clean fluid task: transfer gaussian random api (#48529)

python/paddle/distribution/normal.py

@@ -21,7 +21,8 @@ import paddle
 from paddle.distribution import distribution
 from paddle.fluid.data_feeder import check_type, convert_dtype
 from paddle.fluid.framework import _non_static_mode
-from paddle.fluid.layers import nn, tensor
+from paddle.fluid.layers import tensor
+from paddle.tensor import random
 
 
 class Normal(distribution.Distribution):
@@ -180,8 +181,9 @@ class Normal(distribution.Distribution):
                 self.loc + self.scale, batch_shape + shape, self.dtype, 0.0
             )
             zero_tmp_reshape = paddle.reshape(zero_tmp, output_shape)
+
             zero_tmp_shape = paddle.shape(zero_tmp_reshape)
-            normal_random_tmp = nn.gaussian_random(
+            normal_random_tmp = random.gaussian(
                 zero_tmp_shape, mean=0.0, std=1.0, seed=seed, dtype=self.dtype
             )
             output = normal_random_tmp * (zero_tmp_reshape + self.scale)
@@ -189,7 +191,7 @@ class Normal(distribution.Distribution):
             return output
         else:
             output_shape = shape + batch_shape
-            output = nn.gaussian_random(
+            output = random.gaussian(
                 output_shape, mean=0.0, std=1.0, seed=seed, dtype=self.dtype
             ) * (tensor.zeros(output_shape, dtype=self.dtype) + self.scale)
             output = paddle.add(output, self.loc, name=name)

python/paddle/fluid/layers/nn.py

@@ -84,7 +84,6 @@ __all__ = [
     'elementwise_div',
     'elementwise_sub',
     'elementwise_mul',
-    'gaussian_random',
     'clip',
     'clip_by_norm',
     'mean',
@@ -2720,152 +2719,6 @@ def relu(x, name=None):
 from paddle.fluid.framework import convert_np_dtype_to_dtype_
 
 
-@deprecated(since="2.0.0", update_to="paddle.normal")
-@templatedoc()
-def gaussian_random(
-    shape, mean=0.0, std=1.0, seed=0, dtype='float32', name=None
-):
-    """
-    This OP returns a Tensor filled with random values sampled from a Gaussian
-    distribution, with ``shape`` and ``dtype``.
-
-    Args:
-        shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
-            is a list or tuple, the elements of it should be integers or Tensors
-            (with the shape [1], and the data type int32 or int64). If ``shape``
-            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
-            int64).
-        mean(float|int, optional): Mean of the output tensor, default is 0.0.
-        std(float|int, optional): Standard deviation of the output tensor, default
-            is 1.0.
-        seed(int, optional): ${seed_comment}
-        dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of
-            the output Tensor. Supported data types: float32, float64.
-            Default is float32.
-        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:
-        Tensor: A Tensor filled with random values sampled from a Gaussian
-        distribution, with ``shape`` and ``dtype``.
-
-    Examples:
-       .. code-block:: python
-
-            import paddle
-            import paddle.fluid as fluid
-            paddle.enable_static()
-
-            # example 1:
-            # attr shape is a list which doesn't contain Tensor.
-            result_1 = fluid.layers.gaussian_random(shape=[3, 4])
-            # [[-0.31261674,  1.8736548,  -0.6274357,   0.96988016],
-            #  [-0.12294637,  0.9554768,   1.5690808,  -1.2894802 ],
-            #  [-0.60082096, -0.61138713,  1.5345167,  -0.21834975]]
-
-            # example 2:
-            # attr shape is a list which contains Tensor.
-            dim_1 = fluid.layers.fill_constant([1], "int64", 2)
-            dim_2 = fluid.layers.fill_constant([1], "int32", 3)
-            result_2 = fluid.layers.gaussian_random(shape=[dim_1, dim_2])
-            # [[ 0.51398206, -0.3389769,   0.23597084],
-            #  [ 1.0388143,  -1.2015356,  -1.0499583 ]]
-
-            # example 3:
-            # attr shape is a Tensor, the data type must be int64 or int32.
-            var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
-            result_3 = fluid.layers.gaussian_random(var_shape)
-            # if var_shape's value is [2, 3]
-            # result_3 is:
-            # [[-0.12310527,  0.8187662,   1.923219  ]
-            #  [ 0.70721835,  0.5210541,  -0.03214082]]
-
-       .. code-block:: python
-
-           # declarative mode
-           # required: skiptest
-           import numpy as np
-           from paddle import fluid
-
-           x = fluid.layers.gaussian_random((2, 3), std=2., seed=10)
-
-           place = fluid.CPUPlace()
-           exe = fluid.Executor(place)
-           start = fluid.default_startup_program()
-           main = fluid.default_main_program()
-
-           exe.run(start)
-           x_np, = exe.run(main, feed={}, fetch_list=[x])
-
-           x_np
-           # array([[2.3060477, 2.676496 , 3.9911983],
-           #        [0.9990833, 2.8675377, 2.2279181]], dtype=float32)
-
-       .. code-block:: python
-
-           # imperative mode
-           import numpy as np
-           from paddle import fluid
-           import paddle.fluid.dygraph as dg
-
-           place = fluid.CPUPlace()
-           with dg.guard(place) as g:
-               x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10)
-               x_np = x.numpy()
-           x_np
-           # array([[2.3060477 , 2.676496  , 3.9911983 , 0.9990833 ],
-           #        [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
-    """
-    if not isinstance(dtype, core.VarDesc.VarType):
-        dtype = convert_np_dtype_to_dtype_(dtype)
-
-    if in_dygraph_mode():
-        shape = utils.convert_shape_to_list(shape)
-        place = _current_expected_place()
-        return _C_ops.gaussian(
-            shape, float(mean), float(std), seed, dtype, place
-        )
-
-    if _in_legacy_dygraph():
-        shape = utils.convert_shape_to_list(shape)
-        return _legacy_C_ops.gaussian_random(
-            'shape',
-            shape,
-            'mean',
-            float(mean),
-            'std',
-            float(std),
-            'seed',
-            seed,
-            'dtype',
-            dtype,
-        )
-
-    check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random/randn')
-    check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random/randn')
-
-    inputs = {}
-    attrs = {
-        'mean': mean,
-        'std': std,
-        'seed': seed,
-        'dtype': dtype,
-        'use_mkldnn': False,
-    }
-    utils.get_shape_tensor_inputs(
-        inputs=inputs, attrs=attrs, shape=shape, op_type='gaussian_random/randn'
-    )
-
-    helper = LayerHelper('gaussian_random', **locals())
-    out = helper.create_variable_for_type_inference(dtype)
-    helper.append_op(
-        type='gaussian_random', inputs=inputs, outputs={'Out': out}, attrs=attrs
-    )
-
-    return out
-
-
 def _elementwise_op(helper):
     op_type = helper.layer_type
     x = helper.kwargs.get('x', None)

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

@@ -21,6 +21,7 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.tests.unittests.op_test import OpTest, convert_uint16_to_float
+from paddle.tensor import random
 
 
 class TestGaussianRandomOp(OpTest):
@@ -228,11 +229,11 @@ class TestGaussianRandomAPI(unittest.TestCase):
             name="shape_tensor_int64", shape=[2], dtype="int64"
         )
 
-        out_1 = fluid.layers.gaussian_random(
+        out_1 = random.gaussian(
             shape=[2000, 500], dtype="float32", mean=0.0, std=1.0, seed=10
         )
 
-        out_2 = fluid.layers.gaussian_random(
+        out_2 = random.gaussian(
             shape=[2000, positive_2_int32],
             dtype="float32",
             mean=0.0,
@@ -240,7 +241,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_3 = fluid.layers.gaussian_random(
+        out_3 = random.gaussian(
             shape=[2000, positive_2_int64],
             dtype="float32",
             mean=0.0,
@@ -248,7 +249,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_4 = fluid.layers.gaussian_random(
+        out_4 = random.gaussian(
             shape=shape_tensor_int32,
             dtype="float32",
             mean=0.0,
@@ -256,7 +257,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_5 = fluid.layers.gaussian_random(
+        out_5 = random.gaussian(
             shape=shape_tensor_int64,
             dtype="float32",
             mean=0.0,
@@ -264,7 +265,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_6 = fluid.layers.gaussian_random(
+        out_6 = random.gaussian(
             shape=shape_tensor_int64,
             dtype=np.float32,
             mean=0.0,

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

@@ -19,6 +19,7 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.framework import _test_eager_guard
+from paddle.tensor import random
 
 
 class AutoPruneLayer0(fluid.Layer):
@@ -487,7 +488,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
 
     def func_case4_with_no_grad_op_maker(self):
         with fluid.dygraph.guard():
-            out = fluid.layers.gaussian_random(shape=[20, 30])
+            out = random.gaussian(shape=[20, 30])
             loss = paddle.mean(out)
             loss.backward()
             self.assertIsNone(out._grad_ivar())

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

@@ -2557,7 +2557,7 @@ class TestBook(LayerTest):
         with program_guard(
             fluid.default_main_program(), fluid.default_startup_program()
         ):
-            out = layers.gaussian_random(shape=[20, 30])
+            out = random.gaussian(shape=[20, 30])
             return out
 
     def make_sum(self):

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

@@ -18,6 +18,7 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
+from paddle.tensor import random
 
 
 class TestManualSeed(unittest.TestCase):
@@ -25,13 +26,13 @@ class TestManualSeed(unittest.TestCase):
         fluid.enable_dygraph()
 
         gen = paddle.seed(12312321111)
-        x = fluid.layers.gaussian_random([10], dtype="float32")
+        x = random.gaussian([10], dtype="float32")
         st1 = gen.get_state()
-        x1 = fluid.layers.gaussian_random([10], dtype="float32")
+        x1 = random.gaussian([10], dtype="float32")
         gen.set_state(st1)
-        x2 = fluid.layers.gaussian_random([10], dtype="float32")
+        x2 = random.gaussian([10], dtype="float32")
         gen.manual_seed(12312321111)
-        x3 = fluid.layers.gaussian_random([10], dtype="float32")
+        x3 = random.gaussian([10], dtype="float32")
         x_np = x.numpy()
         x1_np = x1.numpy()
         x2_np = x2.numpy()

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

@@ -21,6 +21,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.generator as generator
+from paddle.tensor import random
 
 
 class TestGeneratorSeed(unittest.TestCase):
@@ -148,13 +149,13 @@ class TestGeneratorSeed(unittest.TestCase):
         fluid.enable_dygraph()
 
         gen = paddle.seed(12312321111)
-        x = fluid.layers.gaussian_random([10], dtype="float32")
+        x = random.gaussian([10], dtype="float32")
         st1 = gen.get_state()
-        x1 = fluid.layers.gaussian_random([10], dtype="float32")
+        x1 = random.gaussian([10], dtype="float32")
         gen.set_state(st1)
-        x2 = fluid.layers.gaussian_random([10], dtype="float32")
+        x2 = random.gaussian([10], dtype="float32")
         gen.manual_seed(12312321111)
-        x3 = fluid.layers.gaussian_random([10], dtype="float32")
+        x3 = random.gaussian([10], dtype="float32")
         x_np = x.numpy()
         x1_np = x1.numpy()
         x2_np = x2.numpy()
@@ -175,8 +176,8 @@ class TestGeneratorSeed(unittest.TestCase):
         with fluid.program_guard(train_program, startup_program):
             # example 1:
             # attr shape is a list which doesn't contain tensor Variable.
-            result_1 = fluid.layers.gaussian_random(shape=[3, 4])
-            result_2 = fluid.layers.gaussian_random(shape=[3, 4])
+            result_1 = random.gaussian(shape=[3, 4])
+            result_2 = random.gaussian(shape=[3, 4])
 
             exe = fluid.Executor(fluid.CPUPlace())
             exe.run(startup_program)

python/paddle/fluid/tests/unittests/xpu/test_gaussian_random_op_xpu.py

@@ -29,6 +29,7 @@ import paddle
 import paddle.fluid as fluid
 
 paddle.enable_static()
+from paddle.tensor import random
 
 
 class XPUTestGaussianRandomOp(XPUOpTestWrapper):
@@ -192,11 +193,11 @@ class TestGaussianRandomAPI(unittest.TestCase):
             name="shape_tensor_int64", shape=[2], dtype="int64"
         )
 
-        out_1 = fluid.layers.gaussian_random(
+        out_1 = random.gaussian(
             shape=[2000, 500], dtype="float32", mean=0.0, std=1.0, seed=10
         )
 
-        out_2 = fluid.layers.gaussian_random(
+        out_2 = random.gaussian(
             shape=[2000, positive_2_int32],
             dtype="float32",
             mean=0.0,
@@ -204,7 +205,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_3 = fluid.layers.gaussian_random(
+        out_3 = random.gaussian(
             shape=[2000, positive_2_int64],
             dtype="float32",
             mean=0.0,
@@ -212,7 +213,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_4 = fluid.layers.gaussian_random(
+        out_4 = random.gaussian(
             shape=shape_tensor_int32,
             dtype="float32",
             mean=0.0,
@@ -220,7 +221,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_5 = fluid.layers.gaussian_random(
+        out_5 = random.gaussian(
             shape=shape_tensor_int64,
             dtype="float32",
             mean=0.0,
@@ -228,7 +229,7 @@ class TestGaussianRandomAPI(unittest.TestCase):
             seed=10,
         )
 
-        out_6 = fluid.layers.gaussian_random(
+        out_6 = random.gaussian(
             shape=shape_tensor_int64,
             dtype=np.float32,
             mean=0.0,

python/paddle/tensor/random.py

@@ -314,7 +314,7 @@ def uniform_random_batch_size_like(
     return out
 
 
-def gaussian(shape, mean=0.0, std=1.0, dtype=None, name=None):
+def gaussian(shape, mean=0.0, std=1.0, seed=0, dtype=None, name=None):
     """
     Returns a Tensor filled with random values sampled from a Gaussian
     distribution, with ``shape`` and ``dtype``.
@@ -338,7 +338,6 @@ def gaussian(shape, mean=0.0, std=1.0, dtype=None, name=None):
         distribution, with ``shape`` and ``dtype``.
     """
     op_type_for_check = 'gaussian/standard_normal/randn/normal'
-    seed = 0
 
     if dtype is None:
         dtype = paddle.framework.get_default_dtype()