[Yaml] add yaml for Uniform random and add unit test. (#41517) (#41619)

* gather op

* add mod

* [Yaml] final state for uniform and uniform_random

Author: 2742195759

paddle/fluid/operators/uniform_random_op.cc

@@ -16,9 +16,11 @@ limitations under the License. */
 #include <string>
 
 #include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/platform/bfloat16.h"
+#include "paddle/phi/infermeta/nullary.h"
 
 namespace paddle {
 namespace operators {
@@ -122,74 +124,6 @@ class UniformRandomOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
-    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "UniformRandomOp");
-
-    PADDLE_ENFORCE_LT(
-        ctx->Attrs().Get<float>("min"), ctx->Attrs().Get<float>("max"),
-        platform::errors::InvalidArgument(
-            "The uniform_random's min must less then max. But received min = "
-            "%f great than or equal max = %f.",
-            ctx->Attrs().Get<float>("min"), ctx->Attrs().Get<float>("max")));
-    PADDLE_ENFORCE_GE(ctx->Attrs().Get<int>("diag_num"), 0,
-                      platform::errors::InvalidArgument(
-                          "The uniform_random's diag_num must greater than or "
-                          "equal 0. But recevied diag_num (%d) < 0.",
-                          ctx->Attrs().Get<int>("diag_num")));
-    PADDLE_ENFORCE_GE(ctx->Attrs().Get<int>("diag_step"), 0,
-                      platform::errors::InvalidArgument(
-                          "The uniform_random's diag_step must greater than or "
-                          "equal 0. But recevied diag_step (%d) < 0.",
-                          ctx->Attrs().Get<int>("diag_step")));
-
-    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(uniform_random) can't be zero."
-                            "Please check the Attr(shape)'s size of"
-                            "Op(fluid.layers.uniform_random).)"));
-      auto out_dims = std::vector<int>(inputs_name.size(), -1);
-      ctx->SetOutputDim("Out", phi::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(uniform_random) must be 1."
-              "But received ShapeTensor's dimensions = %d, shape = [%s]",
-              shape_dims.size(), shape_dims));
-      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 = phi::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", phi::make_ddim(tensor_shape));
-  }
-
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
@@ -274,12 +208,16 @@ class UniformRandomOpVarTypeInference : public framework::VarTypeInference {
 }  // namespace operators
 }  // namespace paddle
 
+DECLARE_INFER_SHAPE_FUNCTOR(uniform_random, UniformRandomInferShapeFunctor,
+                            PD_INFER_META(phi::UniformRandomInferMeta));
+
 REGISTER_OPERATOR(
     uniform_random, paddle::operators::UniformRandomOp,
     paddle::operators::UniformRandomOpMaker,
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
     paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    paddle::operators::UniformRandomOpVarTypeInference);
+    paddle::operators::UniformRandomOpVarTypeInference,
+    UniformRandomInferShapeFunctor);
 
 REGISTER_OP_CPU_KERNEL(
     uniform_random_batch_size_like,

paddle/phi/infermeta/nullary.cc

@@ -63,6 +63,18 @@ void RandpermInferMeta(int n, DataType dtype, MetaTensor* out) {
   out->set_dtype(dtype);
 }
 
+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);
+  out->set_dtype(dtype);
+  out->set_layout(DataLayout::NCHW);
+}
+
 void RandintInferMeta(
     int low, int high, const IntArray& shape, DataType dtype, MetaTensor* out) {
   PADDLE_ENFORCE_NOT_NULL(

paddle/phi/infermeta/nullary.h

@@ -65,4 +65,11 @@ void TruncatedGaussianRandomInferMeta(const std::vector<int>& shape,
                                       DataType dtype,
                                       MetaTensor* out);
 
+void UniformRandomInferMeta(const IntArray& shape,
+                            DataType dtype,
+                            float min,
+                            float max,
+                            int seed,
+                            MetaTensor* out);
+
 }  // namespace phi

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

@@ -26,6 +26,7 @@
 from paddle.fluid.op import Operator
 import paddle.fluid as fluid
 from paddle.fluid import Program, program_guard
+from paddle.fluid.framework import _test_eager_guard
 
 
 def output_hist(out):
@@ -52,6 +53,7 @@ def output_hist_diag(out):
 class TestUniformRandomOp_attr_tensorlist(OpTest):
     def setUp(self):
         self.op_type = "uniform_random"
+        self.python_api = paddle.uniform
         self.new_shape = (1000, 784)
         shape_tensor = []
         for index, ele in enumerate(self.new_shape):
@@ -84,6 +86,7 @@ def init_attrs(self):
 class TestUniformRandomOp_attr_tensorlist_int32(OpTest):
     def setUp(self):
         self.op_type = "uniform_random"
+        self.python_api = paddle.uniform
         self.new_shape = (1000, 784)
         shape_tensor = []
         for index, ele in enumerate(self.new_shape):
@@ -110,6 +113,7 @@ def verify_output(self, outs):
 class TestUniformRandomOp_attr_tensor(OpTest):
     def setUp(self):
         self.op_type = "uniform_random"
+        self.python_api = paddle.uniform
         self.inputs = {"ShapeTensor": np.array([1000, 784]).astype("int64")}
         self.init_attrs()
         self.outputs = {"Out": np.zeros((1000, 784)).astype("float32")}
@@ -131,6 +135,7 @@ def verify_output(self, outs):
 class TestUniformRandomOp_attr_tensor_int32(OpTest):
     def setUp(self):
         self.op_type = "uniform_random"
+        self.python_api = paddle.uniform
         self.inputs = {"ShapeTensor": np.array([1000, 784]).astype("int32")}
         self.init_attrs()
         self.outputs = {"Out": np.zeros((1000, 784)).astype("float32")}
@@ -152,6 +157,7 @@ def verify_output(self, outs):
 class TestUniformRandomOp(OpTest):
     def setUp(self):
         self.op_type = "uniform_random"
+        self.python_api = paddle.uniform
         self.inputs = {}
         self.init_attrs()
         self.outputs = {"Out": np.zeros((1000, 784)).astype("float32")}
@@ -174,6 +180,18 @@ def verify_output(self, outs):
             np.allclose(
                 hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
 
+    def test_check_api(self):
+        places = self._get_places()
+        for place in places:
+            with fluid.dygraph.base.guard(place=place):
+                out = self.python_api(self.attrs['shape'], 'float32',
+                                      self.attrs['min'], self.attrs['max'],
+                                      self.attrs['seed'])
+
+    def test_check_api_eager(self):
+        with _test_eager_guard():
+            self.test_check_api()
+
 
 class TestUniformRandomOpError(unittest.TestCase):
     def test_errors(self):

python/paddle/tensor/random.py

@@ -548,7 +548,14 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
     if not isinstance(dtype, core.VarDesc.VarType):
         dtype = convert_np_dtype_to_dtype_(dtype)
 
-    if paddle.in_dynamic_mode():
+    if in_dygraph_mode():
+        shape = utils.convert_shape_to_list(shape)
+        return _C_ops.final_state_uniform_random(shape, dtype,
+                                                 float(min),
+                                                 float(max), seed,
+                                                 _current_expected_place())
+
+    if _in_legacy_dygraph():
         shape = utils.convert_shape_to_list(shape)
         return _C_ops.uniform_random('shape', shape, 'min',
                                      float(min), 'max',

python/paddle/utils/code_gen/api.yaml

@@ -1975,6 +1975,18 @@
     func : unfold
   backward : unfold_grad
 
+- api : uniform_random
+  args : (IntArray shape,  DataType dtype,  float min,  float max,  int seed, Place place={})
+  output : Tensor(out)
+  infer_meta :
+    func : UniformRandomInferMeta
+    param: [shape, dtype, min, max, seed]
+  kernel :
+    func : uniform_random
+    param: [shape, dtype, min, max, seed]
+    data_type : dtype
+    backend : place
+
 # The `axis` argument of Python API paddle.unique is not vector
 - api : unique
   args : (Tensor x, bool return_index, bool return_inverse, bool return_counts, int[] axis, DataType dtype=DataType::INT64)