[Clean fluid] Clean fluid elementwise_min/pow/mod/floordiv, remove API (#48040)

* clean fluid elementwise_pow, remove API

* clean elem_pow doc

* clean elementwise_mod

* clean elementwise min, floordiv, mod

python/paddle/fluid/layers/distributions.py

@@ -672,7 +672,7 @@ class MultivariateNormalDiag(Distribution):
         one_diag = tensor.diag(
             tensor.ones(shape=[batch_shape[0]], dtype=self.loc.dtype)
         )
-        inv_diag = nn.elementwise_pow(value, (one_all - 2 * one_diag))
+        inv_diag = paddle.pow(value, (one_all - 2 * one_diag))
 
         return inv_diag
 

python/paddle/fluid/layers/nn.py

@@ -58,7 +58,6 @@ from ..data_feeder import (
     check_type,
     check_dtype,
 )
-import paddle
 from paddle.utils import deprecated
 from paddle import _C_ops, _legacy_C_ops
 
@@ -151,10 +150,6 @@ __all__ = [
     'elementwise_div',
     'elementwise_sub',
     'elementwise_mul',
-    'elementwise_min',
-    'elementwise_pow',
-    'elementwise_mod',
-    'elementwise_floordiv',
     'uniform_random_batch_size_like',
     'gaussian_random',
     'sampling_id',
@@ -12369,187 +12364,11 @@ def elementwise_mul(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_mul', **locals()))
 
 
-def elementwise_min(x, y, axis=-1, act=None, name=None):
-    """
-        :alias_main: paddle.elementwise_min
-            :alias: paddle.elementwise_min,paddle.tensor.elementwise_min,paddle.tensor.math.elementwise_min
-            :old_api: paddle.fluid.layers.elementwise_min
-
-    Examples:
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.array([2, 3, 4]).astype('float32'),
-                    "y": np.array([1, 5, 2]).astype('float32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[3], dtype='float32')
-            y = fluid.data(name="y", shape=[3], dtype='float32')
-            z = fluid.layers.elementwise_min(x, y)
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) #[1, 3, 2]
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.ones((2, 3, 4, 5)).astype('float32'),
-                    "y": np.zeros((3, 4)).astype('float32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
-            y = fluid.data(name="y", shape=[3,4], dtype='float32')
-            z = fluid.layers.elementwise_min(x, y, axis=1)
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value)#[[[[0., 0., 0., 0., 0.] .... [0., 0., 0., 0., 0.]]]]
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name='elementwise_min'
-        )
-
-    return _elementwise_op(LayerHelper('elementwise_min', **locals()))
-
-
-def elementwise_pow(x, y, axis=-1, act=None, name=None):
-    """
-
-    Examples:
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.array([2, 3, 4]).astype('float32'),
-                    "y": np.array([1, 5, 2]).astype('float32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[3], dtype='float32')
-            y = fluid.data(name="y", shape=[3], dtype='float32')
-            z = fluid.layers.elementwise_pow(x, y)
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) #[2, 243, 16]
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name='elementwise_pow'
-        )
-    return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
-
-
-@deprecated(since="2.0.0", update_to="paddle.remainder")
-def elementwise_mod(x, y, axis=-1, act=None, name=None):
-    """
-
-    Examples:
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.array([10, 15, 8]).astype('int32'),
-                    "y": np.array([3, 6, 5]).astype('int32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[3], dtype='int32')
-            y = fluid.data(name="y", shape=[3], dtype='int32')
-            z = fluid.layers.elementwise_mod(x, y)
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) #[1, 3, 3]
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name='elementwise_mod'
-        )
-
-    return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
-
-
-@deprecated(since="2.0.0", update_to="paddle.floor_divide")
-def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
-    """
-
-    Examples:
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.array([10, 15, 8]).astype('int32'),
-                    "y": np.array([3, 7, 5]).astype('int32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[3], dtype='int32')
-            y = fluid.data(name="y", shape=[3], dtype='int32')
-            z = fluid.layers.elementwise_floordiv(x, y)
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) #[3, 2, 1]
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name='elementwise_floordiv'
-        )
-
-    return _elementwise_op(LayerHelper('elementwise_floordiv', **locals()))
-
-
 for func in [
     elementwise_add,
     elementwise_div,
     elementwise_sub,
     elementwise_mul,
-    elementwise_pow,
-    elementwise_min,
-    elementwise_mod,
-    elementwise_floordiv,
 ]:
     op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
 

python/paddle/fluid/layers/rnn.py

@@ -1317,10 +1317,8 @@ class BeamSearchDecoder(Decoder):
         scores = nn.reshape(scores, [-1, self.beam_size * self.vocab_size])
         # TODO: add grad for topk then this beam search can be used to train
         topk_scores, topk_indices = paddle.topk(x=scores, k=self.beam_size)
-        beam_indices = nn.elementwise_floordiv(
-            topk_indices, self.vocab_size_tensor
-        )
-        token_indices = nn.elementwise_mod(topk_indices, self.vocab_size_tensor)
+        beam_indices = paddle.floor_divide(topk_indices, self.vocab_size_tensor)
+        token_indices = paddle.remainder(topk_indices, self.vocab_size_tensor)
         next_log_probs = self._gather(
             nn.reshape(log_probs, [-1, self.beam_size * self.vocab_size]),
             topk_indices,

python/paddle/fluid/optimizer.py

@@ -4802,7 +4802,7 @@ class ExponentialMovingAverage:
         )
         global_step = layers.cast(global_step, "float32")
         decay_var = block._clone_variable(self._decay_var)
-        decay_pow_acc = layers.elementwise_pow(decay_var, global_step)
+        decay_pow_acc = paddle.pow(decay_var, global_step)
         return decay_pow_acc, global_step
 
     def _create_ema_vars(self, param):
@@ -7756,7 +7756,7 @@ class LookaheadOptimizer:
                 shape=[1], dtype='float32', value=1.0
             )
 
-            mod = layers.elementwise_mod(step, k)
+            mod = paddle.remainder(step, k)
             with layers.control_flow.Switch() as switch:
                 with switch.case(step == one_var):
                     for param_name in params:

python/paddle/fluid/tests/unittests/dygraph_to_static/seq2seq_dygraph_model.py

@@ -459,12 +459,8 @@ class BaseModel(fluid.dygraph.Layer):
                 input=scores, k=self.beam_size
             )
 
-            beam_indices = fluid.layers.elementwise_floordiv(
-                topk_indices, vocab_size_tensor
-            )
-            token_indices = fluid.layers.elementwise_mod(
-                topk_indices, vocab_size_tensor
-            )
+            beam_indices = paddle.floor_divide(topk_indices, vocab_size_tensor)
+            token_indices = paddle.remainder(topk_indices, vocab_size_tensor)
             next_log_probs = self._gather(scores, topk_indices, batch_pos)
 
             x = 0

python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py

@@ -856,12 +856,8 @@ class Transformer(Layer):
             topk_scores, topk_indices = fluid.layers.topk(
                 input=scores, k=beam_size
             )
-            beam_indices = fluid.layers.elementwise_floordiv(
-                topk_indices, vocab_size_tensor
-            )
-            token_indices = fluid.layers.elementwise_mod(
-                topk_indices, vocab_size_tensor
-            )
+            beam_indices = paddle.floor_divide(topk_indices, vocab_size_tensor)
+            token_indices = paddle.remainder(topk_indices, vocab_size_tensor)
 
             # update states
             caches = map_structure(

python/paddle/fluid/tests/unittests/ipu/test_elemetwise_x_op_ipu.py

@@ -150,7 +150,7 @@ class TestMax(TestMul):
 
 class TestPow(TestMul):
     def set_test_op(self):
-        self.op = paddle.fluid.layers.elementwise_pow
+        self.op = paddle.pow
 
 
 class TestMod(TestMul):
@@ -161,7 +161,7 @@ class TestMod(TestMul):
         self.rtol_fp16 = 1e-3
 
     def set_test_op(self):
-        self.op = paddle.fluid.layers.elementwise_mod
+        self.op = paddle.remainder
 
 
 if __name__ == "__main__":

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

@@ -379,7 +379,7 @@ class TestCondNestedControlFlow(unittest.TestCase):
                 lambda: fluid.layers.cond(
                     a == b,
                     lambda: fluid.layers.elementwise_sub(a, b),
-                    lambda: fluid.layers.elementwise_pow(a, b),
+                    lambda: paddle.pow(a, b),
                 ),
             )
             append_backward(out)

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

@@ -614,7 +614,7 @@ class TestLayer(LayerTest):
             t6 = layers.data(name='t6', shape=[3, 3], dtype='float32')
 
             ret = layers.elementwise_add(t, t2)
-            ret = layers.elementwise_pow(ret, t3)
+            ret = paddle.pow(ret, t3)
             ret = layers.elementwise_div(ret, t4)
             ret = layers.elementwise_sub(ret, t5)
             ret = layers.elementwise_mul(ret, t6)
@@ -627,14 +627,14 @@ class TestLayer(LayerTest):
         with self.dynamic_graph():
             with _test_eager_guard():
                 ret = layers.elementwise_add(to_variable(n), to_variable(n2))
-                ret = layers.elementwise_pow(ret, to_variable(n3))
+                ret = paddle.pow(ret, to_variable(n3))
                 ret = layers.elementwise_div(ret, to_variable(n4))
                 ret = layers.elementwise_sub(ret, to_variable(n5))
                 dy_eager_ret = layers.elementwise_mul(ret, to_variable(n6))
                 dy_eager_ret_value = dy_eager_ret.numpy()
 
             ret = layers.elementwise_add(to_variable(n), to_variable(n2))
-            ret = layers.elementwise_pow(ret, to_variable(n3))
+            ret = paddle.pow(ret, to_variable(n3))
             ret = layers.elementwise_div(ret, to_variable(n4))
             ret = layers.elementwise_sub(ret, to_variable(n5))
             dy_ret = layers.elementwise_mul(ret, to_variable(n6))

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

@@ -97,7 +97,7 @@ def static(
 
         id = fluid.data('id', [1], 'int32')
         two = layers.fill_constant([1], 'int32', 2)
-        mod_two = layers.elementwise_mod(id, two) == 0
+        mod_two = paddle.remainder(id, two) == 0
 
         if loss_in_switch:
             avg_loss = layers.case(