add layer norm to Layers, add transformer test in imperative mode (#16092)

* add layer norm to Layers, add transformer prepare encoding

* little change

* finish encoder part

* add decoder part

* finish model part

* add test case and part of data feed

* add transformer test

* add to_parameter, add remove in set_attr

* test=develop, fix pos encoding bug, create_parameter with stantard name

* test=develop, rm dropout test in imperative

* test=develop, fix cpu error

* test=develop, fix minize bug

* test=develop, fix one hot not stop gradient

* test=develop, fix one hot not stop gradient

* test=develop, refine parameter name

* test=develop, fix transformer test in imperative mode

* test=develop, fix transformer test in imperative mode

* test=develop, fix boost and mkl download error

* test=develop, fix boost and mkl download error

* test=develop, fix ci and refine code

* test=develop, fix ci and refine code

paddle/fluid/imperative/layer.cc

@@ -315,6 +315,9 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
       for (size_t i = 0; i < outputs.size(); ++i) {
         framework::Variable* grad = outputs[i]->var_;
         framework::Variable* orig_grad = origin_outputs[i]->var_;
+        VLOG(3) << "AddTo Called with orig_grad is: "
+                << origin_outputs[i]->name_ << " Grad to be added is "
+                << outputs[i]->name_;
         AddTo(grad, orig_grad, place_);
         delete grad;
       }

paddle/fluid/imperative/tracer.cc

@@ -277,6 +277,7 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
           VarBase* var = current_vars_map[var_it->second];
           InitGrad(var, prepared_op.GetDeviceContext());
           grad_out_vars.push_back(var->grads_);
+          VLOG(3) << "grads output var name: " << var->name_;
         }
       }
     }

python/paddle/dataset/wmt14.py

@@ -15,7 +15,7 @@
 WMT14 dataset.
 The original WMT14 dataset is too large and a small set of data for set is
 provided. This module will download dataset from
-http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and
+http://paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and
 parse training set and test set into paddle reader creators.
 
 """

python/paddle/fluid/imperative/base.py

@@ -44,7 +44,7 @@ def guard(place=None):
                     yield
 
 
-def to_variable(value, block=None):
+def to_variable(value, block=None, name=None):
     if isinstance(value, np.ndarray):
         assert enabled(), "to_variable could only be called in imperative mode"
 
@@ -53,7 +53,7 @@ def to_variable(value, block=None):
         py_var = framework.Variable(
             block,
             type=core.VarDesc.VarType.LOD_TENSOR,
-            name=None,
+            name=name,
             shape=value.shape,
             dtype=value.dtype)
         var = py_var._ivar.value()

python/paddle/fluid/imperative/layer_object_helper.py

@@ -105,6 +105,7 @@ class LayerObjectHelper(LayerHelperBase):
 
         Returns dtype of the input
         """
+        inputs_in = inputs_in if (inputs_in is not None) else []
         inputs = self._multiple_input(inputs_in)
         dtype = None
         for each in inputs:

python/paddle/fluid/imperative/layers.py

@@ -17,10 +17,12 @@ import contextlib
 import sys
 import numpy as np
 import collections
+import six
 from .. import unique_name
 from paddle.fluid import core
 from .layer_object_helper import LayerObjectHelper
 from paddle.fluid import framework
+from ..param_attr import ParamAttr
 
 __all__ = ['Layer', 'PyLayer']
 
@@ -72,6 +74,10 @@ class Layer(core.Layer):
 
         Returns created parameter Variable.
         """
+        if isinstance(attr, ParamAttr) and (attr.name is not None):
+            attr.name = ".".join([self._full_name, attr.name])
+        elif isinstance(attr, six.string_types):
+            attr = ".".join([self._full_name, attr])
         return self._helper.create_parameter(attr, shape, dtype, is_bias,
                                              default_initializer)
 
@@ -164,6 +170,7 @@ class Layer(core.Layer):
             the sublayer passed in.
         """
         assert isinstance(sublayer, core.Layer)
+
         self._sub_layers[name] = sublayer
         return sublayer
 

python/paddle/fluid/imperative/nn.py

@@ -20,10 +20,12 @@ from .. import core
 from ..layers import utils
 from . import layers
 from ..framework import Variable, OpProtoHolder
+from ..layers import layer_function_generator
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant
-
-__all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit']
+__all__ = [
+    'Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit', 'LayerNorm'
+]
 
 
 class Conv2D(layers.Layer):
@@ -438,7 +440,6 @@ class Embedding(layers.Layer):
         self._size = size
         self._is_sparse = is_sparse
         self._is_distributed = is_distributed
-
         self._padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
             size[0] + padding_idx)
 
@@ -471,6 +472,131 @@ class Embedding(layers.Layer):
         return out
 
 
+class LayerNorm(layers.Layer):
+    def __init__(self,
+                 name_scope,
+                 scale=True,
+                 shift=True,
+                 begin_norm_axis=1,
+                 epsilon=1e-05,
+                 param_attr=None,
+                 bias_attr=None,
+                 act=None):
+        """
+        ${comment}
+
+        The formula is as follows:
+
+        ..  math::
+
+            \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i
+
+            \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}
+
+            h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
+
+        * :math:`a`: the vector representation of the summed inputs to the neurons
+        in that layer.
+
+        * :math:`H`: the number of hidden units in a layers
+
+        * :math:`g`: the trainable scale parameter.
+
+        * :math:`b`: the trainable bias parameter.
+
+        Args:
+            input(Variable): The input tensor variable.
+            scale(bool): Whether to learn the adaptive gain :math:`g` after
+                normalization. Default True.
+            shift(bool): Whether to learn the adaptive bias :math:`b` after
+                normalization. Default True.
+            begin_norm_axis(int): The normalization will be performed along
+                dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
+                Default 1.
+            epsilon(float): The small value added to the variance to prevent
+                division by zero. Default 1e-05.
+            param_attr(ParamAttr|None): The parameter attribute for the learnable
+                gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
+                omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
+                a default :code:`ParamAttr` would be added as scale. The
+                :attr:`param_attr` is initialized as 1 if it is added. Default None.
+            bias_attr(ParamAttr|None): The parameter attribute for the learnable
+                bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
+                omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
+                a default :code:`ParamAttr` would be added as bias. The
+                :attr:`bias_attr` is initialized as 0 if it is added. Default None.
+            act(str): Activation to be applied to the output of layer normalizaiton.
+                      Default None.
+        Returns:
+            ${y_comment}
+
+        Examples:
+
+            >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
+            >>>                          dtype='float32')
+            >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
+        """
+
+        super(LayerNorm, self).__init__(name_scope)
+        self._scale = scale
+        self._shift = shift
+        self._begin_norm_axis = begin_norm_axis
+        self._epsilon = epsilon
+        self._param_attr = param_attr
+        self._bias_attr = bias_attr
+        self._act = act
+
+    def _build_once(self, input):
+        self._dtype = self._helper.input_dtype(input)
+        input_shape = input.shape
+        param_shape = [
+            reduce(lambda x, y: x * y, input_shape[self._begin_norm_axis:])
+        ]
+        if self._scale:
+            self._scale_w = self.create_parameter(
+                attr=self._param_attr,
+                shape=param_shape,
+                dtype=self._dtype,
+                default_initializer=Constant(1.0))
+        if self._shift:
+            assert self._bias_attr is not False
+            self._bias_w = self.create_parameter(
+                attr=self._bias_attr,
+                shape=param_shape,
+                dtype=self._dtype,
+                is_bias=True)
+
+    def forward(self, input):
+        inputs = dict()
+        inputs['X'] = input
+        if self._scale:
+            inputs['Scale'] = self._scale_w
+        if self._shift:
+            inputs['Bias'] = self._bias_w
+        # create output
+        mean_out = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        variance_out = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        layer_norm_out = self._helper.create_variable_for_type_inference(
+            self._dtype)
+
+        self._helper.append_op(
+            type="layer_norm",
+            inputs=inputs,
+            outputs={
+                "Y": layer_norm_out,
+                "Mean": mean_out,
+                "Variance": variance_out,
+            },
+            attrs={
+                "epsilon": self._epsilon,
+                "begin_norm_axis": self._begin_norm_axis
+            })
+
+        return self._helper.append_activation(layer_norm_out)
+
+
 class GRUUnit(layers.Layer):
     """
     **GRU unit layer**

python/paddle/fluid/layer_helper_base.py

@@ -268,11 +268,9 @@ class LayerHelperBase(object):
         """
         # Deepcopy the attr so that parameters can be shared in program
         attr = copy.deepcopy(attr)
-        if attr is None:
         attr = ParamAttr._to_attr(attr)
         if not attr:
             return None
-
         assert isinstance(attr, ParamAttr)
         suffix = 'b' if is_bias else 'w'
         if attr.name is None:

python/paddle/fluid/layers/nn.py

@@ -6206,7 +6206,8 @@ def one_hot(input, depth):
         type="one_hot",
         inputs={'X': input},
         attrs={'depth': depth},
-        outputs={'Out': one_hot_out})
+        outputs={'Out': one_hot_out},
+        stop_gradient=True)
     return one_hot_out
 
 

python/paddle/fluid/optimizer.py

@@ -397,6 +397,7 @@ class Optimizer(object):
             for param in parameters:
                 if not param.trainable:
                     continue
+                if param._ivar._grad_ivar() is not None:
                     # create gradient variable
                     grad_var = Variable(
                         block=loss.block,

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

@@ -70,6 +70,34 @@ class LayerTest(unittest.TestCase):
 
 
 class TestLayer(LayerTest):
+    def test_layer_norm(self):
+        inp = np.ones([3, 32, 32], dtype='float32')
+        with self.static_graph():
+            t = layers.data(
+                name='data',
+                shape=[3, 32, 32],
+                dtype='float32',
+                append_batch_size=False)
+            ret = layers.layer_norm(t)
+            static_ret = self.get_static_graph_result(
+                feed={'data': inp}, fetch_list=[ret])[0]
+        with self.static_graph():
+            t = layers.data(
+                name='data',
+                shape=[3, 32, 32],
+                dtype='float32',
+                append_batch_size=False)
+            lm = nn.LayerNorm('layer_norm')
+            ret = lm(t)
+            static_ret2 = self.get_static_graph_result(
+                feed={'data': inp}, fetch_list=[ret])[0]
+        with self.dynamic_graph():
+            lm = nn.LayerNorm('layer_norm')
+            dy_ret = lm(base.to_variable(inp))
+
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+        self.assertTrue(np.allclose(dy_ret._numpy(), static_ret2))
+
     def test_relu(self):
         with self.static_graph():
             t = layers.data(name='t', shape=[3, 3], dtype='float32')