::normal_distribution<T> dist(mean, std);
 
     const std::string op_type = "gaussian_random";

paddle/fluid/operators/randint_op.cc

@@ -14,6 +14,7 @@
 
 #include <string>
 #include <vector>
+
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/operators/uniform_random_op.h"
@@ -37,11 +38,11 @@ class CPURandintKernel : public framework::OpKernel<T> {
         new_shape = GetNewDataFromShapeTensorList(list_new_shape_tensor);
       }
     }
-
     auto* out = ctx.Output<framework::LoDTensor>("Out");
     if (!new_shape.empty()) out->Resize(framework::make_ddim(new_shape));
     T* data = out->mutable_data<T>(ctx.GetPlace());
     int64_t size = out->numel();
+
     unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
     std::minstd_rand engine;
     if (seed == 0) {

paddle/fluid/operators/uniform_random_op.cc

@@ -13,8 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 #include "paddle/fluid/operators/uniform_random_op.h"
 #include <string>
+#include "paddle/fluid/framework/generator.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
+
 namespace paddle {
 namespace operators {
 
@@ -55,19 +57,40 @@ class CPUUniformRandomKernel : public framework::OpKernel<T> {
           "supports SelectedRows and LoDTensor");
     }
     T *data = tensor->mutable_data<T>(ctx.GetPlace());
-    unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
+
-    std::minstd_rand engine;
+    int64_t size = tensor->numel();
-    if (seed == 0) {
-      seed = std::random_device()();
-    }
-    engine.seed(seed);
     std::uniform_real_distribution<T> dist(
         static_cast<T>(ctx.Attr<float>("min")),
         static_cast<T>(ctx.Attr<float>("max")));
-    int64_t size = tensor->numel();
+    auto gen_ptr = framework::Generator::GetInstance();
-    for (int64_t i = 0; i < size; ++i) {
+    if (gen_ptr->is_init_py) {
-      data[i] = dist(engine);
+      std::mt19937_64 &gen_engine = gen_ptr->GetCPUEngine();
+      // auto gen_engine = gen_ptr_->GetCPUEngine();
+      // std::uniform_real_distribution<T> dist(
+      //    static_cast<T>(ctx.Attr<float>("min")),
+      //    static_cast<T>(ctx.Attr<float>("max")));
+
+      for (int64_t i = 0; i < size; ++i) {
+        data[i] = dist(gen_engine);
+      }
+    } else {
+      unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
+      std::minstd_rand engine;
+      if (seed == 0) {
+        seed = std::random_device()();
+      }
+      engine.seed(seed);
+      // std::uniform_real_distribution<T> dist(
+      //    static_cast<T>(ctx.Attr<float>("min")),
+      //    static_cast<T>(ctx.Attr<float>("max")));
+      // int64_t size = tensor->numel();
+      for (int64_t i = 0; i < size; ++i) {
+        data[i] = dist(engine);
+      }
     }
+    // std::mt19937_64 &engine = gen_ptr->GetCPUEngine();
+    // auto engine = gen_ptr_->GetCPUEngine();
+
     unsigned int diag_num =
         static_cast<unsigned int>(ctx.Attr<int>("diag_num"));
     unsigned int diag_step =

paddle/fluid/operators/uniform_random_op.cu

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 #include <thrust/random.h>
 #include <thrust/transform.h>
+#include "paddle/fluid/framework/generator.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/operators/uniform_random_op.h"
@@ -87,9 +88,14 @@ class GPUUniformRandomKernel : public framework::OpKernel<T> {
     }
     T* data = tensor->mutable_data<T>(context.GetPlace());
     unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
-    if (seed == 0) {
+    if (framework::Generator::GetInstance()->is_init_py) {
-      std::random_device rd;
+      seed = static_cast<unsigned int>(
-      seed = rd();
+          framework::Generator::GetInstance()->GetCurrentSeed());
+    } else {
+      if (seed == 0) {
+        std::random_device rd;
+        seed = rd();
+      }
     }
     T min = static_cast<T>(context.Attr<float>("min"));
     T max = static_cast<T>(context.Attr<float>("max"));

paddle/fluid/operators/uniform_random_op.h

@@ -17,6 +17,7 @@
 #include <utility>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/pybind/CMakeLists.txt

@@ -1,7 +1,7 @@
 set(PYBIND_DEPS pybind python proto_desc memory executor fleet_wrapper box_wrapper prune
   feed_fetch_method pass_builder parallel_executor profiler layer tracer engine scope_pool
   analysis_predictor imperative_profiler imperative_flag save_load_util dlpack_tensor device_context
-  gloo_wrapper infer_io_utils heter_wrapper)
+  gloo_wrapper infer_io_utils heter_wrapper generator)
 
 if (WITH_NCCL)
   set(PYBIND_DEPS ${PYBIND_DEPS} nccl_wrapper)
@@ -37,7 +37,8 @@ set(PYBIND_SRCS
   data_set_py.cc
   imperative.cc
   ir.cc
-  inference_api.cc)
+  inference_api.cc
+  generator_py.cc)
 
 if (WITH_CRYPTO)
   set(PYBIND_DEPS ${PYBIND_DEPS} paddle_crypto)

paddle/fluid/pybind/generator_py.cc

@@ -0,0 +1,51 @@
+/* Copyright (c) 2020 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 <fcntl.h>
+
+#ifdef _POSIX_C_SOURCE
+#undef _POSIX_C_SOURCE
+#endif
+
+#ifdef _XOPEN_SOURCE
+#undef _XOPEN_SOURCE
+#endif
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/pybind/generator_py.h"
+
+namespace py = pybind11;
+
+namespace paddle {
+namespace pybind {
+void BindGenerator(py::module* m) {
+  py::class_<framework::GeneratorState>(*m, "GeneratorState", "");
+  py::class_<std::mt19937_64>(*m, "mt19937_64", "");
+  py::class_<framework::Generator, std::shared_ptr<framework::Generator>>(
+      *m, "Generator")
+      .def(py::init([]() { return framework::Generator::GetInstanceX(); }),
+           py::return_value_policy::reference)
+      .def("get_state", &framework::Generator::GetState,
+           py::return_value_policy::move)
+      .def("set_state", &framework::Generator::SetState)
+      .def("manual_seed", &framework::Generator::SetCurrentSeed)
+      .def("seed", &framework::Generator::Seed)
+      .def("initial_seed", &framework::Generator::GetCurrentSeed)
+      .def("random", &framework::Generator::Random64)
+      .def("get_cpu_engine", &framework::Generator::GetCPUEngine,
+           py::return_value_policy::move)
+      .def("set_cpu_engine", &framework::Generator::SetCPUEngine);
+}  // end Generator
+}  // end namespace pybind
+}  // end namespace paddle

paddle/fluid/pybind/generator_py.h

@@ -0,0 +1,28 @@
+//   Copyright (c) 2020 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 "pybind11/pybind11.h"
+#include "pybind11/stl.h"
+
+namespace py = pybind11;
+
+namespace paddle {
+namespace pybind {
+
+void BindGenerator(py::module* m);
+
+}  // namespace pybind
+}  // namespace paddle

paddle/fluid/pybind/pybind.cc

@@ -64,6 +64,7 @@ limitations under the License. */
 #include "paddle/fluid/pybind/data_set_py.h"
 #include "paddle/fluid/pybind/exception.h"
 #include "paddle/fluid/pybind/fleet_wrapper_py.h"
+#include "paddle/fluid/pybind/generator_py.h"
 #include "paddle/fluid/pybind/global_value_getter_setter.h"
 #include "paddle/fluid/pybind/gloo_wrapper_py.h"
 #include "paddle/fluid/pybind/heter_wrapper_py.h"
@@ -2503,6 +2504,7 @@ All parameter, weight, gradient are variables in Paddle.
   BindNode(&m);
   BindInferenceApi(&m);
   BindDataset(&m);
+  BindGenerator(&m);
 #ifdef PADDLE_WITH_CRYPTO
   BindCrypto(&m);
 #endif

python/paddle/fluid/__init__.py

@@ -89,6 +89,7 @@ from .dygraph.base import enable_dygraph, disable_dygraph
 from .io import save, load, load_program_state, set_program_state
 from .dygraph.checkpoint import save_dygraph, load_dygraph
 from .dygraph.varbase_patch_methods import monkey_patch_varbase
+from . import generator
 Tensor = LoDTensor
 enable_imperative = enable_dygraph
 disable_imperative = disable_dygraph
@@ -96,7 +97,7 @@ disable_imperative = disable_dygraph
 __all__ = framework.__all__ + executor.__all__ + \
     trainer_desc.__all__ + transpiler.__all__ + \
     parallel_executor.__all__ + lod_tensor.__all__ + \
-    data_feed_desc.__all__ + compiler.__all__ + backward.__all__  + [
+    data_feed_desc.__all__ + compiler.__all__ + backward.__all__  + generator.__all__ + [
         'io',
         'initializer',
         'embedding',

python/paddle/fluid/generator.py

@@ -0,0 +1,60 @@
+#   Copyright (c) 2020 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.
+"""This is definition of generator class, which is for managing the state of the algorithm that produces pseudo random numbers."""
+
+from . import core
+
+__all__ = ['Generator']
+
+default_rng_seed_val = 34342423252
+
+
+class Generator(object):
+    """Generator class"""
+
+    def __init__(self, device="CPU"):
+        """init"""
+        self.device = device
+        seed_in = default_rng_seed_val
+        if self.device == "CPU":
+            self.generator = core.Generator()
+            self.generator.manual_seed(seed_in)
+        else:
+            raise ValueError(
+                "generator class with device %s does not exist, currently only support generator with device 'CPU' "
+                % device)
+
+    def get_state(self):
+        return self.generator.get_state()
+
+    def set_state(self, state):
+        self.generator.set_state(state)
+
+    def manual_seed(self, seed):
+        self.generator.manual_seed(seed)
+
+    def seed(self):
+        return self.generator.seed()
+
+    def initial_seed(self):
+        return self.generator.initial_seed()
+
+    def random(self):
+        return self.generator.random()
+
+    def get_cpu_engine(self):
+        return self.generator.get_cpu_engine()
+
+    def set_cpu_engine(self, cpu_engine):
+        self.generator.set_cpu_engine(cpu_engine)

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

@@ -0,0 +1,44 @@
+#   Copyright (c) 2020 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.
+"""Test cloud role maker."""
+
+from __future__ import print_function
+import os
+import unittest
+import paddle.fluid.generator as generator
+import time  # temp for debug
+
+
+class TestGenerator(unittest.TestCase):
+    """
+    Test cases for cpu generator.
+    """
+
+    def test_basic_generator(self):
+        """Test basic generator."""
+        gen = generator.Generator()
+        gen.manual_seed(123123143)
+        s = gen.initial_seed()
+        s = gen.seed()
+        st = gen.get_state()
+        gen.set_state(st)
+        gen.random()
+        gen.set_cpu_engine(gen.get_cpu_engine())
+
+    def test_basic_generator_error(self):
+        self.assertRaises(ValueError, generator.Generator, device="CUDA")
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -0,0 +1,119 @@
+#   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.
+"""Test cloud role maker."""
+
+from __future__ import print_function
+import os
+import unittest
+import paddle.fluid.generator as generator
+
+import time  # temp for debug
+import paddle.fluid as fluid
+import numpy as np
+import paddle
+import paddle.fluid.core as core
+
+
+class TestGeneratorSeed(unittest.TestCase):
+    """
+    Test cases for cpu generator seed.
+    """
+
+    def test_generator_uniform_random_dygraph(self):
+        """Test Generator seed."""
+        gen = generator.Generator()
+
+        fluid.enable_dygraph()
+
+        gen.manual_seed(12312321111)
+        x = fluid.layers.uniform_random([10], dtype="float32", min=0.0, max=1.0)
+        st1 = gen.get_state()
+        x1 = fluid.layers.uniform_random(
+            [10], dtype="float32", min=0.0, max=1.0)
+        gen.set_state(st1)
+        x2 = fluid.layers.uniform_random(
+            [10], dtype="float32", min=0.0, max=1.0)
+        gen.manual_seed(12312321111)
+        x3 = fluid.layers.uniform_random(
+            [10], dtype="float32", min=0.0, max=1.0)
+        x_np = x.numpy()
+        x1_np = x1.numpy()
+        x2_np = x2.numpy()
+        x3_np = x3.numpy()
+
+        if not core.is_compiled_with_cuda():
+            self.assertTrue(np.allclose(x1_np, x2_np))
+            self.assertTrue(np.allclose(x_np, x3_np))
+
+    def test_generator_uniform_random_static(self):
+
+        fluid.disable_dygraph()
+
+        gen = generator.Generator()
+        gen.manual_seed(123123143)
+
+        startup_program = fluid.Program()
+        train_program = fluid.Program()
+        with fluid.program_guard(train_program, startup_program):
+            # example 1:
+            # attr shape is a list which doesn't contain tensor Variable.
+            result_1 = fluid.layers.uniform_random(shape=[3, 4])
+            result_2 = fluid.layers.uniform_random(shape=[3, 4])
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            exe.run(startup_program)
+            out1 = exe.run(train_program,
+                           feed={},
+                           fetch_list=[result_1, result_2])
+            #gen.set_state(cur_state)
+            gen.manual_seed(123123143)
+            out2 = exe.run(train_program,
+                           feed={},
+                           fetch_list=[result_1, result_2])
+
+            out1_res1 = np.array(out1[0])
+            out1_res2 = np.array(out1[1])
+            out2_res1 = np.array(out2[0])
+            out2_res2 = np.array(out2[1])
+
+            if not core.is_compiled_with_cuda():
+                self.assertTrue(np.allclose(out1_res1, out2_res1))
+                self.assertTrue(np.allclose(out1_res2, out2_res2))
+                self.assertTrue(not np.allclose(out1_res2, out1_res1))
+
+    def test_generator_randint_dygraph(self):
+        """Test Generator seed."""
+        gen = generator.Generator()
+
+        fluid.enable_dygraph()
+
+        gen.manual_seed(12312321111)
+        x = paddle.randint(low=1)
+        st1 = gen.get_state()
+        x1 = paddle.randint(low=1)
+        gen.set_state(st1)
+        x2 = paddle.randint(low=1)
+        gen.manual_seed(12312321111)
+        x3 = paddle.randint(low=1)
+        x_np = x.numpy()
+        x1_np = x1.numpy()
+        x2_np = x2.numpy()
+        x3_np = x3.numpy()
+        if not core.is_compiled_with_cuda():
+            self.assertTrue(np.allclose(x1_np, x2_np))
+            self.assertTrue(np.allclose(x_np, x3_np))
+
+
+if __name__ == "__main__":
+    unittest.main()