[Fluid API] Remove shrink_memory, create_array & array_length

python/paddle/fluid/layers/control_flow.py

@@ -56,10 +56,8 @@
  'Switch',
  'increment',
  'array_write',
- 'create_array',
  'less_than',
  'array_read',
- 'array_length',
  'cond',
  'IfElse',
  'StaticRNN',
@@ -1712,7 +1710,7 @@ def array_write(x, i, array=None):
  ], "The shape of index 'i' should be [1] in dygraph mode"
  i = i.numpy().item(0)
  if array is None:
- array = create_array(x.dtype)
+ array = paddle.tensor.create_array(x.dtype)
  assert isinstance(
  array, list
  ), "The 'array' in array_write must be a list in dygraph mode"
@@ -1750,64 +1748,6 @@ def array_write(x, i, array=None):
  return array
 
 
-def create_array(dtype, initialized_list=None):
- """
- This OP creates an LOD_TENSOR_ARRAY. It is used as
- the input of :ref:`api_fluid_layers_array_read` and
- :ref:`api_fluid_layers_array_write`. Also it can be used
- with :ref:`api_fluid_layers_While` to create RNN network.
-
- Args:
- dtype (str): The data type of the elements in the lod_tensor_array.
- Support data type: float32, float64, int32, int64.
- initialized_list(list): Used to initialize as default value for created array.
- All values in initialized list should be a Tensor.
-
- Returns:
- Variable: The empty lod_tensor_array. The data type of elements in Tensor is ``dtype``.
-
- Examples:
- .. code-block:: python
-
- import paddle.fluid as fluid
- data = fluid.layers.create_array(dtype='float32') # Create a float32 LoDTensorArray.
-
- """
- array = []
- if initialized_list is not None:
- if not isinstance(initialized_list, (list, tuple)):
- raise TypeError(
- "Require type(initialized_list) should be list/tuple, but received {}".format(
- type(initialized_list)
- )
- )
- array = list(initialized_list)
-
- # NOTE: Only support plain list like [x, y,...], not support nested list in static mode.
- for val in array:
- if not isinstance(val, Variable):
- raise TypeError(
- "All values in `initialized_list` should be Variable, but recevied {}.".format(
- type(val)
- )
- )
-
- if _non_static_mode():
- return array
-
- helper = LayerHelper("array", **locals())
- tensor_array = helper.create_variable(
- name="{0}.out".format(helper.name),
- type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
- dtype=dtype,
- )
-
- for val in array:
- array_write(x=val, i=array_length(tensor_array), array=tensor_array)
-
- return tensor_array
-
-
 @templatedoc()
 def less_than(x, y, force_cpu=None, cond=None, name=None):
  """
@@ -1956,114 +1896,6 @@ def array_read(array, i):
  return out
 
 
-def shrink_memory(x, i, table):
- """
- This function creates an operator to shrink rnn memory using the RankTable
- as mentioned in the input parameter.
-
- NOTE: This API is very low-level API. It is used by DynamicRNN only.
-
- Since the Dynamic RNN uses no-padding way to implement RNN. The sequence
- will be sorted by order, and the length of valid memory will be shrink after
- each time step.
-
- Args:
- x(Variable): The memory object in the previous time step.
- i(Variable): The step count variable. A int scalar as LoDTensor.
- table(Variable): The RNNRankTable object.
-
- Returns:
- the memory variable after shrink.
-
- Examples:
-
- Since this API is very low level API. The example is not provided.
- Please reference the implementation of class DynamicRNN for detail
- usage.
- """
- helper = LayerHelper('shrink_memory', **locals())
- check_type(x, 'x', Variable, 'shrink_memory')
- check_type(i, 'i', Variable, 'shrink_memory')
- check_type(table, 'table', Variable, 'shrink_memory')
- out = helper.create_variable_for_type_inference(dtype=x.dtype)
- helper.append_op(
- type='shrink_rnn_memory',
- inputs={'X': [x], 'I': [i], 'RankTable': [table]},
- outputs={'Out': [out]},
- attrs={},
- )
- return out
-
-
-def array_length(array):
- """
- This OP is used to get the length of the input array :ref:`api_fluid_LoDTensorArray` .
- It can be used together with :ref:`api_fluid_layers_array_read` , :ref:`api_fluid_layers_array_write` ,
- :ref:`api_fluid_layers_While` OP to traverse, read and write LoDTensorArray.
-
- Args:
- array (LoDTensorArray): The input array that will be used to compute the length.
-
- Returns:
- Variable: 1-D Tensor with shape [1], which is the length of array. Datatype: int64.
-
- Examples:
- .. code-block:: python
-
- import paddle.fluid as fluid
- tmp = fluid.layers.zeros(shape=[10], dtype='int32')
- i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
- # tmp is 1-D Tensor with shape [10]. We write tmp into arr on subscript 10,
- # then the length of arr becomes 11.
- arr = fluid.layers.array_write(tmp, i=i)
- # return the length of arr
- arr_len = fluid.layers.array_length(arr)
-
- # You can use executor to print out the length of LoDTensorArray.
- input = fluid.layers.Print(arr_len, message="The length of LoDTensorArray:")
- main_program = fluid.default_main_program()
- exe = fluid.Executor(fluid.CPUPlace())
- exe.run(main_program)
-
- # The printed result is:
-
- # 1569576542 The length of LoDTensorArray: The place is:CPUPlace
- # Tensor[array_length_0.tmp_0]
- # shape: [1,]
- # dtype: l
- # data: 11,
-
- # 1-D Tensor with shape [1], whose value is 11. It means that the length of LoDTensorArray
- # is 11.
- # dtype is the corresponding C++ data type, which may vary in different environments.
- # Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
- # so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
- # and '__int64' on Windows. They both represent 64-bit integer variables.
- """
-
- if _non_static_mode():
- assert isinstance(
- array, list
- ), "The 'array' in array_write must be a list in dygraph mode"
- return len(array)
-
- if (
- not isinstance(array, Variable)
- or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY
- ):
- raise TypeError(
- "array should be tensor array vairable in array_length Op"
- )
-
- helper = LayerHelper('array_length', **locals())
- tmp = helper.create_variable_for_type_inference(dtype='int64')
- tmp.stop_gradient = True
- helper.append_op(
- type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]}
- )
- return tmp
-
-
 class ConditionalBlockGuard(BlockGuard):
  """
  ConditionalBlockGuard is derived from BlockGuard. It is dedicated for

python/paddle/fluid/layers/math_op_patch.py

@@ -18,7 +18,7 @@
 from .. import core
 from ..framework import Variable, unique_name, static_only
 from .layer_function_generator import OpProtoHolder
-from .control_flow import array_write, array_length
+from .control_flow import array_write
 from paddle.fluid.dygraph.base import in_declarative_mode
 
 _supported_int_dtype_ = [
@@ -246,6 +246,8 @@ def append(self, var):
  self.type
  )
  )
+ from paddle.tensor.array import array_length
+
  array_write(x=var, i=array_length(self), array=self)
 
  @static_only

python/paddle/fluid/layers/rnn.py

@@ -1638,7 +1638,7 @@ def _create_array_out_of_while(dtype):
  default_main_program().current_block_idx = (
  default_main_program().current_block().parent_idx
  )
- tensor_array = control_flow.create_array(dtype)
+ tensor_array = paddle.tensor.create_array(dtype)
  default_main_program().current_block_idx = current_block_idx
  return tensor_array
 

python/paddle/fluid/tests/unittests/collective/collective_sendrecv_op_array.py

@@ -49,7 +49,7 @@ def get_model(self, main_prog, startup_program):
  data2 = fluid.layers.assign(
  np.array([[0, 1, 2]], dtype='float32')
  )
- tensor_array = fluid.layers.create_array(dtype='float32')
+ tensor_array = paddle.tensor.create_array(dtype='float32')
  i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
  fluid.layers.array_write(data1, i, tensor_array)
  fluid.layers.array_write(data2, i + 1, tensor_array)

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

@@ -127,12 +127,12 @@ def test_hybrid_parallel_inference_helper_mp1pp2(self):
  layers.assign(layers.cast(cond_int, dtype='bool'), cond)
 
  with paddle.fluid.device_guard(f'{device}:all'):
- out = layers.create_array(data.dtype)
+ out = paddle.tensor.create_array(data.dtype)
  layers.assign(data, out)
 
  with paddle.fluid.device_guard(f'{device}:all'):
  # use a empty lod_tensor_array to clear lod_tensor_array
- layers.assign(layers.create_array(data.dtype), data)
+ layers.assign(paddle.tensor.create_array(data.dtype), data)
 
  helper = HybridParallelInferenceHelper(
  startup_program,

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

@@ -220,8 +220,8 @@ def forward(self, inputs):
  np.zeros((self.batch_size, self.hidden_size), dtype='float32')
  )
  zero = fluid.layers.zeros(shape=[1], dtype="int64")
- enc_hidden = fluid.layers.create_array(dtype="float32")
- enc_cell = fluid.layers.create_array(dtype="float32")
+ enc_hidden = paddle.tensor.create_array(dtype="float32")
+ enc_cell = paddle.tensor.create_array(dtype="float32")
  for i in range(self.num_layers):
  index = zero + i
  enc_hidden = fluid.layers.array_write(
@@ -322,8 +322,8 @@ def beam_search(self, inputs):
  np.zeros((self.batch_size, self.hidden_size), dtype='float32')
  )
  zero = fluid.layers.zeros(shape=[1], dtype="int64")
- enc_hidden = fluid.layers.create_array(dtype="float32")
- enc_cell = fluid.layers.create_array(dtype="float32")
+ enc_hidden = paddle.tensor.create_array(dtype="float32")
+ enc_cell = paddle.tensor.create_array(dtype="float32")
  for j in range(self.num_layers):
  index = zero + j
  enc_hidden = fluid.layers.array_write(
@@ -735,8 +735,8 @@ def forward(self, inputs):
  )
  enc_hidden_0.stop_gradient = True
  zero = fluid.layers.zeros(shape=[1], dtype="int64")
- enc_hidden = fluid.layers.create_array(dtype="float32")
- enc_cell = fluid.layers.create_array(dtype="float32")
+ enc_hidden = paddle.tensor.create_array(dtype="float32")
+ enc_cell = paddle.tensor.create_array(dtype="float32")
  for i in range(self.num_layers):
  index = zero + i
  enc_hidden = fluid.layers.array_write(

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

@@ -170,7 +170,7 @@ def set_program(self, use_fluid_api):
  self.program = fluid.Program()
  with fluid.program_guard(self.program):
  input = fluid.layers.assign(self.x)
- tensor_array = fluid.layers.create_array(dtype='float32')
+ tensor_array = paddle.tensor.create_array(dtype='float32')
  zero = fluid.layers.fill_constant(
  shape=[1], value=0, dtype="int64"
  )
@@ -183,7 +183,7 @@ def set_program(self, use_fluid_api):
  self.program = paddle.static.Program()
  with paddle.static.program_guard(self.program):
  input = paddle.assign(self.x)
- tensor_array = fluid.layers.create_array(
+ tensor_array = paddle.tensor.create_array(
  dtype='float32'
  ) # Api create_array is not supported in paddle 2.0 yet.
  zero = paddle.zeros(shape=[1], dtype="int64")

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

@@ -138,7 +138,7 @@ def set_program(self):
  self.program = fluid.Program()
  with fluid.program_guard(self.program):
  input = fluid.layers.assign(self.x)
- tensor_array = fluid.layers.create_array(dtype='float32')
+ tensor_array = paddle.tensor.create_array(dtype='float32')
  zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
 
  for i in range(self.iter_num):
@@ -176,7 +176,7 @@ def set_program(self):
  self.program = fluid.Program()
  with fluid.program_guard(self.program):
  input = fluid.layers.assign(self.x)
- tensor_array = fluid.layers.create_array(dtype='float32')
+ tensor_array = paddle.tensor.create_array(dtype='float32')
  zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
 
  for i in range(self.iter_num):

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

@@ -16,6 +16,7 @@
 
 import numpy as np
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.framework import Program, program_guard
@@ -124,7 +125,7 @@ def test_errors(self):
  def test_id_Variable():
  # the input pre_ids must be Variable
  test_ids = np.random.randint(1, 5, [5, 1]).astype("int64")
- scores = fluid.layers.create_array(dtype='float32')
+ scores = paddle.tensor.create_array(dtype='float32')
  fluid.layers.beam_search_decode(
  test_ids, scores, beam_size=5, end_id=0
  )
@@ -133,7 +134,7 @@ def test_id_Variable():
 
  def test_score_Variable():
  # the input pre_scores must be Variable
- ids = fluid.layers.create_array(dtype='int64')
+ ids = paddle.tensor.create_array(dtype='int64')
  test_scores = np.random.uniform(1, 5, [5, 1]).astype("float32")
  fluid.layers.beam_search_decode(
  ids, test_scores, beam_size=5, end_id=0
@@ -143,8 +144,8 @@ def test_score_Variable():
 
  def test_id_dtype():
  # the dtype of input pre_ids must be int64
- type_ids = fluid.layers.create_array(dtype='float32')
- scores = fluid.layers.create_array(dtype='float32')
+ type_ids = paddle.tensor.create_array(dtype='float32')
+ scores = paddle.tensor.create_array(dtype='float32')
  fluid.layers.beam_search_decode(
  type_ids, scores, beam_size=5, end_id=0
  )
@@ -153,8 +154,8 @@ def test_id_dtype():
 
  def test_score_dtype():
  # the dtype of input pre_scores must be float32
- ids = fluid.layers.create_array(dtype='int64')
- type_scores = fluid.layers.create_array(dtype='int64')
+ ids = paddle.tensor.create_array(dtype='int64')
+ type_scores = paddle.tensor.create_array(dtype='int64')
  fluid.layers.beam_search_decode(
  ids, type_scores, beam_size=5, end_id=0
  )

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

@@ -414,7 +414,7 @@ def set_program(self, use_fluid_api):
  self.program = fluid.Program()
  with fluid.program_guard(self.program):
  input = fluid.layers.assign(self.x)
- tensor_array = fluid.layers.create_array(dtype='float32')
+ tensor_array = paddle.tensor.create_array(dtype='float32')
  zero = fluid.layers.fill_constant(
  shape=[1], value=0, dtype="int64"
  )
@@ -427,7 +427,7 @@ def set_program(self, use_fluid_api):
  self.program = paddle.static.Program()
  with paddle.static.program_guard(self.program):
  input = paddle.assign(self.x)
- tensor_array = fluid.layers.create_array(
+ tensor_array = paddle.tensor.create_array(
  dtype='float32'
  ) # Api create_array is not supported in paddle 2.0 yet.
  zero = paddle.zeros(shape=[1], dtype="int64")