Add randint op API (#23337)

* add randint op

paddle/fluid/operators/randint_op.cc

@@ -0,0 +1,170 @@
+// 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 <string>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/uniform_random_op.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class CPURandintKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    std::vector<int64_t> new_shape;
+    auto list_new_shape_tensor =
+        ctx.MultiInput<framework::Tensor>("ShapeTensorList");
+    if (list_new_shape_tensor.size() > 0 || ctx.HasInput("ShapeTensor")) {
+      if (ctx.HasInput("ShapeTensor")) {
+        auto* shape_tensor = ctx.Input<framework::Tensor>("ShapeTensor");
+        new_shape = GetNewDataFromShapeTensor(shape_tensor);
+      } else if (list_new_shape_tensor.size() > 0) {
+        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();
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<> dist(ctx.Attr<int>("low"),
+                                         ctx.Attr<int>("high") - 1);
+    for (int64_t i = 0; i < size; ++i) data[i] = dist(gen);
+  }
+};
+
+class RandintOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_EQ(
+        ctx->HasOutput("Out"), true,
+        platform::errors::InvalidArgument("Output(Out) of RandintOp is null."));
+    PADDLE_ENFORCE_LT(
+        ctx->Attrs().Get<int>("low"), ctx->Attrs().Get<int>("high"),
+        platform::errors::InvalidArgument("randint's low must less then high, "
+                                          "but received: low = %d, high = %d.",
+                                          ctx->Attrs().Get<int>("low"),
+                                          ctx->Attrs().Get<int>("high")));
+
+    if (ctx->HasInputs("ShapeTensorList")) {
+      // top prority shape
+      auto inputs_name = ctx->Inputs("ShapeTensorList");
+      PADDLE_ENFORCE_GT(
+          inputs_name.size(), 0,
+          platform::errors::InvalidArgument(
+              "Input(ShapeTensorList)'size of Op(randint) can't be zero."
+              "Please check the Attr(shape)'s size of"
+              "Op(fluid.layers.randint).)"));
+      auto out_dims = std::vector<int>(inputs_name.size(), -1);
+      ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
+
+      return;
+    }
+
+    auto& shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
+    if (ctx->HasInput("ShapeTensor") && shape.empty()) {
+      auto shape_dims = ctx->GetInputDim("ShapeTensor");
+      PADDLE_ENFORCE_EQ(shape_dims.size(), 1,
+                        platform::errors::InvalidArgument(
+                            "ShapeError: Input(ShapeTensor)' dimension size of "
+                            "Op(randint) must be 1."
+                            "But received ShapeTensor's dimensions = %d.",
+                            shape_dims.size()));
+      int num_ele = 1;
+      for (int i = 0; i < shape_dims.size(); ++i) {
+        num_ele *= shape_dims[i];
+      }
+      auto vec_dims = std::vector<int64_t>(num_ele, -1);
+      auto out_dims = framework::make_ddim(vec_dims);
+      ctx->SetOutputDim("Out", out_dims);
+      return;
+    }
+
+    PADDLE_ENFORCE_EQ(shape.empty(), false,
+                      platform::errors::InvalidArgument(
+                          "if there is no Input(ShapeTensorList) and no "
+                          "Input(ShapeTensor),the "
+                          "attr(shape) information must "
+                          "be set by Attr(shape)."));
+
+    std::vector<int64_t> tensor_shape;
+    tensor_shape.reserve(shape.size());
+    for (auto dim : shape) {
+      tensor_shape.push_back(static_cast<int64_t>(dim));
+    }
+    ctx->SetOutputDim("Out", framework::make_ddim(tensor_shape));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype")),
+        ctx.GetPlace());
+  }
+};
+
+class RandintOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("ShapeTensor",
+             "(Tensor<int64_t> or Tensor<int32_t>, optional) . If provided, "
+             "randint"
+             "according to "
+             "this given shape. It means that it has a higher priority than "
+             "Attr(shape) but a lower priority than Input(ShapeTensor).")
+        .AsDispensable();
+    AddInput("ShapeTensorList",
+             "(vector<Tensor<int64_t>> or vector<Tensor<int32_t>>, optional). "
+             "If provided, randint use this. The shape of the tensor "
+             "must be [1], it has the highest priority comparing with "
+             "Input(ShapeTensor) and attr(shape).")
+        .AsDuplicable()
+        .AsDispensable();
+    AddOutput("Out", "The output tensor of randint op");
+    AddComment(R"DOC(
+This operator initializes a tensor with random integers sampled from a
+uniform distribution. The random result is in set [low, high).
+)DOC");
+    AddAttr<std::vector<int64_t>>("shape", "The shape of the output tensor.")
+        .SetDefault({});
+    AddAttr<int>("low",
+                 "The lower bound on the range of random values to generate.");
+    AddAttr<int>("high",
+                 "The upper bound on the range of random values to generate.");
+    AddAttr<int>("dtype", "Output tensor data type. [Default INT64].")
+        .SetDefault(framework::proto::VarType::INT64);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(
+    randint, ops::RandintOp, ops::RandintOpMaker,
+    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
+    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>)
+
+REGISTER_OP_CPU_KERNEL(randint, ops::CPURandintKernel<int>,
+                       ops::CPURandintKernel<int64_t>)

paddle/fluid/operators/randint_op.cu

@@ -0,0 +1,76 @@
+// 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 <thrust/random.h>
+#include <thrust/transform.h>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/uniform_random_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct UniformIntGenerator {
+  T low_, high_;
+  __host__ __device__ UniformIntGenerator(T low, T high)
+      : low_(low), high_(high) {}
+
+  __host__ __device__ T operator()(const unsigned int n) const {
+    thrust::minstd_rand rng;
+    rng.seed(0);
+    thrust::uniform_int_distribution<T> dist(low_, high_);
+    rng.discard(n);
+    T out = dist(rng);
+    return out;
+  }
+};
+
+// Use std::uniform_int_distribution and thrust::uniform_int_distribution(thrust
+// is a std library in CUDA) to
+// implement randint.
+template <typename T>
+class GPURandintKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    std::vector<int64_t> new_shape;
+    auto list_new_shape_tensor =
+        context.MultiInput<framework::Tensor>("ShapeTensorList");
+    if (list_new_shape_tensor.size() > 0 || context.HasInput("ShapeTensor")) {
+      if (context.HasInput("ShapeTensor")) {
+        auto* shape_tensor = context.Input<framework::Tensor>("ShapeTensor");
+        new_shape = GetNewDataFromShapeTensor(shape_tensor);
+      } else if (list_new_shape_tensor.size() > 0) {
+        new_shape = GetNewDataFromShapeTensorList(list_new_shape_tensor);
+      }
+    }
+
+    auto* out = context.Output<framework::LoDTensor>("Out");
+    if (!new_shape.empty()) out->Resize(framework::make_ddim(new_shape));
+    T* data = out->mutable_data<T>(context.GetPlace());
+    T low = static_cast<T>(context.Attr<int>("low"));
+    T high = static_cast<T>(context.Attr<int>("high")) - 1;
+
+    thrust::counting_iterator<unsigned int> index_sequence_begin(0);
+    int64_t size = out->numel();
+    thrust::transform(index_sequence_begin, index_sequence_begin + size,
+                      thrust::device_ptr<T>(data),
+                      UniformIntGenerator<T>(low, high));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(randint, ops::GPURandintKernel<int>,
+                        ops::GPURandintKernel<int64_t>)

python/paddle/__init__.py

@@ -90,7 +90,7 @@ from .tensor.logic import elementwise_equal  #DEFINE_ALIAS
 # from .tensor.random import randn   #DEFINE_ALIAS
 from .tensor.random import randperm
 # from .tensor.random import rand   #DEFINE_ALIAS
-# from .tensor.random import randint   #DEFINE_ALIAS
+from .tensor.random import randint  #DEFINE_ALIAS
 # from .tensor.math import abs   #DEFINE_ALIAS
 # from .tensor.math import acos   #DEFINE_ALIAS
 # from .tensor.math import asin   #DEFINE_ALIAS

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

@@ -0,0 +1,173 @@
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import paddle
+
+
+def output_hist(out):
+    hist, _ = np.histogram(out, range=(-5, 10))
+    hist = hist.astype("float32")
+    hist /= float(out.size)
+    prob = 0.1 * np.ones((10))
+    return hist, prob
+
+
+class TestRandintOp(OpTest):
+    def setUp(self):
+        self.op_type = "randint"
+        self.inputs = {}
+        self.init_attrs()
+        self.outputs = {"Out": np.zeros((10000, 784)).astype("float32")}
+
+    def init_attrs(self):
+        self.attrs = {"shape": [10000, 784], "low": -5, "high": 10}
+        self.output_hist = output_hist
+
+    def test_check_output(self):
+        self.check_output_customized(self.verify_output)
+
+    def verify_output(self, outs):
+        hist, prob = self.output_hist(np.array(outs[0]))
+        self.assertTrue(
+            np.allclose(
+                hist, prob, rtol=0, atol=0.1), "hist: " + str(hist))
+
+
+class TestRandintOpError(unittest.TestCase):
+    def test_errors(self):
+        main_prog = Program()
+        start_prog = Program()
+        with program_guard(main_prog, start_prog):
+
+            def test_shape():
+                shape = np.array([2, 3])
+                paddle.randint(5, shape=shape, dtype='int32')
+
+            self.assertRaises(TypeError, test_shape)
+
+            def test_dtype():
+                paddle.randint(5, shape=[32, 32], dtype='float32')
+
+            self.assertRaises(TypeError, test_dtype)
+
+            def test_low_high():
+                paddle.randint(low=5, high=5, shape=[32, 32], dtype='int32')
+
+            self.assertRaises(ValueError, test_low_high)
+
+
+class TestRandintOp_attr_tensorlist(OpTest):
+    def setUp(self):
+        self.op_type = "randint"
+        self.new_shape = (10000, 784)
+        shape_tensor = []
+        for index, ele in enumerate(self.new_shape):
+            shape_tensor.append(("x" + str(index), np.ones(
+                (1)).astype("int64") * ele))
+        self.inputs = {'ShapeTensorList': shape_tensor}
+        self.init_attrs()
+        self.outputs = {"Out": np.zeros((10000, 784)).astype("int32")}
+
+    def init_attrs(self):
+        self.attrs = {"low": -5, "high": 10}
+        self.output_hist = output_hist
+
+    def test_check_output(self):
+        self.check_output_customized(self.verify_output)
+
+    def verify_output(self, outs):
+        hist, prob = self.output_hist(np.array(outs[0]))
+        self.assertTrue(
+            np.allclose(
+                hist, prob, rtol=0, atol=0.1), "hist: " + str(hist))
+
+
+class TestRandint_attr_tensor(OpTest):
+    def setUp(self):
+        self.op_type = "randint"
+        self.inputs = {"ShapeTensor": np.array([10000, 784]).astype("int64")}
+        self.init_attrs()
+        self.outputs = {"Out": np.zeros((10000, 784)).astype("int64")}
+
+    def init_attrs(self):
+        self.attrs = {"low": -5, "high": 10}
+        self.output_hist = output_hist
+
+    def test_check_output(self):
+        self.check_output_customized(self.verify_output)
+
+    def verify_output(self, outs):
+        hist, prob = self.output_hist(np.array(outs[0]))
+        self.assertTrue(
+            np.allclose(
+                hist, prob, rtol=0, atol=0.1), "hist: " + str(hist))
+
+
+# Test python API
+class TestRandintAPI(unittest.TestCase):
+    def test_api(self):
+        startup_program = fluid.Program()
+        train_program = fluid.Program()
+        with fluid.program_guard(train_program, startup_program):
+            # results are from [0, 5).
+            output1 = paddle.randint(5)
+            # shape is a list and dtype is 'int32'
+            output2 = paddle.randint(
+                low=-100, high=100, shape=[64, 64], dtype='int32')
+            # shape is a tuple and dtype is 'int64'
+            output3 = paddle.randint(
+                low=-100, high=100, shape=(32, 32, 3), dtype='int64')
+            # shape is a tensorlist and dtype is 'float32'
+            dim_1 = fluid.layers.fill_constant([1], "int64", 32)
+            dim_2 = fluid.layers.fill_constant([1], "int32", 50)
+            output4 = paddle.randint(
+                low=-100, high=100, shape=[dim_1, 5], dtype='int32')
+            # shape is a tensor and dtype is 'float64'
+            var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
+            output5 = paddle.randint(
+                low=1, high=1000, shape=var_shape, dtype='int64')
+
+            place = fluid.CPUPlace()
+            if fluid.core.is_compiled_with_cuda():
+                place = fluid.CUDAPlace(0)
+            exe = fluid.Executor(place)
+
+            exe.run(startup_program)
+            outs = exe.run(
+                train_program,
+                feed={'var_shape': np.array([100, 100]).astype('int64')},
+                fetch_list=[output1, output2, output3, output4, output5])
+
+
+class TestRandintDygraphMode(unittest.TestCase):
+    def test_check_output(self):
+        with fluid.dygraph.guard():
+            x = paddle.randint(10, shape=[10], dtype="int32")
+            x_np = x.numpy()
+            for i in range(10):
+                self.assertTrue((x_np[i] >= 0 and x_np[i] < 10))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -64,7 +64,7 @@ from .logic import elementwise_equal  #DEFINE_ALIAS
 # from .random import shuffle   #DEFINE_ALIAS
 # from .random import randn   #DEFINE_ALIAS
 # from .random import rand   #DEFINE_ALIAS
-# from .random import randint   #DEFINE_ALIAS
+from .random import randint  #DEFINE_ALIAS
 from .random import randperm
 # from .math import abs   #DEFINE_ALIAS
 # from .math import acos   #DEFINE_ALIAS

python/paddle/tensor/random.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 # TODO: define random functions  
+
 # __all__ = ['gaussin', 
 #            'uniform', 
 #            'shuffle',
@@ -21,12 +22,173 @@
 #            'randint']
 
 from ..fluid import core
-from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator
+from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator, Variable
 from ..fluid.layers.layer_function_generator import templatedoc
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
+from ..fluid.layers import utils
+from ..fluid.layers.tensor import fill_constant
+
+__all__ = ['randperm', 'randint']
+
+
+def randint(low,
+            high=None,
+            shape=None,
+            out=None,
+            dtype=None,
+            device=None,
+            stop_gradient=False,
+            name=None):
+    """
+    This function returns a Tensor filled with random integers from the "discrete uniform" distribution of the
+    specified data type in the interval [low, high). If high is None (the default), then results are from [0, low).
+
+    Args:
+        low (int): The lower bound on the range of random values to generate, the low is included in the range.
+            (unless high=None, in which case this parameter is one above the highest such integer).
+        high (int, optional): The upper bound on the range of random values to generate, the high is excluded 
+            in the range. Default None(see above for behavior if high=None).
+        shape (list|tuple|Variable, optional): The shape of the output Tensor,  if the shape is a list or tuple, 
+                                     its elements can be an integer
+                                     or a Tensor with the shape [1], and the type of the Tensor must be int32 or int64. 
+                                     If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor must be 
+                                     int32 or int64. Default is None, in which case the shape is [1].
+        out(Variable, optional): Optional output which can be any created 
+            Variable that meets the requirements to store the result of operation.
+            if out is None, a new Varibale will be create to store the result.
+        dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output Tensor
+            which can be int32, int64, if dytpe is `None`, the data
+            type of created Tensor is `int64`
+        device(str, optional): This parameter specifies that the Tensor is created 
+            on the GPU or CPU.
+        stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
+            default value is False.
+        name(str, optional): The default value is None.  Normally there is no need for user to set this
+            property.  For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns: 
+        Variable: A Tensor of the specified shape filled with random integers.
+
+    Raises:
+        TypeError: Randint's low must less then high.
+
+    Examples:
+        .. code-block:: python
+            import paddle
+            import paddle.tensor as tensor
+
+            # example 1:
+            # attr shape is a list which doesn't contain tensor Variable.
+            result_1 = paddle.randint(low=-5, high=5, shape=[3, 4], dtype="int64")
+
+            # example 2:
+            # attr shape is a list which contains tensor Variable.
+            dim_1 = fluid.layers.fill_constant([1],"int64",3)
+            dim_2 = fluid.layers.fill_constant([1],"int32",5)
+            result_2 = paddle.randint(low=-5, high=5, shape=[dim_1, dim_2], dtype="int32")
+
+            # example 3:
+            # attr shape is a Variable, the data type must be int64 or int32.
+            var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
+            result_3 = padddle.randint(low=-5, high=5, shape=var_shape, dtype="int32")
+            var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
+            result_4 = paddle.randint(low=-5, high=5, shape=var_shape_int32, dtype="int64")
+
+            # example 4:
+            # Input only one parameter
+            # low=0, high=10, shape=[1], dtype='int64'
+            result_4 = paddle.randint(10)
+     """
 
-__all__ = ['randperm']
+    def get_new_shape_tensor(list_shape):
+        new_shape_tensor = []
+        for dim in list_shape:
+            if isinstance(dim, Variable):
+                dim.stop_gradient = True
+                new_shape_tensor.append(dim)
+            else:
+                assert isinstance(dim, int) or isinstance(dim, long)
+                temp_out = helper.create_variable_for_type_inference('int64')
+                fill_constant([1], 'int64', dim, force_cpu=True, out=temp_out)
+                new_shape_tensor.append(temp_out)
+        return new_shape_tensor
+
+    def get_attr_shape(list_shape):
+        unk_dim_idx = -1
+        attrs_shape = []
+        for dim_idx, dim_size in enumerate(list_shape):
+            if isinstance(dim_size, Variable):
+                attrs_shape.append(-1)
+            else:
+                attrs_shape.append(dim_size)
+                assert dim_size > 0, (
+                    "Each dimension size given in shape must not be negative "
+                    "except one unknown dimension.")
+        return attrs_shape
+
+    if dtype is None:
+        dtype = 'int64'
+    check_dtype(dtype, 'dtype', ['int32', 'int64'], 'randint')
+
+    inputs = dict()
+    attrs = dict()
+
+    if shape is None:
+        shape = [1]
+        assert len(shape) > 0, ("The size of argument(shape) can't be zero.")
+
+    helper = LayerHelper("randint", **locals())
+
+    if in_dygraph_mode():
+        attrs['shape'] = shape
+    else:
+        if isinstance(shape, Variable):
+            shape.stop_gradient = True
+            inputs["ShapeTensor"] = shape
+        elif isinstance(shape, (list, tuple)):
+            assert len(shape) > 0, (
+                "The size of argument(shape) can't be zero.")
+            if utils._contain_var(shape):
+                inputs['ShapeTensorList'] = get_new_shape_tensor(shape)
+            else:
+                attrs["shape"] = get_attr_shape(shape)
+    check_type(shape, 'shape', (list, tuple, Variable), 'randint')
+
+    if high is None:
+        high = low
+        low = 0
+    attrs['low'] = low
+    attrs['high'] = high
+    if (low >= high):
+        raise ValueError(
+            "randint's low must less then high, but received low = {0}, "
+            "high = {1}".format(low, high))
+
+    if out is None:
+        if name is None:
+            out = helper.create_variable_for_type_inference(dtype=dtype)
+        else:
+            out = helper.create_variable(
+                name=name, dtype=dtype, persistable=False)
+    else:
+        check_dtype(dtype, 'dtype',
+                    convert_dtype(out.dtype), 'randint',
+                    "(The dtype in randint must be the same with out's dtype.)")
+    attrs['dtype'] = out.dtype
+    out.stop_gradient = stop_gradient
+
+    if device is None:
+        helper.append_op(
+            type='randint', inputs=inputs, outputs={'Out': out}, attrs=attrs)
+    else:
+        with device_guard(device):
+            helper.append_op(
+                type='randint',
+                inputs=inputs,
+                outputs={'Out': out},
+                attrs=attrs)
+    return out
 
 
 @templatedoc()