[Clean Fluid API]Remove squeeze: use paddle.squeeze to replace paddle.fluid.layers.squeeze (#47938)

* Remove API: squeeze
	Modify the funcation all of squeeze.
	Use paddle.squeeze to replace paddle.fluid.layers.squeeze

* Remove: squeeze
	fix some modifications which are not needless to the original
format

* Fix the function call of 'squeeze' from old style to new style.

* modify the call of squeeze from old style to new style

* Modify the call of squeeze from old style to new style

* replace squeeze with paddle.squeeze

* modify the nn.py based on the review suggestion

remove a redundant comment.

* remove one comment which is not necessary

python/paddle/fluid/layers/nn.py

@@ -93,7 +93,6 @@ __all__ = [
     'smooth_l1',
     'one_hot',
     'autoincreased_step_counter',
-    'squeeze',
     'unsqueeze',
     'lod_reset',
     'lod_append',
@@ -4095,7 +4094,7 @@ def ctc_greedy_decoder(
         return ctc_out
     else:
         ctc_out_len = helper.create_variable_for_type_inference(dtype="int64")
-        ctc_input = squeeze(topk_indices, [2])
+        ctc_input = paddle.squeeze(topk_indices, [2])
 
         helper.append_op(
             type="ctc_align",
@@ -4636,105 +4635,6 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
     return counter
 
 
-def squeeze(input, axes, name=None):
-    """
-    This OP will squeeze single-dimensional entries of input tensor's shape. If axes is provided, will
-    remove the dims by axes, the dims selected by axes should be one. If not provide axes, all dims equal
-    to one will be deleted.
-
-
-    .. code-block:: text
-
-        Case1:
-
-          Input:
-            X.shape = (1, 3, 1, 5)
-            axes = [0]
-          Output:
-            Out.shape = (3, 1, 5)
-
-        Case2:
-
-          Input:
-            X.shape = (1, 3, 1, 5)
-            axes = []
-          Output:
-            Out.shape = (3, 5)
-
-        Case3:
-
-          Input:
-            X.shape = [1,3,1,5]
-            axes = [-2]
-          Output:
-            Out.shape = [1,3,5]
-
-    Args:
-        input (Variable): The input Tensor. Supported data type: float32, float64, bool, int8, int32, int64.
-                          axes (list): One integer or List of integers, indicating the dimensions to be squeezed.
-                          Axes range is :math:`[-rank(input), rank(input))`.
-                          If axes is negative, :math:`axes=axes+rank(input)`.
-        name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
-
-    Returns:
-        Variable: Output squeezed Tensor. Data type is same as input Tensor.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            import paddle.fluid.layers as layers
-            # set batch size=None
-            x = fluid.data(name='x', shape=[None, 5, 1, 10])
-            y = layers.squeeze(input=x, axes=[2]) # y.shape=[None, 5, 10]
-
-    """
-    if in_dygraph_mode():
-        return _C_ops.squeeze(input, axes)
-    if _in_legacy_dygraph():
-        out, _ = _legacy_C_ops.squeeze2(input, 'axes', axes)
-        return out
-
-    helper = LayerHelper("squeeze", **locals())
-    check_variable_and_dtype(
-        input,
-        'input',
-        [
-            'float16',
-            'float32',
-            'float64',
-            'bool',
-            'int8',
-            'int32',
-            'int64',
-            'complex64',
-            'complex128',
-        ],
-        'squeeze',
-    )
-    check_type(axes, 'axis/axes', (list, tuple, Variable), 'squeeze')
-
-    attrs = {}
-    if isinstance(axes, Variable):
-        axes.stop_gradient = True
-        attrs["axes"] = axes
-    elif isinstance(axes, (list, tuple)):
-        if utils._contain_var(axes):
-            attrs["axes"] = utils._convert_to_tensor_list(axes)
-        else:
-            attrs["axes"] = axes
-    out = helper.create_variable_for_type_inference(dtype=input.dtype)
-    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
-    helper.append_op(
-        type="squeeze2",
-        inputs={"X": input},
-        attrs=attrs,
-        outputs={"Out": out, "XShape": x_shape},
-    )
-
-    return out
-
-
 def unsqueeze(input, axes, name=None):
     """
     Insert single-dimensional entries to the shape of a Tensor. Takes one

python/paddle/fluid/layers/rnn.py

@@ -2088,11 +2088,11 @@ class TrainingHelper(DecodeHelper):
 
         def _slice(x):  # TODO: use Variable.__getitem__
             axes = [0 if self.time_major else 1]
-            return nn.squeeze(
+            return paddle.squeeze(
                 nn.slice(
                     x, axes=axes, starts=[next_time], ends=[next_time + 1]
                 ),
-                axes=axes,
+                axis=axes,
             )
 
         next_inputs = map_structure(_slice, self.inputs_)

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

@@ -295,7 +295,7 @@ class BaseModel(fluid.dygraph.Layer):
         loss = fluid.layers.softmax_with_cross_entropy(
             logits=dec_output, label=label, soft_label=False
         )
-        loss = fluid.layers.squeeze(loss, axes=[2])
+        loss = paddle.squeeze(loss, axes=[2])
         max_tar_seq_len = fluid.layers.shape(tar)[1]
         tar_mask = fluid.layers.sequence_mask(
             tar_sequence_length, maxlen=max_tar_seq_len, dtype='float32'
@@ -811,7 +811,7 @@ class AttentionModel(fluid.dygraph.Layer):
                 else:
                     step_input = new_hidden
             dec_att = self.attention(step_input, enc_outputs, enc_padding_mask)
-            dec_att = fluid.layers.squeeze(dec_att, [1])
+            dec_att = paddle.squeeze(dec_att, [1])
             concat_att_out = fluid.layers.concat([dec_att, step_input], 1)
             out = self.concat_fc(concat_att_out)
             input_feed = out
@@ -823,7 +823,7 @@ class AttentionModel(fluid.dygraph.Layer):
         loss = fluid.layers.softmax_with_cross_entropy(
             logits=dec_output, label=label, soft_label=False
         )
-        loss = fluid.layers.squeeze(loss, axes=[2])
+        loss = paddle.squeeze(loss, axes=[2])
         max_tar_seq_len = fluid.layers.shape(tar)[1]
         tar_mask = fluid.layers.sequence_mask(
             tar_sequence_length, maxlen=max_tar_seq_len, dtype='float32'

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

@@ -137,7 +137,7 @@ class Conv1D(fluid.dygraph.Layer):
     def forward(self, x):
         x = fluid.layers.unsqueeze(input=x, axes=[2])
         x = self._conv2d(x)
-        x = fluid.layers.squeeze(input=x, axes=[2])
+        x = paddle.squeeze(x, axis=[2])
         return x
 
 
@@ -275,10 +275,10 @@ class BMN(fluid.dygraph.Layer):
         # TEM
         xs = paddle.nn.functional.relu(self.ts_conv1(x))
         xs = paddle.nn.functional.relu(self.ts_conv2(xs))
-        xs = fluid.layers.squeeze(xs, axes=[1])
+        xs = paddle.squeeze(xs, axis=[1])
         xe = paddle.nn.functional.relu(self.te_conv1(x))
         xe = paddle.nn.functional.relu(self.te_conv2(xe))
-        xe = fluid.layers.squeeze(xe, axes=[1])
+        xe = paddle.squeeze(xe, axis=[1])
 
         # PEM
         xp = paddle.nn.functional.relu(self.p_conv1(x))
@@ -287,7 +287,7 @@ class BMN(fluid.dygraph.Layer):
         xp = paddle.reshape(xp, shape=[0, 0, -1, self.dscale, self.tscale])
 
         xp = self.p_conv3d1(xp)
-        xp = fluid.layers.squeeze(xp, axes=[2])
+        xp = paddle.squeeze(xp, axis=[2])
         xp = paddle.nn.functional.relu(self.p_conv2d1(xp))
         xp = paddle.nn.functional.relu(self.p_conv2d2(xp))
         xp = paddle.nn.functional.relu(self.p_conv2d3(xp))
@@ -411,11 +411,11 @@ def bmn_loss_func(
         loss = -1 * (loss_pos + loss_neg) / num_entries
         return loss
 
-    pred_bm_reg = fluid.layers.squeeze(
-        fluid.layers.slice(pred_bm, axes=[1], starts=[0], ends=[1]), axes=[1]
+    pred_bm_reg = paddle.squeeze(
+        fluid.layers.slice(pred_bm, axes=[1], starts=[0], ends=[1]), axis=[1]
     )
-    pred_bm_cls = fluid.layers.squeeze(
-        fluid.layers.slice(pred_bm, axes=[1], starts=[1], ends=[2]), axes=[1]
+    pred_bm_cls = paddle.squeeze(
+        fluid.layers.slice(pred_bm, axes=[1], starts=[1], ends=[2]), axis=[1]
     )
 
     bm_mask = _get_mask(cfg)

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

@@ -47,7 +47,7 @@ class TestBase(IPUOpTest):
         x = paddle.static.data(
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
-        out = paddle.fluid.layers.squeeze(x, **self.attrs)
+        out = paddle.squeeze(x, **self.attrs)
         self.fetch_list = [out.name]
 
     def run_model(self, exec_mode):

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

@@ -4097,7 +4097,7 @@ class TestBook(LayerTest):
         # TODO(minqiyang): dygraph do not support layers with param now
         with self.static_graph():
             x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
-            out = layers.squeeze(input=x, axes=[2])
+            out = paddle.squeeze(x, axis=[2])
             return out
 
     def test_flatten(self):

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

@@ -635,7 +635,7 @@ class RecurrentOpSubBlockTest(RecurrentOpTest1):
             new_h = layers.matmul(concat_in, w2)
             new_h = layers.unsqueeze(new_h, [1])
             new_h, _ = dot_attention(new_h, y)
-            new_h = layers.squeeze(new_h, [1])
+            new_h = paddle.squeeze(new_h, [1])
 
             rnn.update_memory(pre_h, new_h)
             rnn.step_output(new_h)

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

@@ -80,7 +80,7 @@ class DecoderCell(layers.RNNCell):
                 attn_scores, encoder_padding_mask
             )
         attn_scores = layers.softmax(attn_scores)
-        attn_out = layers.squeeze(
+        attn_out = paddle.squeeze(
             layers.matmul(attn_scores, encoder_output), [1]
         )
         attn_out = layers.concat([attn_out, hidden], 1)

python/paddle/fluid/tests/unittests/test_squeeze2_op.py

@@ -97,7 +97,7 @@ class TestSqueeze2AxesTensor(UnittestBase):
             # axes is a Variable
             axes = paddle.assign([0, 2])
             out = paddle.squeeze(feat, axes)
-            out2 = paddle.fluid.layers.squeeze(feat, axes)
+            out2 = paddle.squeeze(feat, axes)
 
             sgd = paddle.optimizer.SGD()
             sgd.minimize(paddle.mean(out))
@@ -136,7 +136,7 @@ class TestSqueeze2AxesTensorList(UnittestBase):
                 paddle.full([1], 2, dtype='int32'),
             ]
             out = paddle.squeeze(feat, axes)
-            out2 = paddle.fluid.layers.squeeze(feat, axes)
+            out2 = paddle.squeeze(feat, axes)
 
             sgd = paddle.optimizer.SGD()
             sgd.minimize(paddle.mean(out))