[Clean Fluid API]Remove API: log (#47966)

* replace log with paddle.log

* replace log with paddle.nn.functional.log

* fix the code style of remove_log

* fix the ImportError of log

* fix the error of modification of the dist_transformer.py

* fix error of Static-Check

python/paddle/distribution/normal.py

@@ -242,7 +242,7 @@ class Normal(distribution.Distribution):
         )
         return paddle.add(
             0.5 + zero_tmp,
-            0.5 * math.log(2 * math.pi) + nn.log((self.scale + zero_tmp)),
+            0.5 * math.log(2 * math.pi) + paddle.log((self.scale + zero_tmp)),
             name=name,
         )
 
@@ -260,7 +260,7 @@ class Normal(distribution.Distribution):
         value = self._check_values_dtype_in_probs(self.loc, value)
 
         var = self.scale * self.scale
-        log_scale = nn.log(self.scale)
+        log_scale = paddle.log(self.scale)
         return paddle.subtract(
             -1.0 * ((value - self.loc) * (value - self.loc)) / (2.0 * var),
             log_scale + math.log(math.sqrt(2.0 * math.pi)),
@@ -331,5 +331,5 @@ class Normal(distribution.Distribution):
         t1 = (self.loc - other.loc) / other.scale
         t1 = t1 * t1
         return paddle.add(
-            0.5 * var_ratio, 0.5 * (t1 - 1.0 - nn.log(var_ratio)), name=name
+            0.5 * var_ratio, 0.5 * (t1 - 1.0 - paddle.log(var_ratio)), name=name
         )

python/paddle/distribution/uniform.py

@@ -27,6 +27,8 @@ from paddle.fluid.layers import (
     nn,
     tensor,
 )
+
+import paddle
 from paddle.tensor import random
 
 
@@ -216,7 +218,7 @@ class Uniform(distribution.Distribution):
             if in_dygraph_mode():
                 lb = _C_ops.cast(lb_bool, value.dtype)
                 ub = _C_ops.cast(ub_bool, value.dtype)
-                return nn.log(lb * ub) - nn.log(self.high - self.low)
+                return paddle.log(lb * ub) - paddle.log(self.high - self.low)
 
             if _in_legacy_dygraph():
                 lb = _legacy_C_ops.cast(
@@ -225,7 +227,7 @@ class Uniform(distribution.Distribution):
                 ub = _legacy_C_ops.cast(
                     ub_bool, 'in_dtype', ub_bool.dtype, 'out_dtype', value.dtype
                 )
-                return nn.log(lb * ub) - nn.log(self.high - self.low)
+                return paddle.log(lb * ub) - paddle.log(self.high - self.low)
 
         name = self.name + '_log_prob'
         lb_bool = self.low < value
@@ -233,7 +235,7 @@ class Uniform(distribution.Distribution):
         lb = tensor.cast(lb_bool, dtype=value.dtype)
         ub = tensor.cast(ub_bool, dtype=value.dtype)
         return paddle.subtract(
-            nn.log(lb * ub), nn.log(self.high - self.low), name=name
+            paddle.log(lb * ub), paddle.log(self.high - self.low), name=name
         )
 
     def probs(self, value):
@@ -286,4 +288,4 @@ class Uniform(distribution.Distribution):
 
         """
         name = self.name + '_entropy'
-        return nn.log(self.high - self.low, name=name)
+        return paddle.log(self.high - self.low, name=name)

python/paddle/fluid/layers/distributions.py

@@ -264,7 +264,7 @@ class Uniform(Distribution):
         ub_bool = control_flow.less_than(value, self.high)
         lb = tensor.cast(lb_bool, dtype=value.dtype)
         ub = tensor.cast(ub_bool, dtype=value.dtype)
-        return nn.log(lb * ub) - nn.log(self.high - self.low)
+        return paddle.log(lb * ub) - paddle.log(self.high - self.low)
 
     def entropy(self):
         """Shannon entropy in nats.
@@ -273,7 +273,7 @@ class Uniform(Distribution):
           Variable: Shannon entropy of uniform distribution.The data type is float32.
 
         """
-        return nn.log(self.high - self.low)
+        return paddle.log(self.high - self.low)
 
 
 class Normal(Distribution):
@@ -412,7 +412,9 @@ class Normal(Distribution):
             self.loc + self.scale, batch_shape, self.loc.dtype, 0.0
         )
         return (
-            0.5 + 0.5 * math.log(2 * math.pi) + nn.log((self.scale + zero_tmp))
+            0.5
+            + 0.5 * math.log(2 * math.pi)
+            + paddle.log((self.scale + zero_tmp))
         )
 
     def log_prob(self, value):
@@ -430,7 +432,7 @@ class Normal(Distribution):
         )
 
         var = self.scale * self.scale
-        log_scale = nn.log(self.scale)
+        log_scale = paddle.log(self.scale)
         return (
             -1.0 * ((value - self.loc) * (value - self.loc)) / (2.0 * var)
             - log_scale
@@ -454,7 +456,7 @@ class Normal(Distribution):
         var_ratio = var_ratio * var_ratio
         t1 = (self.loc - other.loc) / other.scale
         t1 = t1 * t1
-        return 0.5 * (var_ratio + t1 - 1.0 - nn.log(var_ratio))
+        return 0.5 * (var_ratio + t1 - 1.0 - paddle.log(var_ratio))
 
 
 class Categorical(Distribution):
@@ -542,7 +544,8 @@ class Categorical(Distribution):
         other_z = paddle.sum(other_e_logits, axis=-1, keepdim=True)
         prob = e_logits / z
         kl = paddle.sum(
-            prob * (logits - nn.log(z) - other_logits + nn.log(other_z)),
+            prob
+            * (logits - paddle.log(z) - other_logits + paddle.log(other_z)),
             axis=-1,
             keepdim=True,
         )
@@ -562,7 +565,7 @@ class Categorical(Distribution):
 
         prob = e_logits / z
         entropy = -1.0 * paddle.sum(
-            prob * (logits - nn.log(z)), axis=-1, keepdim=True
+            prob * (logits - paddle.log(z)), axis=-1, keepdim=True
         )
 
         return entropy
@@ -687,7 +690,7 @@ class MultivariateNormalDiag(Distribution):
         """
         entropy = 0.5 * (
             self.scale.shape[0] * (1.0 + math.log(2 * math.pi))
-            + nn.log(self._det(self.scale))
+            + paddle.log(self._det(self.scale))
         )
 
         return entropy
@@ -710,7 +713,9 @@ class MultivariateNormalDiag(Distribution):
         )
         tri_matmul = nn.matmul(loc_matmul_cov, (other.loc - self.loc))
         k = list(self.scale.shape)[0]
-        ln_cov = nn.log(self._det(other.scale)) - nn.log(self._det(self.scale))
+        ln_cov = paddle.log(self._det(other.scale)) - paddle.log(
+            self._det(self.scale)
+        )
         kl = 0.5 * (tr_cov_matmul + tri_matmul - k + ln_cov)
 
         return kl

python/paddle/fluid/layers/nn.py

@@ -97,7 +97,6 @@ __all__ = [
     'resize_trilinear',
     'resize_nearest',
     'relu',
-    'log',
     'unique',
     'unique_with_counts',
     'elementwise_add',
@@ -5246,47 +5245,6 @@ def resize_nearest(
     )
 
 
-def log(x, name=None):
-    r"""
-    Calculates the natural log of the given input tensor, element-wise.
-
-    .. math::
-
-        Out = \\ln(x)
-
-    Args:
-        x (Tensor): Input Tensor. Must be one of the following types: float32, float64.
-        name (str|None): 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: The natural log of the input Tensor computed element-wise.
-
-    Examples:
-
-        .. code-block:: python
-
-            import paddle
-
-            x = [[2,3,4], [7,8,9]]
-            x = paddle.to_tensor(x, dtype='float32')
-            res = paddle.log(x)
-            # [[0.693147, 1.09861, 1.38629], [1.94591, 2.07944, 2.19722]]
-    """
-    if in_dygraph_mode():
-        return _C_ops.log(x)
-    if _in_legacy_dygraph():
-        return _legacy_C_ops.log(x)
-
-    check_variable_and_dtype(x, 'x', ['float32', 'float64'], "log")
-    inputs = {'X': [x]}
-    helper = LayerHelper('log', **locals())
-    dtype = helper.input_dtype(input_param_name='x')
-    out = helper.create_variable_for_type_inference(dtype)
-    helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out})
-    return out
-
-
 @deprecated(since="2.0.0", update_to="paddle.nn.functional.relu")
 def relu(x, name=None):
     """

python/paddle/fluid/layers/rnn.py

@@ -1304,7 +1304,7 @@ class BeamSearchDecoder(Decoder):
                 self.noend_mask_tensor, "float64"
             )
 
-        step_log_probs = nn.log(nn.softmax(logits))
+        step_log_probs = paddle.log(nn.softmax(logits))
         step_log_probs = self._mask_probs(step_log_probs, beam_state.finished)
         log_probs = nn.elementwise_add(
             x=step_log_probs, y=beam_state.log_probs, axis=0
@@ -3529,8 +3529,8 @@ def beam_search(
                 name='probs', shape=[None, 10000], dtype='float32')
             topk_scores, topk_indices = fluid.layers.topk(probs, k=beam_size)
             accu_scores = fluid.layers.elementwise_add(
-                x=fluid.layers.log(x=topk_scores),
-                y=fluid.layers.reshape(pre_scores, shape=[-1]),
+                x=paddle.log(x=topk_scores),
+                y=paddle.reshape(pre_scores, shape=[-1]),
                 axis=0)
             selected_ids, selected_scores = fluid.layers.beam_search(
                 pre_ids=pre_ids,

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

@@ -1837,7 +1837,7 @@ def fast_decode(
                 input=layers.softmax(logits), k=beam_size
             )
             accu_scores = layers.elementwise_add(
-                x=layers.log(topk_scores),
+                x=paddle.log(topk_scores),
                 y=paddle.reshape(pre_scores, shape=[-1]),
                 axis=0,
             )

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

@@ -435,9 +435,7 @@ class BaseModel(fluid.dygraph.Layer):
             cell_outputs = self._split_batch_beams(step_input)
             cell_outputs = self.fc(cell_outputs)
 
-            step_log_probs = fluid.layers.log(
-                fluid.layers.softmax(cell_outputs)
-            )
+            step_log_probs = paddle.log(fluid.layers.softmax(cell_outputs))
             noend_array = [-self.kinf] * self.tar_vocab_size
             noend_array[self.beam_end_token] = 0
             noend_mask_tensor = to_variable(

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

@@ -329,13 +329,11 @@ def bmn_loss_func(
             coef_0 = 0.5 * ratio / (ratio - 1)
             coef_1 = 0.5 * ratio
             epsilon = 0.000001
-            # temp = fluid.layers.log(pred_score + epsilon)
-            loss_pos = paddle.multiply(
-                fluid.layers.log(pred_score + epsilon), pmask
-            )
+            # temp = paddle.log(pred_score + epsilon)
+            loss_pos = paddle.multiply(paddle.log(pred_score + epsilon), pmask)
             loss_pos = coef_1 * fluid.layers.reduce_mean(loss_pos)
             loss_neg = paddle.multiply(
-                fluid.layers.log(1.0 - pred_score + epsilon), (1.0 - pmask)
+                paddle.log(1.0 - pred_score + epsilon), (1.0 - pmask)
             )
             loss_neg = coef_0 * fluid.layers.reduce_mean(loss_neg)
             loss = -1 * (loss_pos + loss_neg)
@@ -400,12 +398,10 @@ def bmn_loss_func(
         coef_0 = 0.5 * ratio / (ratio - 1)
         coef_1 = 0.5 * ratio
         epsilon = 0.000001
-        loss_pos = paddle.multiply(
-            fluid.layers.log(pred_score + epsilon), pmask
-        )
+        loss_pos = paddle.multiply(paddle.log(pred_score + epsilon), pmask)
         loss_pos = coef_1 * paddle.sum(loss_pos)
         loss_neg = paddle.multiply(
-            fluid.layers.log(1.0 - pred_score + epsilon), nmask
+            paddle.log(1.0 - pred_score + epsilon), nmask
         )
         loss_neg = coef_0 * paddle.sum(loss_neg)
         loss = -1 * (loss_pos + loss_neg) / num_entries

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

@@ -122,7 +122,7 @@ def train(args, place, to_static):
             mask = to_variable(_mask)
             mask.stop_gradient = True
 
-            loss_probs = fluid.layers.log(loss_probs)
+            loss_probs = paddle.log(loss_probs)
             loss_probs = paddle.multiply(loss_probs, mask)
             loss_probs = paddle.sum(loss_probs, axis=-1)
 

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

@@ -845,7 +845,7 @@ class Transformer(Layer):
             )
             caches = map_structure(split_batch_beams, caches)
             step_log_probs = split_batch_beams(
-                fluid.layers.log(fluid.layers.softmax(logits))
+                paddle.log(fluid.layers.softmax(logits))
             )
 
             step_log_probs = mask_probs(

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

@@ -2417,8 +2417,8 @@ class TestLog(TestActivation):
             name="in2", shape=[11, 17], append_batch_size=False, dtype="int64"
         )
 
-        self.assertRaises(TypeError, fluid.layers.log, in1)
-        self.assertRaises(TypeError, fluid.layers.log, in2)
+        self.assertRaises(TypeError, paddle.log, in1)
+        self.assertRaises(TypeError, paddle.log, in2)
 
 
 class TestLog_ZeroDim(TestLog):

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

@@ -314,7 +314,7 @@ class TestBeamSearchOpError(unittest.TestCase):
             probs = fluid.data(name='probs', shape=[10000], dtype='float32')
             topk_scores, topk_indices = fluid.layers.topk(probs, k=4)
             accu_scores = fluid.layers.elementwise_add(
-                x=fluid.layers.log(x=topk_scores),
+                x=paddle.log(x=topk_scores),
                 y=paddle.reshape(pre_scores, shape=[-1]),
                 axis=0,
             )

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

@@ -71,7 +71,7 @@ class TestImperativeMnist(unittest.TestCase):
             dy_mask = fluid.dygraph.base.to_variable(mask)
             dy_mask.stop_gradient = True
 
-            loss_probs = fluid.layers.log(loss_probs)
+            loss_probs = paddle.log(loss_probs)
             loss_probs = fluid.layers.elementwise_mul(loss_probs, dy_mask)
             loss_probs = paddle.sum(loss_probs, axis=-1)
 
@@ -139,7 +139,7 @@ class TestImperativeMnist(unittest.TestCase):
 
             st_loss_probs = policy(st_state)
 
-            st_loss_probs = fluid.layers.log(st_loss_probs)
+            st_loss_probs = paddle.log(st_loss_probs)
             st_loss_probs = fluid.layers.elementwise_mul(st_loss_probs, st_mask)
             st_loss_probs = paddle.sum(st_loss_probs, axis=-1)