clean fluid task: transfer uniform_random_batch_size_like api (#48270)

python/paddle/distribution/uniform.py

@@ -15,6 +15,7 @@
 import numpy as np
 from paddle import _C_ops, _legacy_C_ops
 from paddle.distribution import distribution
+from paddle.tensor import random
 from paddle.fluid.data_feeder import check_type, convert_dtype
 from paddle.fluid.framework import (
     _non_static_mode,
@@ -167,7 +168,7 @@ class Uniform(distribution.Distribution):
             zero_tmp = tensor.fill_constant_batch_size_like(
                 self.low + self.high, batch_shape + shape, self.dtype, 0.0
             )
-            uniform_random_tmp = nn.uniform_random_batch_size_like(
+            uniform_random_tmp = random.uniform_random_batch_size_like(
                 zero_tmp,
                 zero_tmp.shape,
                 dtype=self.dtype,

python/paddle/fluid/layers/distributions.py

@@ -221,8 +221,10 @@ class Uniform(Distribution):
             zero_tmp = tensor.fill_constant_batch_size_like(
                 self.low + self.high, batch_shape + shape, self.low.dtype, 0.0
             )
-            uniform_random_tmp = nn.uniform_random_batch_size_like(
-                zero_tmp, zero_tmp.shape, min=0.0, max=1.0, seed=seed
+            uniform_random_tmp = (
+                paddle.tensor.random.uniform_random_batch_size_like(
+                    zero_tmp, zero_tmp.shape, min=0.0, max=1.0, seed=seed
+                )
             )
             output = (
                 uniform_random_tmp * (zero_tmp + self.high - self.low)

python/paddle/fluid/layers/nn.py

@@ -121,7 +121,6 @@ __all__ = [
     'elementwise_div',
     'elementwise_sub',
     'elementwise_mul',
-    'uniform_random_batch_size_like',
     'gaussian_random',
     'sampling_id',
     'gaussian_random_batch_size_like',
@@ -7182,115 +7181,6 @@ def flatten(x, axis=1, name=None):
 from paddle.fluid.framework import convert_np_dtype_to_dtype_
 
 
-@deprecated(since='1.8.0', update_to="paddle.uniform")
-@templatedoc()
-def uniform_random_batch_size_like(
-    input,
-    shape,
-    dtype='float32',
-    input_dim_idx=0,
-    output_dim_idx=0,
-    min=-1.0,
-    max=1.0,
-    seed=0,
-):
-    """
-    This OP initializes a variable with random values sampled from a
-    uniform distribution in the range [min, max). The input_dim_idx used to get the input dimension value which will be used to resize the output dimension.
-
-    .. code-block:: text
-
-        *Case 1:
-
-            Given:
-                input =[[0.946741  , 0.1357001 , 0.38086128]]    # input.shape=[1,3]
-                shape=[2,4]
-
-            result.shape[output_dim_idx] = input.shape[input_dim_idx],
-            output_dim_idx = 0,
-            input_dim_idx = 0,
-            result.shape[0] = input.shape[0],
-            then:
-                result=[[ 0.3443427 , -0.23056602,  0.3477049 ,  0.06139076]]    # result.shape=[1,4]
-
-       *Case 2:
-
-           Given:
-               input =[[0.946741  , 0.1357001 , 0.38086128]]     # input.shape=[1,3]
-               shape=[2,4]
-               input_dim_idx=1
-               output_dim_idx=1
-
-           result.shape[output_dim_idx] = input.shape[input_dim_idx],
-           output_dim_idx = 1,
-           input_dim_idx = 1,
-           result.shape[1] = input.shape[1],
-           then:
-               result=[[-0.23133647, -0.84195036,  0.21441269],
-                       [-0.08774924,  0.25605237, -0.09403259]]    # result.shape=[2,3]
-    Args:
-        input (Variable): A Tensor. Supported data types: float32, float64.
-        shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int.
-        input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default  0.
-        output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0.
-        min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
-        max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
-        seed (int, optional):  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.
-        dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output Tensor. Supported data types: float32, float64. Default float32.
-    Returns:
-        Variable: A Tensor of the specified shape filled with uniform_random values. The shape of the Tensor is determined by the shape parameter and the specified dimension of the input Tensor.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle
-            import paddle.fluid as fluid
-            paddle.enable_static()
-
-            # example 1:
-            input = fluid.data(name="input", shape=[1, 3], dtype='float32')
-            out_1 = fluid.layers.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4]
-
-            # example 2:
-            out_2 = fluid.layers.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3]
-
-
-    """
-    check_variable_and_dtype(
-        input,
-        'Input',
-        ("float32", 'float64', "uint16"),
-        'uniform_random_batch_size_like',
-    )
-    check_type(shape, 'shape', (list, tuple), 'uniform_random_batch_size_like')
-    check_dtype(
-        dtype,
-        'dtype',
-        ('float32', 'float64', "uint16"),
-        'uniform_random_batch_size_like',
-    )
-
-    helper = LayerHelper('uniform_random_batch_size_like', **locals())
-    out = helper.create_variable_for_type_inference(dtype)
-    c_dtype = convert_np_dtype_to_dtype_(dtype)
-    helper.append_op(
-        type='uniform_random_batch_size_like',
-        inputs={'Input': input},
-        outputs={'Out': out},
-        attrs={
-            'shape': shape,
-            'input_dim_idx': input_dim_idx,
-            'output_dim_idx': output_dim_idx,
-            'min': min,
-            'max': max,
-            'seed': seed,
-            'dtype': c_dtype,
-        },
-    )
-
-    return out
-
-
 @deprecated(since="2.0.0", update_to="paddle.normal")
 @templatedoc()
 def gaussian_random(

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

@@ -14,6 +14,7 @@
 
 import paddle
 import paddle.fluid as fluid
+from paddle.tensor import random
 import numpy as np
 import unittest
 from paddle import _legacy_C_ops
@@ -402,7 +403,7 @@ def calc_gradients(outputs, inputs, no_grad_set):
 def gradient_penalty(f, real, fake, no_grad_set, cfg):
     def _interpolate(a, b):
         shape = [a.shape[0]]
-        alpha = fluid.layers.uniform_random_batch_size_like(
+        alpha = random.uniform_random_batch_size_like(
             input=a, shape=shape, min=0.1, max=1.0, seed=cfg.seed
         )
 

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

@@ -33,6 +33,7 @@ from paddle.fluid.dygraph import nn
 from paddle.fluid.dygraph import base
 from paddle.fluid.dygraph import to_variable
 from paddle.fluid.framework import _test_eager_guard
+from paddle.tensor import random
 import paddle.nn.functional as F
 
 
@@ -3555,7 +3556,7 @@ class TestBook(LayerTest):
             input = self._get_data(
                 name="input", shape=[13, 11], dtype='float32'
             )
-            out = layers.uniform_random_batch_size_like(input, [-1, 11])
+            out = random.uniform_random_batch_size_like(input, [-1, 11])
             return out
 
     def make_gaussian_random(self):

python/paddle/fluid/tests/unittests/test_uniform_random_bf16_op.py

@@ -23,6 +23,7 @@ from paddle.fluid.tests.unittests.test_uniform_random_op import (
     output_hist,
     output_hist_diag,
 )
+from paddle.tensor import random
 
 
 class TestUniformRandomOpBF16(OpTest):
@@ -262,7 +263,7 @@ class TestUniformRandomBatchSizeLikeOpBF16API(unittest.TestCase):
         train_program = fluid.Program()
         with fluid.program_guard(train_program, startup_program):
             input = fluid.data(name="input", shape=[1, 3], dtype='uint16')
-            out_1 = fluid.layers.uniform_random_batch_size_like(
+            out_1 = random.uniform_random_batch_size_like(
                 input, [2, 4], dtype=np.uint16
             )  # out_1.shape=[1, 4]
 

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

@@ -25,6 +25,7 @@ from paddle.fluid import Program, program_guard
 from paddle.fluid.framework import _test_eager_guard
 
 from test_attribute_var import UnittestBase
+from paddle.tensor import random
 
 
 def output_hist(out):
@@ -481,7 +482,7 @@ class TestUniformRandomBatchSizeLikeOpError(unittest.TestCase):
                 x1 = fluid.create_lod_tensor(
                     np.zeros((100, 784)), [[10, 10, 10, 70]], fluid.CPUPlace()
                 )
-                fluid.layers.uniform_random_batch_size_like(x1)
+                random.uniform_random_batch_size_like(x1)
 
             self.assertRaises(TypeError, test_Variable)
 
@@ -489,7 +490,7 @@ class TestUniformRandomBatchSizeLikeOpError(unittest.TestCase):
                 x1 = fluid.layers.data(
                     name='x2', shape=[100, 784], dtype='float32'
                 )
-                fluid.layers.uniform_random_batch_size_like(x1, shape="shape")
+                random.uniform_random_batch_size_like(x1, shape="shape")
 
             self.assertRaises(TypeError, test_shape)
 
@@ -497,7 +498,7 @@ class TestUniformRandomBatchSizeLikeOpError(unittest.TestCase):
                 x2 = fluid.layers.data(
                     name='x2', shape=[100, 784], dtype='float32'
                 )
-                fluid.layers.uniform_random_batch_size_like(x2, 'int32')
+                random.uniform_random_batch_size_like(x2, 'int32')
 
             self.assertRaises(TypeError, test_dtype)
 

python/paddle/tensor/random.py

@@ -215,6 +215,101 @@ def multinomial(x, num_samples=1, replacement=False, name=None):
     return out
 
 
+def uniform_random_batch_size_like(
+    input,
+    shape,
+    dtype='float32',
+    input_dim_idx=0,
+    output_dim_idx=0,
+    min=-1.0,
+    max=1.0,
+    seed=0,
+):
+    """
+    This OP initializes a variable with random values sampled from a
+    uniform distribution in the range [min, max). The input_dim_idx used to get the input dimension value which will be used to resize the output dimension.
+    .. code-block:: text
+        *Case 1:
+            Given:
+                input =[[0.946741  , 0.1357001 , 0.38086128]]    # input.shape=[1,3]
+                shape=[2,4]
+            result.shape[output_dim_idx] = input.shape[input_dim_idx],
+            output_dim_idx = 0,
+            input_dim_idx = 0,
+            result.shape[0] = input.shape[0],
+            then:
+                result=[[ 0.3443427 , -0.23056602,  0.3477049 ,  0.06139076]]    # result.shape=[1,4]
+       *Case 2:
+           Given:
+               input =[[0.946741  , 0.1357001 , 0.38086128]]     # input.shape=[1,3]
+               shape=[2,4]
+               input_dim_idx=1
+               output_dim_idx=1
+           result.shape[output_dim_idx] = input.shape[input_dim_idx],
+           output_dim_idx = 1,
+           input_dim_idx = 1,
+           result.shape[1] = input.shape[1],
+           then:
+               result=[[-0.23133647, -0.84195036,  0.21441269],
+                       [-0.08774924,  0.25605237, -0.09403259]]    # result.shape=[2,3]
+    Args:
+        input (Variable): A Tensor. Supported data types: float32, float64.
+        shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int.
+        input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default  0.
+        output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0.
+        min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
+        max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
+        seed (int, optional):  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.
+        dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output Tensor. Supported data types: float32, float64. Default float32.
+    Returns:
+        Variable: A Tensor of the specified shape filled with uniform_random values. The shape of the Tensor is determined by the shape parameter and the specified dimension of the input Tensor.
+    Examples:
+        .. code-block:: python
+            import paddle
+            import paddle.fluid as fluid
+            from paddle.tensor import random
+            paddle.enable_static()
+            # example 1:
+            input = fluid.data(name="input", shape=[1, 3], dtype='float32')
+            out_1 = random.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4]
+            # example 2:
+            out_2 = random.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3]
+    """
+    check_variable_and_dtype(
+        input,
+        'Input',
+        ("float32", 'float64', "uint16"),
+        'uniform_random_batch_size_like',
+    )
+    check_type(shape, 'shape', (list, tuple), 'uniform_random_batch_size_like')
+    check_dtype(
+        dtype,
+        'dtype',
+        ('float32', 'float64', "uint16"),
+        'uniform_random_batch_size_like',
+    )
+
+    helper = LayerHelper('uniform_random_batch_size_like', **locals())
+    out = helper.create_variable_for_type_inference(dtype)
+    c_dtype = convert_np_dtype_to_dtype_(dtype)
+    helper.append_op(
+        type='uniform_random_batch_size_like',
+        inputs={'Input': input},
+        outputs={'Out': out},
+        attrs={
+            'shape': shape,
+            'input_dim_idx': input_dim_idx,
+            'output_dim_idx': output_dim_idx,
+            'min': min,
+            'max': max,
+            'seed': seed,
+            'dtype': c_dtype,
+        },
+    )
+
+    return out
+
+
 def gaussian(shape, mean=0.0, std=1.0, dtype=None, name=None):
     """
     Returns a Tensor filled with random values sampled from a Gaussian