[Clean fluid] Add inner function _elementwise_op_with_axis (#48748)

* add inner function _elementwise_op_with_axis

* fix transformer_model

* polish API code

* remove elementwise_div/mul api

* delete API in __all__

* delete elementwise_mul completely

* polish elementwise_mul call

* polish internal api

* resolve conflict, fix rnn.py

* use non-inplace call

* delete elementwise_mul api test

* delete elementwise_mul api test

* clean elementwise_add/sub

* restore _elementwise_op_in_dygraph in nn.py

python/paddle/fluid/contrib/layers/rnn_impl.py

@@ -367,9 +367,9 @@ def basic_gru(
                 new_hidden = unit_list[i](step_input, pre_hidden)
 
                 if mask:
-                    new_hidden = layers.elementwise_mul(
+                    new_hidden = paddle.tensor.math._multiply_with_axis(
                         new_hidden, step_mask, axis=0
-                    ) - layers.elementwise_mul(
+                    ) - paddle.tensor.math._multiply_with_axis(
                         pre_hidden, (step_mask - 1), axis=0
                     )
                 rnn.update_memory(pre_hidden, new_hidden)
@@ -661,14 +661,14 @@ def basic_lstm(
                 )
 
                 if mask:
-                    new_hidden = layers.elementwise_mul(
+                    new_hidden = paddle.tensor.math._multiply_with_axis(
                         new_hidden, step_mask, axis=0
-                    ) - layers.elementwise_mul(
+                    ) - paddle.tensor.math._multiply_with_axis(
                         pre_hidden, (step_mask - 1), axis=0
                     )
-                    new_cell = layers.elementwise_mul(
+                    new_cell = paddle.tensor.math._multiply_with_axis(
                         new_cell, step_mask, axis=0
-                    ) - layers.elementwise_mul(
+                    ) - paddle.tensor.math._multiply_with_axis(
                         pre_cell, (step_mask - 1), axis=0
                     )
 

python/paddle/fluid/layer_helper_base.py

@@ -115,8 +115,6 @@ class LayerHelperBase:
             )
 
     def _create_weight_normalize(self, attr, shape, dtype):
-        from .layers import elementwise_mul
-
         # Remove these ops when LayerHelper and layers support indicating
         # program and block.
         def __norm_op(
@@ -272,7 +270,7 @@ class LayerHelperBase:
             # Currently, elementwise_mul only support broadcast when the shape
             # of y is a subset of the shape of x. Thus, we reshape y to squeeze
             # to achieve the subset.
-            w = elementwise_mul(
+            w = paddle.tensor.math._multiply_with_axis(
                 x=v,
                 y=scale
                 if dim is None

python/paddle/fluid/layers/nn.py

@@ -79,10 +79,6 @@ __all__ = [
     'unsqueeze',
     'lod_reset',
     'relu',
-    'elementwise_add',
-    'elementwise_div',
-    'elementwise_sub',
-    'elementwise_mul',
     'clip',
     'clip_by_norm',
     'mul',
@@ -2464,512 +2460,6 @@ def relu(x, name=None):
     return out
 
 
-from paddle.fluid.framework import convert_np_dtype_to_dtype_
-
-
-def _elementwise_op(helper):
-    op_type = helper.layer_type
-    x = helper.kwargs.get('x', None)
-    y = helper.kwargs.get('y', None)
-
-    assert x is not None, 'x cannot be None in {}'.format(op_type)
-    assert y is not None, 'y cannot be None in {}'.format(op_type)
-    check_variable_and_dtype(
-        x,
-        'x',
-        ['float16', 'uint16', 'float32', 'float64', 'int32', 'int64'],
-        op_type,
-    )
-    check_variable_and_dtype(
-        y,
-        'y',
-        ['float16', 'uint16', 'float32', 'float64', 'int32', 'int64'],
-        op_type,
-    )
-
-    axis = helper.kwargs.get('axis', -1)
-    use_mkldnn = helper.kwargs.get('use_mkldnn', False)
-    name = helper.kwargs.get('name', None)
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-
-    helper.append_op(
-        type=op_type,
-        inputs={'X': x, 'Y': y},
-        outputs={'Out': out},
-        attrs={'axis': axis, 'use_mkldnn': use_mkldnn},
-    )
-    return helper.append_activation(out)
-
-
-def elementwise_add(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_add(x, y)
-            # z = x + y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # [3., 8., 6.]
-
-
-        .. 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_add(x, y, axis=1)
-            # z = x + y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # z.shape=[2,3,4,5]
-
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
-                    "y": np.random.randint(1, 5, size=[5]).astype('float32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
-            y = fluid.data(name="y", shape=[5], dtype='float32')
-            z = fluid.layers.elementwise_add(x, y, axis=3)
-            # z = x + y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-            print(z_value) # z.shape=[2,3,4,5]
-
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x,
-            y,
-            axis=axis,
-            act=act,
-            op_name='elementwise_add',
-            use_mkldnn=_global_flags()["FLAGS_use_mkldnn"],
-        )
-
-    return _elementwise_op(LayerHelper('elementwise_add', **locals()))
-
-
-@deprecated(since="2.0.0", update_to="paddle.divide")
-def elementwise_div(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_div(x, y)
-            # z = x / y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # [2., 0.6, 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_div(x, y, axis=1)
-            # z = x / y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # z.shape=[2,3,4,5]
-
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
-                    "y": np.random.randint(1, 5, size=[5]).astype('float32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
-            y = fluid.data(name="y", shape=[5], dtype='float32')
-            z = fluid.layers.elementwise_div(x, y, axis=3)
-            # z = x / y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-            print(z_value) # z.shape=[2,3,4,5]
-
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name='elementwise_div'
-        )
-
-    return _elementwise_op(LayerHelper('elementwise_div', **locals()))
-
-
-def elementwise_sub(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_sub(x, y)
-            # z = x - y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # [1., -2., 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_sub(x, y, axis=1)
-            # z = x - y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # z.shape=[2,3,4,5]
-
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
-                    "y": np.random.randint(1, 5, size=[5]).astype('float32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
-            y = fluid.data(name="y", shape=[5], dtype='float32')
-            z = fluid.layers.elementwise_sub(x, y, axis=3)
-            # z = x - y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-            print(z_value) # z.shape=[2,3,4,5]
-
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name='elementwise_sub'
-        )
-
-    return _elementwise_op(LayerHelper('elementwise_sub', **locals()))
-
-
-@deprecated(since="2.0.0", update_to="paddle.multiply")
-def elementwise_mul(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_mul(x, y)
-            # z = x * y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # [2., 15., 8.]
-
-
-        .. 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_mul(x, y, axis=1)
-            # z = x * y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-
-            print(z_value) # z.shape=[2,3,4,5]
-
-
-        ..  code-block:: python
-
-            import paddle.fluid as fluid
-            import numpy as np
-            import paddle
-
-            def gen_data():
-                return {
-                    "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
-                    "y": np.random.randint(1, 5, size=[5]).astype('float32')
-                }
-            paddle.enable_static()
-            x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
-            y = fluid.data(name="y", shape=[5], dtype='float32')
-            z = fluid.layers.elementwise_mul(x, y, axis=3)
-            # z = x * y
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-
-            z_value = exe.run(feed=gen_data(),
-                                fetch_list=[z.name])
-            print(z_value) # z.shape=[2,3,4,5]
-
-    """
-    if _non_static_mode():
-        return _elementwise_op_in_dygraph(
-            x, y, axis=axis, act=act, op_name='elementwise_mul'
-        )
-
-    return _elementwise_op(LayerHelper('elementwise_mul', **locals()))
-
-
-for func in [
-    elementwise_add,
-    elementwise_div,
-    elementwise_sub,
-    elementwise_mul,
-]:
-    op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
-
-    # insert the c++ doc string on top of python doc string
-    func.__doc__ = (
-        _generate_doc_string_(
-            op_proto,
-            additional_args_lines=[
-                "axis (int32, optional): If X.dimension != Y.dimension, \
-            Y.dimension must be a subsequence of x.dimension. \
-            And axis is the start dimension index for broadcasting Y onto X. ",
-                "act (string, optional): Activation applied to the output. \
-            Default is None. Details: :ref:`api_guide_activations_en` ",
-                "name (string, optional): Name of the output. \
-            Default is None. It's used to print debug info for developers. Details: \
-            :ref:`api_guide_Name` ",
-            ],
-            skip_attrs_set={
-                "x_data_format",
-                "y_data_format",
-                "axis",
-                "use_quantizer",
-                "mkldnn_data_type",
-                "Scale_x",
-                "Scale_y",
-                "Scale_out",
-            },
-        )
-        + """\n"""
-        + str(func.__doc__)
-    )
-
-    doc_list = func.__doc__.splitlines()
-
-    for idx, val in enumerate(doc_list):
-        if (
-            val.startswith("Warning: ")
-            and val.endswith(" instead.")
-            and "and will be removed in future versions." in val
-        ):
-            doc_list.insert(0, doc_list.pop(idx))
-            func.__doc__ = "\n" + "\n".join(i for i in doc_list)
-            break
-
-for func in []:
-    op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
-    func.__doc__ = _generate_doc_string_(
-        op_proto,
-        additional_args_lines=[
-            "act (basestring|None): Activation applied to the output.",
-            "name (basestring|None): Name of the output.",
-        ],
-    )
-    func.__doc__ = (
-        func.__doc__
-        + """
-
-Examples:
-  .. code-block:: python
-
-    import paddle.fluid as fluid
-    # example 1: shape(x) = (2, 3, 4, 5), shape(y) = (2, 3, 4, 5)
-    x0 = fluid.layers.data(name="x0", shape=[2, 3, 4, 5], dtype='float32')
-    y0 = fluid.layers.data(name="y0", shape=[2, 3, 4, 5], dtype='float32')
-    z0 = fluid.layers.%s(x0, y0)
-
-    # example 2: shape(X) = (2, 3, 4, 5), shape(Y) = (5)
-    x1 = fluid.layers.data(name="x1", shape=[2, 3, 4, 5], dtype='float32')
-    y1 = fluid.layers.data(name="y1", shape=[5], dtype='float32')
-    z1 = fluid.layers.%s(x1, y1)
-
-    # example 3: shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2
-    x2 = fluid.layers.data(name="x2", shape=[2, 3, 4, 5], dtype='float32')
-    y2 = fluid.layers.data(name="y2", shape=[4, 5], dtype='float32')
-    z2 = fluid.layers.%s(x2, y2, axis=2)
-
-    # example 4: shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-    x3 = fluid.layers.data(name="x3", shape=[2, 3, 4, 5], dtype='float32')
-    y3 = fluid.layers.data(name="y3", shape=[3, 4], dtype='float32')
-    z3 = fluid.layers.%s(x3, y3, axis=1)
-
-    # example 5: shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
-    x4 = fluid.layers.data(name="x4", shape=[2, 3, 4, 5], dtype='float32')
-    y4 = fluid.layers.data(name="y4", shape=[2], dtype='float32')
-    z4 = fluid.layers.%s(x4, y4, axis=0)
-
-    # example 6: shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0
-    x5 = fluid.layers.data(name="x5", shape=[2, 3, 4, 5], dtype='float32')
-    y5 = fluid.layers.data(name="y5", shape=[2], dtype='float32')
-    z5 = fluid.layers.%s(x5, y5, axis=0)
-    """
-        % (
-            func.__name__,
-            func.__name__,
-            func.__name__,
-            func.__name__,
-            func.__name__,
-            func.__name__,
-        )
-    )
-
-
 def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
     if _non_static_mode():
         op = getattr(_legacy_C_ops, op_name)

python/paddle/fluid/layers/rnn.py

@@ -547,9 +547,9 @@ class ArrayWrapper:
 
 def _maybe_copy(state, new_state, step_mask):
     """update rnn state or just pass the old state through"""
-    new_state = nn.elementwise_mul(
+    new_state = paddle.tensor.math._multiply_with_axis(
         new_state, step_mask, axis=0
-    ) + nn.elementwise_mul(state, (1 - step_mask), axis=0)
+    ) + paddle.tensor.math._multiply_with_axis(state, (1 - step_mask), axis=0)
     return new_state
 
 
@@ -833,9 +833,11 @@ def _dynamic_decode_imperative(
             # otherwise, renamed bool gradients of would be summed up leading
             # to sum(bool) error.
             step_mask.stop_gradient = True
-        new_state = nn.elementwise_mul(
+        new_state = paddle.tensor.math._multiply_with_axis(
             state, step_mask, axis=0
-        ) - nn.elementwise_mul(new_state, (step_mask - 1), axis=0)
+        ) - paddle.tensor.math._multiply_with_axis(
+            new_state, (step_mask - 1), axis=0
+        )
         if convert_dtype(state_dtype) in ["bool"]:
             new_state = tensor.cast(new_state, dtype=state_dtype)
         return new_state
@@ -988,9 +990,11 @@ def _dynamic_decode_declarative(
             # otherwise, renamed bool gradients of would be summed up leading
             # to sum(bool) error.
             step_mask.stop_gradient = True
-        new_state = nn.elementwise_mul(
+        new_state = paddle.tensor.math._multiply_with_axis(
             state, step_mask, axis=0
-        ) - nn.elementwise_mul(new_state, (step_mask - 1), axis=0)
+        ) - paddle.tensor.math._multiply_with_axis(
+            new_state, (step_mask - 1), axis=0
+        )
         if convert_dtype(state_dtype) in ["bool"]:
             new_state = tensor.cast(new_state, dtype=state_dtype)
         return new_state

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_se_resnext.py

@@ -139,7 +139,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
         y = paddle.nn.functional.relu(y)
         y = self._excitation(y)
         y = paddle.nn.functional.sigmoid(y)
-        y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
+        y = paddle.tensor.math._multiply_with_axis(x=input, y=y, axis=0)
         return y
 
 

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

@@ -199,7 +199,9 @@ class SE_ResNeXt:
             ),
             act='sigmoid',
         )
-        scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
+        scale = paddle.tensor.math._multiply_with_axis(
+            x=input, y=excitation, axis=0
+        )
         return scale
 
 

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

@@ -196,9 +196,11 @@ class BaseModel(fluid.dygraph.Layer):
         return x
 
     def _real_state(self, state, new_state, step_mask):
-        new_state = fluid.layers.elementwise_mul(
+        new_state = paddle.tensor.math._multiply_with_axis(
             new_state, step_mask, axis=0
-        ) - fluid.layers.elementwise_mul(state, (step_mask - 1), axis=0)
+        ) - paddle.tensor.math._multiply_with_axis(
+            state, (step_mask - 1), axis=0
+        )
         return new_state
 
     def _gather(self, x, indices, batch_pos):
@@ -452,10 +454,10 @@ class BaseModel(fluid.dygraph.Layer):
                     [-1, -1, self.tar_vocab_size],
                 ),
                 noend_mask_tensor,
-            ) - fluid.layers.elementwise_mul(
+            ) - paddle.tensor.math._multiply_with_axis(
                 step_log_probs, (beam_finished - 1), axis=0
             )
-            log_probs = fluid.layers.elementwise_add(
+            log_probs = paddle.tensor.math._add_with_axis(
                 x=step_log_probs, y=beam_state_log_probs, axis=0
             )
             scores = paddle.reshape(
@@ -689,9 +691,11 @@ class AttentionModel(fluid.dygraph.Layer):
         return x
 
     def _real_state(self, state, new_state, step_mask):
-        new_state = fluid.layers.elementwise_mul(
+        new_state = paddle.tensor.math._multiply_with_axis(
             new_state, step_mask, axis=0
-        ) - fluid.layers.elementwise_mul(state, (step_mask - 1), axis=0)
+        ) - paddle.tensor.math._multiply_with_axis(
+            state, (step_mask - 1), axis=0
+        )
         return new_state
 
     def _gather(self, x, indices, batch_pos):

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

@@ -105,18 +105,14 @@ class Cycle_Gan(fluid.dygraph.Layer):
         G = g_A_loss + g_B_loss
         idt_A = self.build_generator_resnet_9blocks_a(input_B)
         idt_loss_A = (
-            paddle.mean(
-                paddle.abs(fluid.layers.elementwise_sub(x=input_B, y=idt_A))
-            )
+            paddle.mean(paddle.abs(paddle.subtract(x=input_B, y=idt_A)))
             * lambda_B
             * lambda_identity
         )
 
         idt_B = self.build_generator_resnet_9blocks_b(input_A)
         idt_loss_B = (
-            paddle.mean(
-                paddle.abs(fluid.layers.elementwise_sub(x=input_A, y=idt_B))
-            )
+            paddle.mean(paddle.abs(paddle.subtract(x=input_A, y=idt_B)))
             * lambda_A
             * lambda_identity
         )

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

@@ -152,7 +152,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
         y = paddle.nn.functional.relu(y)
         y = self._excitation(y)
         y = paddle.nn.functional.sigmoid(y)
-        y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
+        y = paddle.tensor.math._multiply_with_axis(x=input, y=y, axis=0)
         return y
 
 

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

@@ -758,7 +758,9 @@ class Transformer(Layer):
                     [-1, -1, self.trg_vocab_size],
                 ),
                 noend_mask_tensor,
-            ) - layers.elementwise_mul(probs, (finished - 1), axis=0)
+            ) - paddle.tensor.math._multiply_with_axis(
+                probs, (finished - 1), axis=0
+            )
             return probs
 
         def gather(input, indices, batch_pos):
@@ -846,7 +848,7 @@ class Transformer(Layer):
             step_log_probs = mask_probs(
                 step_log_probs, finished, noend_mask_tensor
             )
-            log_probs = layers.elementwise_add(
+            log_probs = paddle.tensor.math._add_with_axis(
                 x=step_log_probs, y=log_probs, axis=0
             )
             log_probs = paddle.reshape(

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

@@ -35,7 +35,7 @@ class TestMul(IPUOpTest):
             return True
 
     def set_test_op(self):
-        self.op = paddle.fluid.layers.elementwise_mul
+        self.op = paddle.tensor.math._multiply_with_axis
 
     def set_feed_attr(self):
         self.feed_shape = [x.shape for x in self.feed_fp32.values()]

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_elt_act_fuse_pass.py

@@ -360,7 +360,7 @@ class ElementwiseScaleOneDNNFusePassTest_Add(
     ElementwiseActivationMkldnnFusePassTest
 ):
     def set_params(self):
-        self.operand = fluid.layers.elementwise_add
+        self.operand = paddle.add
         self.act_alpha = 0.6
         self.act = paddle.scale
 
@@ -369,7 +369,7 @@ class ElementwiseScaleOneDNNFusePassTest_Sub(
     ElementwiseActivationMkldnnFusePassTest
 ):
     def set_params(self):
-        self.operand = fluid.layers.elementwise_sub
+        self.operand = paddle.subtract
         self.act_alpha = 0.6
         self.act = paddle.scale
 
@@ -378,7 +378,7 @@ class ElementwiseScaleOneDNNFusePassTest_Mul(
     ElementwiseActivationMkldnnFusePassTest
 ):
     def set_params(self):
-        self.operand = fluid.layers.elementwise_mul
+        self.operand = paddle.multiply
         self.act_alpha = 0.6
         self.act = paddle.scale
 
@@ -387,7 +387,7 @@ class ElementwiseScaleOneDNNFusePassTest_Div(
     ElementwiseActivationMkldnnFusePassTest
 ):
     def set_params(self):
-        self.operand = fluid.layers.elementwise_div
+        self.operand = paddle.divide
         self.act_alpha = 0.6
         self.act = paddle.scale
 

python/paddle/fluid/tests/unittests/ir/inference/test_trt_elementwise_op.py

@@ -19,6 +19,7 @@ import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.static.nn as nn
@@ -49,7 +50,7 @@ class TensorRTSubgraphPassElementwiseBroadcastTest(InferencePassTest):
         self.fetch_list = [out]
 
     def append_eltwise(self, data1, data2):
-        return fluid.layers.elementwise_add(x=data1, y=data2, axis=0)
+        return paddle.tensor.math._add_with_axis(x=data1, y=data2, axis=0)
 
     def test_check_output(self):
         if os.path.exists(self.path + "_opt_cache"):
@@ -66,21 +67,21 @@ class TensorRTSubgraphPassElementwiseBroadcastTest1(
     TensorRTSubgraphPassElementwiseBroadcastTest
 ):
     def append_eltwise(self, data1, data2):
-        return fluid.layers.elementwise_sub(x=data1, y=data2, axis=0)
+        return paddle.tensor.math._subtract_with_axis(x=data1, y=data2, axis=0)
 
 
 class TensorRTSubgraphPassElementwiseBroadcastTest2(
     TensorRTSubgraphPassElementwiseBroadcastTest
 ):
     def append_eltwise(self, data1, data2):
-        return fluid.layers.elementwise_mul(x=data1, y=data2, axis=0)
+        return paddle.tensor.math._multiply_with_axis(x=data1, y=data2, axis=0)
 
 
 class TensorRTSubgraphPassElementwiseBroadcastTest3(
     TensorRTSubgraphPassElementwiseBroadcastTest
 ):
     def append_eltwise(self, data1, data2):
-        return fluid.layers.elementwise_div(x=data1, y=data2, axis=0)
+        return paddle.tensor.math._divide_with_axis(x=data1, y=data2, axis=0)
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ir/inference/test_trt_subgraph_pass.py

@@ -394,7 +394,7 @@ class TensorRTSubgraphPassElementwiseMulTest(
     TensorRTSubgraphPassElementwiseTest
 ):
     def append_eltwise(self, data1, data2):
-        return fluid.layers.elementwise_mul(x=data1, y=data2)
+        return paddle.multiply(x=data1, y=data2)
 
 
 class TensorRTSubgraphPassElementwiseSerializeTest(

python/paddle/fluid/tests/unittests/mlu/test_elementwise_add_op_mlu.py

@@ -396,24 +396,6 @@ class TestElementwiseAddOp_same_shape_ysize_large(TestElementwiseAddOp):
         self.axis = 0
 
 
-class TestElementwiseAddOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # the input of elementwise_add must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.MLUPlace(0)
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.MLUPlace(0)
-            )
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)
-
-            # the input dtype of elementwise_add must be float16 or float32
-            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
-            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)
-
-
 class TestAddApi(unittest.TestCase):
     def _executed_api(self, x, y, name=None):
         return paddle.add(x, y, name)

python/paddle/fluid/tests/unittests/mlu/test_elementwise_mul_op_mlu.py

@@ -222,23 +222,5 @@ class TestElementwiseMulOp_xsize_lessthan_ysize(ElementwiseMulOp):
         self.init_kernel_type()
 
 
-class TestElementwiseMulOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # the input of elementwise_mul must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
-            )
-            self.assertRaises(TypeError, fluid.layers.elementwise_mul, x1, y1)
-
-            # the input dtype of elementwise_mul must be float16 or float32 or int32
-            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
-            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.elementwise_mul, x2, y2)
-
-
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/npu/test_elementwise_add_op_npu.py

@@ -226,7 +226,7 @@ class TestAddAPI(unittest.TestCase):
 class TestAddError(unittest.TestCase):
     def test_errors(self):
         with paddle.static.program_guard(paddle.static.Program()):
-            # the input of elementwise_add must be Variable.
+            # the input of paddle.add must be Variable.
             x1 = fluid.create_lod_tensor(
                 np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.NPUPlace(0)
             )
@@ -504,25 +504,6 @@ class TestElementwiseAddOp_same_shape_ysize_large(TestElementwiseAddOp):
         self.axis = 0
 
 
-class TestElementwiseAddOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # the input of elementwise_add must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.NPUPlace(0)
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.NPUPlace(0)
-            )
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)
-
-            # the input dtype of elementwise_add must be float16 or float32 or float64 or int32 or int64
-            # float16 only can be set on GPU place
-            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
-            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)
-
-
 class TestAddApi(unittest.TestCase):
     def _executed_api(self, x, y, name=None):
         return paddle.add(x, y, name)

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

@@ -59,7 +59,9 @@ def squeeze_excitation(input, num_channels, reduction_ratio):
     excitation = fluid.layers.fc(
         input=squeeze, size=num_channels, act='sigmoid'
     )
-    scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
+    scale = paddle.tensor.math._multiply_with_axis(
+        x=input, y=excitation, axis=0
+    )
     return scale
 
 

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

@@ -65,7 +65,6 @@ class TestDeprecatedDocorator(unittest.TestCase):
     """
     tests for paddle's Deprecated Docorator.
     test_fluid_data: test for old fluid.data API.
-    test_fluid_elementwise_mul: test for old fluid.layers.elementwise_xxx APIs.
     test_new_multiply: test for new api, which should not insert warning information.
     test_ops_elementwise_mul: test for C++ elementwise_mul op, which should not insert warning information.
     """
@@ -89,28 +88,6 @@ class TestDeprecatedDocorator(unittest.TestCase):
         # testting
         self.assertGreater(expected, captured)
 
-    def test_fluid_elementwise_mul(self):
-        """
-        test old fluid elementwise_mul api, it should trigger Warinng function,
-        which insert the Warinng info on top of API's doc string.
-        """
-
-        # Initialization
-        a = np.random.uniform(0.1, 1, [51, 76]).astype(np.float32)
-        b = np.random.uniform(0.1, 1, [51, 76]).astype(np.float32)
-        x = paddle.to_tensor(a)
-        y = paddle.to_tensor(b)
-        res = fluid.layers.elementwise_mul(x, y)
-
-        # expected
-        expected = LOWEST_WARNING_POSTION
-
-        # captured
-        captured = get_warning_index(fluid.layers.elementwise_mul)
-
-        # testting
-        self.assertGreater(expected, captured)
-
     def test_new_multiply(self):
         """
         Test for new multiply api, expected result should be False.
@@ -147,7 +124,7 @@ class TestDeprecatedDocorator(unittest.TestCase):
         expected = LOWEST_WARNING_POSTION
 
         # captured
-        captured = get_warning_index(fluid.layers.elementwise_mul)
+        captured = get_warning_index(paddle.multiply)
 
         # testting
         self.assertGreater(expected, captured)

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

@@ -19,7 +19,6 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.fluid import Program, program_guard
 from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.tests.unittests.op_test import (
     OpTest,
@@ -490,25 +489,6 @@ class TestElementwiseAddOp_same_shape_ysize_large(TestElementwiseAddOp):
         self.axis = 0
 
 
-class TestElementwiseAddOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # the input of elementwise_add must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
-            )
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)
-
-            # the input dtype of elementwise_add must be float16 or float32 or float64 or int32 or int64
-            # float16 only can be set on GPU place
-            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
-            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)
-
-
 class TestAddApi(unittest.TestCase):
     def _executed_api(self, x, y, name=None):
         return paddle.add(x, y, name)

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

@@ -17,9 +17,7 @@ import unittest
 import numpy as np
 
 import paddle
-import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.fluid import Program, program_guard
 from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.tests.unittests.op_test import (
     OpTest,
@@ -289,25 +287,6 @@ class TestElementwiseMulOp_xsize_lessthan_ysize(ElementwiseMulOp):
         self.init_kernel_type()
 
 
-class TestElementwiseMulOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # the input of elementwise_mul must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
-            )
-            self.assertRaises(TypeError, fluid.layers.elementwise_mul, x1, y1)
-
-            # the input dtype of elementwise_mul must be float16 or float32 or float64 or int32 or int64
-            # float16 only can be set on GPU place
-            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
-            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.elementwise_mul, x2, y2)
-
-
 class TestComplexElementwiseMulOp(OpTest):
     def setUp(self):
         self.op_type = "elementwise_mul"

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

@@ -36,7 +36,7 @@ class TestElementwiseMulDoubleGradCheck(unittest.TestCase):
         y = layers.data('y', shape, False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_mul(x, y)
+        out = paddle.multiply(x, y)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape).astype(dtype)
 
@@ -65,7 +65,7 @@ class TestElementwiseMulBroadcastDoubleGradCheck(unittest.TestCase):
         y = layers.data('y', shape[:-1], False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_mul(x, y, axis=0)
+        out = paddle.tensor.math._multiply_with_axis(x, y, axis=0)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape[:-1]).astype(dtype)
 
@@ -123,7 +123,7 @@ class TestElementwiseAddBroadcastDoubleGradCheck(unittest.TestCase):
         y = layers.data('y', shape[:-1], False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_add(x, y, axis=0)
+        out = paddle.tensor.math._add_with_axis(x, y, axis=0)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape[:-1]).astype(dtype)
 
@@ -191,7 +191,7 @@ class TestElementwiseSubBroadcastDoubleGradCheck(unittest.TestCase):
         y = layers.data('y', shape[:-1], False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_sub(x, y, axis=0)
+        out = paddle.tensor.math._subtract_with_axis(x, y, axis=0)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape[:-1]).astype(dtype)
 
@@ -223,7 +223,7 @@ class TestElementwiseDivDoubleGradCheck(unittest.TestCase):
         y = layers.data('y', shape, False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_div(x, y, axis=0)
+        out = paddle.tensor.math._divide_with_axis(x, y, axis=0)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr[np.abs(y_arr) < 0.005] = 0.02
@@ -261,7 +261,7 @@ class TestElementwiseDivBroadcastDoubleGradCheck(unittest.TestCase):
         y = layers.data('y', shape[1:-1], False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_div(x, y, axis=1)
+        out = paddle.tensor.math._divide_with_axis(x, y, axis=1)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape[1:-1]).astype(dtype)
         y_arr[np.abs(y_arr) < 0.005] = 0.02
@@ -320,7 +320,7 @@ class TestElementwiseAddBroadcastTripleGradCheck(unittest.TestCase):
         y = layers.data('y', shape[:-1], False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_add(x, y, axis=0)
+        out = paddle.tensor.math._add_with_axis(x, y, axis=0)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape[:-1]).astype(dtype)
 
@@ -352,7 +352,7 @@ class TestElementwiseMulTripleGradCheck(unittest.TestCase):
         y = layers.data('y', shape, False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_mul(x, y)
+        out = paddle.multiply(x, y)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape).astype(dtype)
 
@@ -390,7 +390,7 @@ class TestElementwiseMulBroadcastTripleGradCheck(unittest.TestCase):
         y = layers.data('y', shape[:-1], False, dtype)
         x.persistable = True
         y.persistable = True
-        out = layers.elementwise_add(x, y, axis=0)
+        out = paddle.tensor.math._add_with_axis(x, y, axis=0)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
         y_arr = np.random.uniform(-1, 1, shape[:-1]).astype(dtype)
 

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

@@ -98,7 +98,7 @@ class TestFuseActElewiseAddInplaceGradPass(unittest.TestCase):
             Y = fluid.data(name="Y", shape=[3, 3], dtype='float32')
             Out1 = X * 5
             Out2 = fluid.layers.relu(Out1)
-            prediction = fluid.layers.elementwise_add(Y, Out2, axis=1)
+            prediction = paddle.tensor.math._add_with_axis(Y, Out2, axis=1)
             loss = paddle.mean(prediction)
             sgd = fluid.optimizer.SGD(learning_rate=0.001)
             sgd.minimize(loss)

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

@@ -307,7 +307,7 @@ class SimpleAttention(fluid.dygraph.Layer):
         )
 
         weights_reshape = paddle.nn.functional.softmax(weights_reshape)
-        scaled = fluid.layers.elementwise_mul(
+        scaled = paddle.tensor.math._multiply_with_axis(
             x=encoder_vec, y=weights_reshape, axis=0
         )
         context = paddle.sum(scaled, axis=1)

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

@@ -130,7 +130,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
         y = self.act_1(y)
         y = self._excitation(y)
         y = self.act_2(y)
-        y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
+        y = paddle.tensor.math._multiply_with_axis(x=input, y=y, axis=0)
         return y
 
 

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

@@ -409,9 +409,9 @@ def gradient_penalty(f, real, fake, no_grad_set, cfg):
             input=a, shape=shape, min=0.1, max=1.0, seed=cfg.seed
         )
 
-        inner = fluid.layers.elementwise_mul(
+        inner = paddle.tensor.math._multiply_with_axis(
             b, 1.0 - alpha, axis=0
-        ) + fluid.layers.elementwise_mul(a, alpha, axis=0)
+        ) + paddle.tensor.math._multiply_with_axis(a, alpha, axis=0)
         return inner
 
     x = _interpolate(real, fake)

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

@@ -159,8 +159,8 @@ def multi_head_attention(
 
         def __softmax(x, eps=1e-9):
             exp_out = paddle.exp(x=x)
-            sum_out = paddle.sum(exp_out, axis=-1, keepdim=False)
-            return layers.elementwise_div(x=exp_out, y=sum_out, axis=0)
+            sum_out = paddle.sum(exp_out, axis=-1, keepdim=True)
+            return paddle.divide(x=exp_out, y=sum_out)
 
         scaled_q = paddle.scale(x=q, scale=d_model**-0.5)
         product = paddle.matmul(x=scaled_q, y=k, transpose_y=True)

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

@@ -29,7 +29,6 @@ from xpu.get_test_cover_info import (
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid import Program, program_guard
 
 paddle.enable_static()
 
@@ -263,32 +262,6 @@ class XPUTestElementwiseAddOp(XPUOpTestWrapper):
         def init_axis(self):
             self.axis = 2
 
-    class TestElementwiseAddOpError(unittest.TestCase):
-        def test_errors(self):
-            with program_guard(Program(), Program()):
-                # the input of elementwise_add must be Variable.
-                x1 = fluid.create_lod_tensor(
-                    np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.XPUPlace(0)
-                )
-                y1 = fluid.create_lod_tensor(
-                    np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.XPUPlace(0)
-                )
-                self.assertRaises(
-                    TypeError, fluid.layers.elementwise_add, x1, y1
-                )
-
-                # the input dtype of elementwise_add must be float16 or float32 or float64 or int32 or int64
-                # float16 only can be set on GPU place
-                x2 = fluid.layers.data(
-                    name='x2', shape=[3, 4, 5, 6], dtype="uint8"
-                )
-                y2 = fluid.layers.data(
-                    name='y2', shape=[3, 4, 5, 6], dtype="uint8"
-                )
-                self.assertRaises(
-                    TypeError, fluid.layers.elementwise_add, x2, y2
-                )
-
     class TestAddOp(unittest.TestCase):
         def test_name(self):
             with fluid.program_guard(fluid.Program()):

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

@@ -24,7 +24,6 @@ from op_test_xpu import XPUOpTest
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid import Program, program_guard
 
 paddle.enable_static()
 
@@ -307,28 +306,6 @@ class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
         self.axis = 2
 
 
-@unittest.skipIf(
-    not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
-)
-class TestElementwiseAddOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # the input of elementwise_add must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.XPUPlace(0)
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.XPUPlace(0)
-            )
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1)
-
-            # the input dtype of elementwise_add must be float16 or float32 or float64 or int32 or int64
-            # float16 only can be set on GPU place
-            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
-            y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)
-
-
 @unittest.skipIf(
     not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
 )

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

@@ -26,8 +26,6 @@ from xpu.get_test_cover_info import (
 )
 
 import paddle
-import paddle.fluid as fluid
-from paddle.fluid import Program, program_guard
 
 paddle.enable_static()
 
@@ -241,31 +239,6 @@ class XPUTestElementwiseMulOp(XPUOpTestWrapper):
                 'Out': self.inputs['X'].reshape(1, 1, 10, 10) * self.inputs['Y']
             }
 
-    class TestElementwiseMulOpError(unittest.TestCase):
-        def test_errors(self):
-            with program_guard(Program(), Program()):
-                # the input of elementwise_mul must be Variable.
-                x1 = fluid.create_lod_tensor(
-                    np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.XPUPlace(0)
-                )
-                y1 = fluid.create_lod_tensor(
-                    np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.XPUPlace(0)
-                )
-                self.assertRaises(
-                    TypeError, fluid.layers.elementwise_mul, x1, y1
-                )
-
-                # the input dtype of elementwise_mul must be float32
-                x2 = fluid.layers.data(
-                    name='x2', shape=[3, 4, 5, 6], dtype="uint8"
-                )
-                y2 = fluid.layers.data(
-                    name='y2', shape=[3, 4, 5, 6], dtype="uint8"
-                )
-                self.assertRaises(
-                    TypeError, fluid.layers.elementwise_mul, x2, y2
-                )
-
 
 support_types = get_xpu_op_support_types('elementwise_mul')
 for stype in support_types:

python/paddle/nn/functional/conv.py

@@ -26,11 +26,11 @@ from paddle.fluid.framework import (
     _in_legacy_dygraph,
     in_dygraph_mode,
 )
+from paddle.tensor.math import _add_with_axis
 
 from ...device import get_cudnn_version
 from ...fluid.data_feeder import check_dtype, check_variable_and_dtype
 from ...fluid.layer_helper import LayerHelper
-from ...fluid.layers import nn
 from ...fluid.layers.utils import (
     _contain_var,
     _convert_to_tensor_list,
@@ -226,7 +226,7 @@ def _conv_nd(
         )
         pre_bias = getattr(_legacy_C_ops, op_type)(x, weight, *attrs)
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+            out = _add_with_axis(pre_bias, bias, axis=channel_dim)
         else:
             out = pre_bias
     else:
@@ -491,7 +491,7 @@ def conv1d(
                 False,
             )
         if bias is not None:
-            out = nn.elementwise_add(out, bias, axis=channel_dim)
+            out = _add_with_axis(out, bias, axis=channel_dim)
     elif _in_legacy_dygraph():
         attrs = (
             'strides',
@@ -515,7 +515,7 @@ def conv1d(
         )
         out = getattr(_legacy_C_ops, l_type)(x, weight, *attrs)
         if bias is not None:
-            out = nn.elementwise_add(out, bias, axis=channel_dim)
+            out = _add_with_axis(out, bias, axis=channel_dim)
     else:
         inputs = {'Input': [x], 'Filter': [weight]}
         attrs = {
@@ -540,7 +540,7 @@ def conv1d(
             type=l_type, inputs=inputs, outputs=outputs, attrs=attrs
         )
         if bias is not None:
-            out = nn.elementwise_add(out, bias, axis=channel_dim)
+            out = _add_with_axis(out, bias, axis=channel_dim)
     out = squeeze(out, axis=[squeeze_aixs])
     return out
 
@@ -1047,7 +1047,7 @@ def conv1d_transpose(
             conv2d_data_format,
         )
         if bias is not None:
-            out = nn.elementwise_add(out, bias, axis=channel_dim)
+            out = _add_with_axis(out, bias, axis=channel_dim)
     elif _in_legacy_dygraph():
         attrs = (
             'output_padding',
@@ -1071,7 +1071,7 @@ def conv1d_transpose(
         )
         out = getattr(_legacy_C_ops, op_type)(x, weight, *attrs)
         if bias is not None:
-            out = nn.elementwise_add(out, bias, axis=channel_dim)
+            out = _add_with_axis(out, bias, axis=channel_dim)
     else:
         inputs = {'Input': [x], 'Filter': [weight]}
         attrs = {
@@ -1096,7 +1096,7 @@ def conv1d_transpose(
             type=op_type, inputs=inputs, outputs=outputs, attrs=attrs
         )
         if bias is not None:
-            out = nn.elementwise_add(out, bias, axis=channel_dim)
+            out = _add_with_axis(out, bias, axis=channel_dim)
 
     out = squeeze(out, axis=[squeeze_axis])
     return out
@@ -1351,7 +1351,7 @@ def conv2d_transpose(
             data_format,
         )
         if bias is not None:
-            return nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+            return _add_with_axis(pre_bias, bias, axis=channel_dim)
         else:
             return pre_bias
 
@@ -1378,7 +1378,7 @@ def conv2d_transpose(
         )
         pre_bias = getattr(_legacy_C_ops, op_type)(x, weight, *attrs)
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+            out = _add_with_axis(pre_bias, bias, axis=channel_dim)
         else:
             out = pre_bias
     else:
@@ -1405,7 +1405,7 @@ def conv2d_transpose(
         )
 
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+            out = _add_with_axis(pre_bias, bias, axis=channel_dim)
         else:
             out = pre_bias
 
@@ -1824,7 +1824,7 @@ def conv3d_transpose(
             data_format_,
         )
         if bias is not None:
-            return nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+            return _add_with_axis(pre_bias, bias, axis=channel_dim)
         else:
             return pre_bias
 
@@ -1851,7 +1851,7 @@ def conv3d_transpose(
         )
         pre_bias = getattr(_legacy_C_ops, op_type)(x, weight, *attrs)
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+            out = _add_with_axis(pre_bias, bias, axis=channel_dim)
         else:
             out = pre_bias
     else:
@@ -1879,7 +1879,7 @@ def conv3d_transpose(
             type=op_type, inputs=inputs, outputs=outputs, attrs=attrs
         )
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=channel_dim)
+            out = _add_with_axis(pre_bias, bias, axis=channel_dim)
         else:
             out = pre_bias
 

python/paddle/nn/functional/loss.py

@@ -1405,15 +1405,13 @@ def l1_loss(input, label, reduction='mean', name=None):
     )
 
     if reduction == 'sum':
-        unreduced = paddle.fluid.layers.elementwise_sub(input, label, act='abs')
+        unreduced = paddle.abs(paddle.subtract(x=input, y=label))
         return paddle.sum(unreduced, name=name)
     elif reduction == 'mean':
-        unreduced = paddle.fluid.layers.elementwise_sub(input, label, act='abs')
+        unreduced = paddle.abs(paddle.subtract(x=input, y=label))
         return paddle.mean(unreduced, name=name)
     else:
-        return paddle.fluid.layers.elementwise_sub(
-            input, label, act='abs', name=name
-        )
+        return paddle.abs(paddle.subtract(x=input, y=label, name=name))
 
 
 def nll_loss(

python/paddle/nn/utils/weight_norm_hook.py

@@ -90,7 +90,7 @@ def _weight_norm(v, g, dim):
         v_normalized = F.l2_normalize(p_matrix, axis=1)
         v_normalized = paddle.reshape(v_normalized, transposed_shape)
         v_normalized = paddle.transpose(v_normalized, perm)
-    weight = F.elementwise_mul(
+    weight = paddle.tensor.math._multiply_with_axis(
         v_normalized, g, axis=dim if dim is not None else -1
     )
     return weight

python/paddle/tensor/math.py

@@ -31,7 +31,7 @@ from ..fluid.data_feeder import (
     check_variable_and_dtype,
     convert_dtype,
 )
-from ..fluid.layers import elementwise_sub, utils
+from ..fluid.layers import utils
 from ..framework import (
     LayerHelper,
     _in_legacy_dygraph,
@@ -985,6 +985,115 @@ def multiply(x, y, name=None):
             return _elementwise_op(LayerHelper(op_type, **locals()))
 
 
+@dygraph_only
+def _elementwise_op_with_axis_in_dygraph(
+    x, y, axis=-1, name=None, op_type="Undifined"
+):
+    assert (
+        in_dygraph_mode()
+    ), "You can only call `_elementwise_op_with_axis_in_dygraph` function within in_dygraph_mode"
+    assert op_type in ["add", "subtract", "multiply", "divide"], (
+        "op_name input error! _elementwise_op_with_axis is an inner function to replace elementwise_add/sub/mul/div. Input op_name=%s, Expect op_name=[add|subtract|multiply|divide]\n"
+        % op_type
+    )
+    op = getattr(_C_ops, op_type)
+    x_shape = list(x.shape)
+    y_shape = list(y.shape)
+    if axis == -1 or len(x_shape) == len(y_shape):
+        return op(x, y)
+    if len(x_shape) > len(y_shape):
+        padding = len(x_shape) - len(y_shape) - axis
+        y = paddle.reshape(y, [1] * axis + y_shape + [1] * padding)
+    else:
+        padding = len(y_shape) - len(x_shape) - axis
+        x = paddle.reshape(x, [1] * axis + y_shape + [1] * padding)
+    return op(x, y)
+
+
+def _add_with_axis(x, y, axis=-1, name=None):
+    # opt performance, only dynamic mode needs reshape
+    if in_dygraph_mode():
+        return _elementwise_op_with_axis_in_dygraph(x, y, axis, name, "add")
+    else:
+        op_type = 'elementwise_add'
+        act = None
+        if _in_legacy_dygraph():
+            return _elementwise_op_in_dygraph(
+                x, y, axis=axis, act=act, op_name=op_type
+            )
+        else:
+            if x.dtype != y.dtype:
+                raise TypeError(
+                    'Input tensors must be same type, but received type of x: %s, type of y: %s '
+                    % (x.dtype, y.dtype)
+                )
+            return _elementwise_op(LayerHelper(op_type, **locals()))
+
+
+def _subtract_with_axis(x, y, axis=-1, name=None):
+    # opt performance, only dynamic mode needs reshape
+    if in_dygraph_mode():
+        return _elementwise_op_with_axis_in_dygraph(
+            x, y, axis, name, "subtract"
+        )
+    else:
+        op_type = 'elementwise_sub'
+        act = None
+        if _in_legacy_dygraph():
+            return _elementwise_op_in_dygraph(
+                x, y, axis=axis, act=act, op_name=op_type
+            )
+        else:
+            if x.dtype != y.dtype:
+                raise TypeError(
+                    'Input tensors must be same type, but received type of x: %s, type of y: %s '
+                    % (x.dtype, y.dtype)
+                )
+            return _elementwise_op(LayerHelper(op_type, **locals()))
+
+
+def _multiply_with_axis(x, y, axis=-1, name=None):
+    # opt performance, only dynamic mode needs reshape
+    if in_dygraph_mode():
+        return _elementwise_op_with_axis_in_dygraph(
+            x, y, axis, name, "multiply"
+        )
+    else:
+        op_type = 'elementwise_mul'
+        act = None
+        if _in_legacy_dygraph():
+            return _elementwise_op_in_dygraph(
+                x, y, axis=axis, act=act, op_name=op_type
+            )
+        else:
+            if x.dtype != y.dtype:
+                raise TypeError(
+                    'Input tensors must be same type, but received type of x: %s, type of y: %s '
+                    % (x.dtype, y.dtype)
+                )
+            return _elementwise_op(LayerHelper(op_type, **locals()))
+
+
+def _divide_with_axis(x, y, axis=-1, name=None):
+    # opt performance, only dynamic mode needs reshape
+    if in_dygraph_mode():
+        return _elementwise_op_with_axis_in_dygraph(x, y, axis, name, "divide")
+    else:
+        op_type = 'elementwise_div'
+        act = None
+        if _in_legacy_dygraph():
+            return _elementwise_op_in_dygraph(
+                x, y, axis=axis, act=act, op_name=op_type
+            )
+        else:
+            if x.dtype != y.dtype:
+                raise TypeError(
+                    'Input tensors must be same type, but received type of x: %s, type of y: %s '
+                    % (x.dtype, y.dtype)
+                )
+            return _elementwise_op(LayerHelper(op_type, **locals()))
+
+
 def maximum(x, y, name=None):
     """
     Compare two tensors and returns a new tensor containing the element-wise maxima. The equation is:
@@ -4877,7 +4986,9 @@ def diff(x, n=1, axis=-1, prepend=None, append=None, name=None):
         if x.dtype == paddle.bool:
             return _legacy_C_ops.logical_xor(input_back, input_front)
         else:
-            return elementwise_sub(input_back, input_front, axis=axis)
+            return paddle.tensor.math._subtract_with_axis(
+                input_back, input_front, axis=axis
+            )
     else:
         check_variable_and_dtype(
             x, 'x', ['float32', 'float64', 'bool', 'int32', 'int64'], 'diff'
@@ -4941,7 +5052,9 @@ def diff(x, n=1, axis=-1, prepend=None, append=None, name=None):
                 outputs={"Out": out},
             )
         else:
-            out = elementwise_sub(input_back, input_front, axis=axis)
+            out = paddle.tensor.math._subtract_with_axis(
+                input_back, input_front, axis=axis
+            )
 
         return out
 

python/paddle/vision/ops.py

@@ -15,6 +15,7 @@
 import numpy as np
 
 from paddle import _C_ops, _legacy_C_ops
+from paddle.tensor.math import _add_with_axis
 
 from ..fluid.data_feeder import check_type, check_variable_and_dtype
 from ..fluid.framework import (
@@ -25,7 +26,7 @@ from ..fluid.framework import (
 )
 from ..fluid.initializer import Normal
 from ..fluid.layer_helper import LayerHelper
-from ..fluid.layers import nn, utils
+from ..fluid.layers import utils
 from ..framework import _current_expected_place
 from ..nn import BatchNorm2D, Conv2D, Layer, ReLU, Sequential
 
@@ -985,7 +986,7 @@ def deform_conv2d(
             1,
         )
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=1)
+            out = _add_with_axis(pre_bias, bias, axis=1)
         else:
             out = pre_bias
     elif _in_legacy_dygraph():
@@ -1014,7 +1015,7 @@ def deform_conv2d(
                 x, offset, mask, weight, *attrs
             )
         if bias is not None:
-            out = nn.elementwise_add(pre_bias, bias, axis=1)
+            out = _add_with_axis(pre_bias, bias, axis=1)
         else:
             out = pre_bias
     else: