Make layers as a python module (#6564)

* Make cast op support bool

Also add `elemwise_sub/mul/abs/clip` layers

* Make fuild.layers as a module

* Move layers as a module

* Split layers.py into layers module

* Fix CI

* Fix CI

python/paddle/v2/fluid/layers/__init__.py

+import ops
+from ops import *
+import nn
+from nn import *
+import io
+from io import *
+import tensor
+from tensor import *
+import control_flow
+from control_flow import *
+
+__all__ = []
+__all__ += nn.__all__
+__all__ += io.__all__
+__all__ += tensor.__all__
+__all__ += control_flow.__all__
+__all__ += ops.__all__

python/paddle/v2/fluid/layers/io.py

+from .. import core
+from ..layer_helper import LayerHelper
+
+__all__ = ['data']
+
+
+def data(name,
+         shape,
+         append_batch_size=True,
+         dtype='float32',
+         lod_level=0,
+         type=core.VarDesc.VarType.LOD_TENSOR,
+         main_program=None,
+         startup_program=None,
+         stop_gradient=True):
+    """
+    Data Layer.
+
+    Args:
+       name: The name/alias of the function
+       shape: Tuple declaring the shape.
+       append_batch_size: Whether or not to append the data as a batch.
+       dtype: The type of data : float32, float_16, int etc
+       type: The output type. By default it is LOD_TENSOR.
+       lod_level(int): The LoD Level. 0 means the input data is not a sequence.
+       main_program: Name of the main program that calls this
+       startup_program: Name of the startup program
+       stop_gradient: A boolean that mentions whether gradient should flow.
+
+    This function takes in input and based on whether data has
+    to be returned back as a minibatch, it creates the global variable using
+    the helper functions. The global variables can be accessed by all the
+    following operations and layers in the graph.
+
+    All the input variables of this function are passed in as local variables
+    to the LayerHelper constructor.
+
+    """
+    helper = LayerHelper('data', **locals())
+    shape = list(shape)
+    for i in xrange(len(shape)):
+        if shape[i] is None:
+            shape[i] = -1
+            append_batch_size = False
+        elif shape[i] < 0:
+            append_batch_size = False
+
+    if append_batch_size:
+        shape = [-1] + shape  # append batch size as -1
+
+    return helper.create_global_variable(
+        name=name,
+        shape=shape,
+        dtype=dtype,
+        type=type,
+        stop_gradient=stop_gradient,
+        lod_level=lod_level)

python/paddle/v2/fluid/layers/ops.py

+from ..registry import register_layer
+__all__ = [
+    'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose',
+    'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
+    'elementwise_sub', 'elementwise_mul', 'clip', 'abs'
+]
+
+for _OP in set(__all__):
+    globals()[_OP] = register_layer(_OP)

python/paddle/v2/fluid/layers/tensor.py

+from ..layer_helper import LayerHelper
+
+__all__ = [
+    'create_tensor', 'cast', 'concat', 'sums', 'assign',
+    'fill_constant_batch_size_like', 'fill_constant', 'ones', 'zeros'
+]
+
+
+def create_tensor(dtype, name=None, main_program=None, startup_program=None):
+    helper = LayerHelper("create_tensor", **locals())
+    return helper.create_variable(name=helper.name, dtype=dtype)
+
+
+def cast(x, dtype, main_program=None):
+    """
+    This function takes in the input with input_dtype
+    and casts it to the output_dtype as the output.
+    """
+    helper = LayerHelper('cast', **locals())
+    out = helper.create_tmp_variable(dtype=dtype)
+    helper.append_op(
+        type='cast',
+        inputs={'X': [x]},
+        outputs={'Out': [out]},
+        attrs={'in_dtype': x.dtype,
+               'out_dtype': out.dtype})
+    return out
+
+
+def concat(input, axis, main_program=None, startup_program=None):
+    """
+    This function concats the input along the axis mentioned
+    and returns that as the output.
+    """
+    helper = LayerHelper('concat', **locals())
+    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(
+        type='concat',
+        inputs={'X': input},
+        outputs={'Out': [out]},
+        attrs={'axis': axis})
+    return out
+
+
+def sums(input, out=None, main_program=None, startup_program=None):
+    """
+    This function takes in the input and performs the sum operation on it
+    and returns that as the output.
+    """
+    helper = LayerHelper('sum', **locals())
+    if out is None:
+        out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
+    return out
+
+
+def assign(input, output, main_program=None, startup_program=None):
+    helper = LayerHelper('assign', **locals())
+    helper.append_op(
+        type='scale',
+        inputs={'X': [input]},
+        outputs={'Out': [output]},
+        attrs={'scale': 1.0})
+    return output
+
+
+def fill_constant(shape,
+                  dtype,
+                  value,
+                  out=None,
+                  main_program=None,
+                  startup_program=None):
+    """
+    This function creates a tensor , with shape as mentioned in the input and
+    specified dtype and fills this up with a constant value that
+    comes in the input. It also sets the stop_gradient to be True.
+    """
+    helper = LayerHelper("fill_constant", **locals())
+    if out is None:
+        out = helper.create_tmp_variable(dtype=dtype)
+    helper.append_op(
+        type='fill_constant',
+        inputs={},
+        outputs={'Out': [out]},
+        attrs={'shape': shape,
+               'dtype': out.dtype,
+               'value': float(value)})
+    out.stop_gradient = True
+    return out
+
+
+def fill_constant_batch_size_like(input,
+                                  shape,
+                                  dtype,
+                                  value,
+                                  input_dim_idx=0,
+                                  output_dim_idx=0,
+                                  main_program=None,
+                                  startup_program=None):
+    helper = LayerHelper("fill_constant_batch_size_like", **locals())
+    out = helper.create_tmp_variable(dtype=dtype)
+    helper.append_op(
+        type='fill_constant_batch_size_like',
+        inputs={'Input': input},
+        outputs={'Out': [out]},
+        attrs={
+            'shape': shape,
+            'dtype': out.dtype,
+            'value': float(value),
+            'input_dim_idx': input_dim_idx,
+            'output_dim_idx': output_dim_idx
+        })
+    out.stop_gradient = True
+    return out
+
+
+def ones(shape, dtype, main_program=None):
+    """
+    This function performs the same function as fill_constant() declared above
+    with the constant value being 1.0.
+    """
+    return fill_constant(value=1.0, **locals())
+
+
+def zeros(shape, dtype, main_program=None):
+    """
+    This function performs the same function as fill_constant() declared above
+    with the constant value being 0.0.
+    """
+    return fill_constant(value=0.0, **locals())

python/paddle/v2/fluid/tests/book/test_image_classification_train.py

 from __future__ import print_function
 
-import numpy as np
+import sys
+
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
-import sys
 
 
 def resnet_cifar10(input, depth=32):

python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py

 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+from paddle.v2.fluid.layer_helper import LayerHelper
+
+
+def lstm(x,
+         c_pre_init,
+         hidden_dim,
+         forget_bias=None,
+         main_program=None,
+         startup_program=None):
+    """
+    This function helps create an operator for the LSTM (Long Short Term
+    Memory) cell that can be used inside an RNN.
+    """
+    helper = LayerHelper('lstm_unit', **locals())
+    rnn = fluid.layers.StaticRNN()
+    with rnn.step():
+        c_pre = rnn.memory(init=c_pre_init)
+        x_t = rnn.step_input(x)
+
+        before_fc = fluid.layers.concat(
+            input=[x_t, c_pre],
+            axis=1,
+            main_program=main_program,
+            startup_program=startup_program)
+        after_fc = fluid.layers.fc(input=before_fc,
+                                   size=hidden_dim * 4,
+                                   main_program=main_program,
+                                   startup_program=startup_program)
+
+        dtype = x.dtype
+        c = helper.create_tmp_variable(dtype)
+        h = helper.create_tmp_variable(dtype)
+
+        helper.append_op(
+            type='lstm_unit',
+            inputs={"X": after_fc,
+                    "C_prev": c_pre},
+            outputs={"C": c,
+                     "H": h},
+            attrs={"forget_bias": forget_bias})
+
+        rnn.update_memory(c_pre, c)
+        rnn.output(h)
+
+    return rnn()
 
 
 def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
@@ -23,8 +68,7 @@ def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
     c_pre_init = fluid.layers.fill_constant(
         dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
     c_pre_init.stop_gradient = False
-    layer_1_out = fluid.layers.lstm(
-        emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
+    layer_1_out = lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
     layer_1_out = fluid.layers.transpose(x=layer_1_out, axis=[1, 0, 2])
 
     prediction = fluid.layers.fc(input=layer_1_out,

python/setup.py.in

@@ -68,6 +68,7 @@ packages=['paddle',
           'paddle.v2.plot',
           'paddle.v2.fluid',
           'paddle.v2.fluid.proto',
+          'paddle.v2.fluid.layers',
           'py_paddle']
 
 with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f: