[OpAttr]min/max of uniform_random support Tensor type (#45417)

* [OpAttr]min/max of Uniform_rand support Tensor type

* fix typo

paddle/fluid/operators/uniform_random_op.cc

@@ -182,9 +182,11 @@ uniform distribution. The random result is in set [min, max).
     AddAttr<std::vector<int64_t>>("shape", "The shape of the output tensor")
         .SetDefault({});
     AddAttr<float>("min", "Minimum value of uniform random. [default -1.0].")
-        .SetDefault(-1.0f);
+        .SetDefault(-1.0f)
+        .SupportTensor();
     AddAttr<float>("max", "Maximun value of uniform random. [default 1.0].")
-        .SetDefault(1.0f);
+        .SetDefault(1.0f)
+        .SupportTensor();
     AddAttr<int>("seed",
                  "Random seed used for generating samples. "
                  "0 means use a seed generated by the system."

paddle/phi/api/yaml/legacy_api.yaml

@@ -2754,11 +2754,11 @@
   backward : unfold_grad
 
 - api : uniform_random
-  args : (IntArray shape,  DataType dtype,  float min,  float max,  int seed, Place place={})
+  args : (IntArray shape,  DataType dtype,  Scalar min,  Scalar max,  int seed, Place place={})
   output : Tensor(out)
   infer_meta :
     func : UniformRandomInferMeta
-    param: [shape, dtype, min, max, seed]
+    param: [shape, dtype]
   kernel :
     func : uniform_random
     param: [shape, dtype, min, max, seed]

paddle/phi/infermeta/nullary.cc

@@ -79,9 +79,6 @@ void RandpermInferMeta(int n, DataType dtype, MetaTensor* out) {
 
 void UniformRandomInferMeta(const IntArray& shape,
                             DataType dtype,
-                            float min,
-                            float max,
-                            int seed,
                             MetaTensor* out) {
   auto out_dims = phi::make_ddim(shape.GetData());
   out->set_dims(out_dims);

paddle/phi/infermeta/nullary.h

@@ -67,9 +67,6 @@ void TruncatedGaussianRandomInferMeta(const std::vector<int>& shape,
 
 void UniformRandomInferMeta(const IntArray& shape,
                             DataType dtype,
-                            float min,
-                            float max,
-                            int seed,
                             MetaTensor* out);
 
 void TrilIndicesInferMeta(

paddle/phi/kernels/cpu/uniform_random_kernel.cc

@@ -47,8 +47,8 @@ template <typename T, typename Context>
 void UniformRandomRawKernel(const Context &dev_ctx,
                             const IntArray &shape,
                             DataType dtype,
-                            float min,
-                            float max,
+                            const Scalar &min,
+                            const Scalar &max,
                             int seed,
                             int diag_num,
                             int diag_step,
@@ -64,7 +64,8 @@ void UniformRandomRawKernel(const Context &dev_ctx,
   } else {
     engine = dev_ctx.GetGenerator()->GetCPUEngine();
   }
-  UniformRealDistribution<T>(data, size, min, max, engine);
+  UniformRealDistribution<T>(
+      data, size, min.to<float>(), max.to<float>(), engine);
   if (diag_num > 0) {
     PADDLE_ENFORCE_GT(
         size,
@@ -88,8 +89,8 @@ template <typename T, typename Context>
 void UniformRandomKernel(const Context &dev_ctx,
                          const IntArray &shape,
                          DataType dtype,
-                         float min,
-                         float max,
+                         const Scalar &min,
+                         const Scalar &max,
                          int seed,
                          DenseTensor *out) {
   UniformRandomRawKernel<T>(

paddle/phi/kernels/gpu/uniform_random_kernel.cu

@@ -57,8 +57,8 @@ template <typename T, typename Context>
 void UniformRandomRawKernel(const Context& dev_ctx,
                             const IntArray& shape,
                             DataType dtype,
-                            float min,
-                            float max,
+                            const Scalar& min,
+                            const Scalar& max,
                             int seed,
                             int diag_num,
                             int diag_step,
@@ -70,12 +70,12 @@ void UniformRandomRawKernel(const Context& dev_ctx,
     // Use global Generator seed
     using MT = typename kps::details::MPTypeTrait<T>::Type;
     funcs::uniform_distribution<MT> dist;
-    funcs::uniform_real_transform<MT> trans(min, max);
+    funcs::uniform_real_transform<MT> trans(min.to<float>(), max.to<float>());
     funcs::distribution_and_transform<T>(dev_ctx, out, dist, trans);
   } else {
     // Use OP seed
-    auto func =
-        UniformGenerator<T>(min, max, seed, diag_num, diag_step, diag_val);
+    auto func = UniformGenerator<T>(
+        min.to<float>(), max.to<float>(), seed, diag_num, diag_step, diag_val);
     IndexKernel<T, UniformGenerator<T>>(dev_ctx, out, func);
   }
 }
@@ -84,8 +84,8 @@ template <typename T, typename Context>
 void UniformRandomKernel(const Context& dev_ctx,
                          const IntArray& shape,
                          DataType dtype,
-                         float min,
-                         float max,
+                         const Scalar& min,
+                         const Scalar& max,
                          int seed,
                          DenseTensor* out) {
   UniformRandomRawKernel<T>(

paddle/phi/kernels/selected_rows/uniform_random_kernel.cc

@@ -26,8 +26,8 @@ template <typename T, typename Context>
 void UniformRandomRawKernel(const Context& dev_ctx,
                             const IntArray& shape,
                             DataType dtype,
-                            float min,
-                            float max,
+                            const Scalar& min,
+                            const Scalar& max,
                             int seed,
                             int diag_num,
                             int diag_step,
@@ -49,8 +49,8 @@ template <typename T, typename Context>
 void UniformRandomKernel(const Context& dev_ctx,
                          const IntArray& shape,
                          DataType dtype,
-                         float min,
-                         float max,
+                         const Scalar& min,
+                         const Scalar& max,
                          int seed,
                          SelectedRows* out) {
   phi::UniformRandomKernel<T>(

paddle/phi/kernels/selected_rows/uniform_random_kernel.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include "paddle/phi/common/int_array.h"
+#include "paddle/phi/common/scalar.h"
 #include "paddle/phi/core/selected_rows.h"
 
 namespace phi {
@@ -24,8 +25,8 @@ template <typename T, typename Context>
 void UniformRandomRawKernel(const Context& dev_ctx,
                             const IntArray& shape,
                             DataType dtype,
-                            float min,
-                            float max,
+                            const Scalar& min,
+                            const Scalar& max,
                             int seed,
                             int diag_num,
                             int diag_step,
@@ -36,8 +37,8 @@ template <typename T, typename Context>
 void UniformRandomKernel(const Context& dev_ctx,
                          const IntArray& shape,
                          DataType dtype,
-                         float min,
-                         float max,
+                         const Scalar& min,
+                         const Scalar& max,
                          int seed,
                          SelectedRows* out);
 

paddle/phi/kernels/uniform_random_kernel.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include "paddle/phi/common/int_array.h"
+#include "paddle/phi/common/scalar.h"
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/device_context.h"
 
@@ -24,8 +25,8 @@ template <typename T, typename Context>
 void UniformRandomRawKernel(const Context& dev_ctx,
                             const IntArray& shape,
                             DataType dtype,
-                            float min,
-                            float max,
+                            const Scalar& min,
+                            const Scalar& max,
                             int seed,
                             int diag_num,
                             int diag_step,
@@ -36,8 +37,8 @@ template <typename T, typename Context>
 void UniformRandomKernel(const Context& dev_ctx,
                          const IntArray& shape,
                          DataType dtype,
-                         float min,
-                         float max,
+                         const Scalar& min,
+                         const Scalar& max,
                          int seed,
                          DenseTensor* out);
 

python/paddle/fluid/layers/nn.py

@@ -15767,6 +15767,8 @@ def uniform_random(shape,
     check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random/rand')
     check_dtype(dtype, 'dtype', ('float32', 'float64', 'uint16'),
                 'uniform_random/rand')
+    check_type(min, 'min', (float, int, Variable), 'uniform_random/rand')
+    check_type(max, 'max', (float, int, Variable), 'uniform_random/rand')
 
     inputs = dict()
     attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}

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

@@ -28,6 +28,8 @@ import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
 from paddle.fluid.framework import _test_eager_guard
 
+from test_attribute_var import UnittestBase
+
 
 def output_hist(out):
     hist, _ = np.histogram(out, range=(-5, 10))
@@ -648,5 +650,49 @@ class TestRandomValue(unittest.TestCase):
         paddle.enable_static()
 
 
+class TestUniformMinMaxTensor(UnittestBase):
+
+    def init_info(self):
+        self.shapes = [[2, 3, 4]]
+        self.save_path = os.path.join(self.temp_dir.name, self.path_prefix())
+
+    def test_static(self):
+        main_prog = Program()
+        starup_prog = Program()
+        with program_guard(main_prog, starup_prog):
+            fc = paddle.nn.Linear(4, 10)
+            x = paddle.randn([2, 3, 4])
+            x.stop_gradient = False
+            feat = fc(x)  # [2,3,10]
+            min_v = paddle.to_tensor([0.1])
+            max_v = paddle.to_tensor([0.9])
+            y = paddle.uniform([2, 3, 10], min=min_v, max=max_v)
+            z = paddle.fluid.layers.uniform_random([2, 3, 10],
+                                                   min=min_v,
+                                                   max=max_v)
+
+            out = feat + y + z
+
+            sgd = paddle.optimizer.SGD()
+            sgd.minimize(paddle.mean(out))
+            self.assertTrue(self.var_prefix() in str(main_prog))
+
+            exe = paddle.static.Executor()
+            exe.run(starup_prog)
+            res = exe.run(fetch_list=[out])
+            np.testing.assert_array_equal(res[0].shape, [2, 3, 10])
+
+            paddle.static.save_inference_model(self.save_path, [x], [out], exe)
+            # Test for Inference Predictor
+            infer_out = self.infer_prog()
+            np.testing.assert_array_equal(res[0].shape, [2, 3, 10])
+
+    def path_prefix(self):
+        return 'uniform_random'
+
+    def var_prefix(self):
+        return "Var["
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/tensor/random.py

@@ -568,6 +568,8 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
 
     check_type(shape, 'shape', (list, tuple, Variable), 'uniform/rand')
     check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform/rand')
+    check_type(min, 'min', (float, int, Variable), 'uniform/rand')
+    check_type(max, 'max', (float, int, Variable), 'uniform/rand')
 
     inputs = dict()
     attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}