add 'seed' arguemnt of randint API (#23809) (#24094)

test=release/2.0-beta, cherry-pick PR #23809: add 'seed' arguemnt of randint API

paddle/fluid/operators/randint_op.cc

@@ -42,11 +42,15 @@ class CPURandintKernel : public framework::OpKernel<T> {
     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"),
+    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_int_distribution<T> dist(ctx.Attr<int>("low"),
                                           ctx.Attr<int>("high") - 1);
-    for (int64_t i = 0; i < size; ++i) data[i] = dist(gen);
+    for (int64_t i = 0; i < size; ++i) data[i] = dist(engine);
   }
 };
 
@@ -153,6 +157,12 @@ uniform distribution. The random result is in set [low, 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);
+    AddAttr<int>("seed",
+                 "Random seed used for generating samples. "
+                 "0 means use a seed generated by the system."
+                 "Note that if seed is not 0, this operator will always "
+                 "generate the same random numbers every time. [default 0].")
+        .SetDefault(0);
   }
 };
 

paddle/fluid/operators/randint_op.cu

@@ -19,25 +19,6 @@
 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:
@@ -54,17 +35,34 @@ class GPURandintKernel : public framework::OpKernel<T> {
       }
     }
 
+    platform::CPUPlace cpu;
+    auto dtype = static_cast<framework::proto::VarType::Type>(
+        context.Attr<int>("dtype"));
     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;
+    framework::LoDTensor tensor;
+    tensor.Resize(out->dims());
+    tensor.mutable_data(cpu, dtype);
+    T* data = tensor.mutable_data<T>(cpu);
 
-    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));
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
+    std::minstd_rand engine;
+    if (seed == 0) {
+      std::random_device rd;
+      seed = rd();
+    }
+    engine.seed(seed);
+    std::uniform_int_distribution<> dist(context.Attr<int>("low"),
+                                         context.Attr<int>("high") - 1);
+    for (int64_t i = 0; i < size; ++i) data[i] = dist(engine);
+
+    if (platform::is_gpu_place(context.GetPlace())) {
+      // Copy tensor to out
+      framework::TensorCopy(tensor, context.GetPlace(), out);
+    }
   }
 };
 

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

@@ -26,7 +26,7 @@ import paddle
 
 
 def output_hist(out):
-    hist, _ = np.histogram(out, range=(-5, 10))
+    hist, _ = np.histogram(out, range=(-10, 10))
     hist = hist.astype("float32")
     hist /= float(out.size)
     prob = 0.1 * np.ones((10))
@@ -41,7 +41,7 @@ class TestRandintOp(OpTest):
         self.outputs = {"Out": np.zeros((10000, 784)).astype("float32")}
 
     def init_attrs(self):
-        self.attrs = {"shape": [10000, 784], "low": -5, "high": 10}
+        self.attrs = {"shape": [10000, 784], "low": -10, "high": 10, "seed": 10}
         self.output_hist = output_hist
 
     def test_check_output(self):
@@ -51,7 +51,7 @@ class TestRandintOp(OpTest):
         hist, prob = self.output_hist(np.array(outs[0]))
         self.assertTrue(
             np.allclose(
-                hist, prob, rtol=0, atol=0.1), "hist: " + str(hist))
+                hist, prob, rtol=0, atol=0.001), "hist: " + str(hist))
 
 
 class TestRandintOpError(unittest.TestCase):
@@ -90,7 +90,7 @@ class TestRandintOp_attr_tensorlist(OpTest):
         self.outputs = {"Out": np.zeros((10000, 784)).astype("int32")}
 
     def init_attrs(self):
-        self.attrs = {"low": -5, "high": 10}
+        self.attrs = {"low": -10, "high": 10, "seed": 10}
         self.output_hist = output_hist
 
     def test_check_output(self):
@@ -100,7 +100,7 @@ class TestRandintOp_attr_tensorlist(OpTest):
         hist, prob = self.output_hist(np.array(outs[0]))
         self.assertTrue(
             np.allclose(
-                hist, prob, rtol=0, atol=0.1), "hist: " + str(hist))
+                hist, prob, rtol=0, atol=0.001), "hist: " + str(hist))
 
 
 class TestRandint_attr_tensor(OpTest):
@@ -111,7 +111,7 @@ class TestRandint_attr_tensor(OpTest):
         self.outputs = {"Out": np.zeros((10000, 784)).astype("int64")}
 
     def init_attrs(self):
-        self.attrs = {"low": -5, "high": 10}
+        self.attrs = {"low": -10, "high": 10, "seed": 10}
         self.output_hist = output_hist
 
     def test_check_output(self):
@@ -121,7 +121,7 @@ class TestRandint_attr_tensor(OpTest):
         hist, prob = self.output_hist(np.array(outs[0]))
         self.assertTrue(
             np.allclose(
-                hist, prob, rtol=0, atol=0.1), "hist: " + str(hist))
+                hist, prob, rtol=0, atol=0.001), "hist: " + str(hist))
 
 
 # Test python API

python/paddle/tensor/random.py

@@ -41,6 +41,7 @@ def randint(low,
             dtype=None,
             device=None,
             stop_gradient=False,
+            seed=0,
             name=None):
     """
     This function returns a Tensor filled with random integers from the "discrete uniform" distribution of the
@@ -66,6 +67,10 @@ def randint(low,
             on the GPU or CPU.
         stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
             default value is False.
+        seed (int, optional): Random seed used for permute samples. If seed is 
+            equal to 0, it means use a seed generated by the system. Note that 
+            if seed is not 0, this operator will always generate the same random 
+            permutation every time. Default: 0.
         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`.
 
@@ -162,6 +167,7 @@ def randint(low,
         low = 0
     attrs['low'] = low
     attrs['high'] = high
+    attrs['seed'] = seed
     if (low >= high):
         raise ValueError(
             "randint's low must less then high, but received low = {0}, "