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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import six
import paddle
from paddle.fluid import framework, backward, core
from paddle.fluid.dygraph import layers
from paddle.fluid.dygraph.base import switch_to_static_graph
from paddle.fluid.dygraph.dygraph_to_static import logging_utils
from paddle.fluid.dygraph.dygraph_to_static.return_transformer import RETURN_NO_VALUE_MAGIC_NUM
from paddle.fluid.layers.utils import flatten
from paddle.fluid.layers.utils import pack_sequence_as
from paddle.fluid.layers.utils import _hash_with_id
from paddle.fluid.compiler import BuildStrategy
import paddle.compat as cpt
from paddle import _C_ops
class NestSequence(object):
"""
A wrapper class that easily to flatten and restore the nest structure of
given sequence.
"""
def __init__(self, raw_input, need_check=False):
self.__raw_input = raw_input
self.__input_list = self.tolist()
self.__var_ids = self._get_var_ids()
self._check_non_variable(need_check)
def tolist(self):
"""
Flattens the nested sequences into single list.
"""
return flatten(self.__raw_input)
def restore(self, value_list):
"""
Restores the nested sequence from value list.
"""
assert len(self.__input_list) == len(value_list)
return pack_sequence_as(self.__raw_input, value_list)
def _get_var_ids(self):
var_ids = []
for idx, var in enumerate(self.__input_list):
if isinstance(var, (framework.Variable, core.VarBase)):
var_ids.append(idx)
return var_ids
def _check_non_variable(self, need_check):
"""
Raises warning if output of traced function contains non-tensor type values.
"""
if need_check:
warning_types = set()
for var in self.__input_list:
if not isinstance(var, (framework.Variable, core.VarBase)):
warning_types.add(type(var))
if warning_types:
logging_utils.warn(
"Output of traced function contains non-tensor type values: {}. "
"Currently, We don't support to update them while training and will return "
"what we first saw. Please try to return them as tensor.".
format(list(warning_types)))
@property
def var_ids(self):
return self.__var_ids
def __getitem__(self, item):
return self.__input_list[item]
class LazyInitialized(object):
"""
Descriptor to implement lazy initialization of property.
"""
def __init__(self, function):
self.function = function
def __get__(self, instance, cls):
val = self.function(instance)
setattr(instance, self.function.__name__, val)
return val
def _change_is_test_status(program, is_test):
# change all `is_test` attributes
for block in program.blocks:
for op in block.ops:
if op.has_attr('is_test'):
op._set_attr('is_test', is_test)
return program
class PartialProgramLayer:
"""
PartialProgramLayer wraps all the ops from layers decorated by `@declarative`
and execute them as a static subgraph.
.. note::
**1. This is a very low level API. Users should not use this API
directly. Please use `partial_program_from(concrete_program)`
to create it.
**2. LoDTensorArray is not currently supported in the output.
Args:
main_program(Program): The main program that contains ops need to be executed.
inputs(list[Variable]): The input list of the decorated function by `@declarative`.
outputs(list[Variable]): The output list of the decorated function by `@declarative`.
parameters(list[VarBase]|None): All trainable parameters included in the program. Default None.
Returns:
Layer: A Layer object that run all ops internally in static mode.
"""
def __init__(self, main_program, inputs, outputs, parameters=None,
**kwargs):
super(PartialProgramLayer, self).__init__()
self._inputs = NestSequence(inputs)
self._outputs = NestSequence(outputs, need_check=True)
self._params = parameters if parameters is not None else []
self._build_strategy = kwargs.get('build_strategy', BuildStrategy())
assert isinstance(self._build_strategy, BuildStrategy)
self._origin_main_program = self._verify_program(main_program)
self._tmp_scope_vec = self._create_scope_vec()
# A fake_var to handle empty input or output
self.__fake_vars = _create_fake_var()
# Set default mode to train
self._double_grads = self._get_double_grads(self._origin_main_program)
self.training = True
@LazyInitialized
def _infer_program(self):
"""
Lazy initialized property of infer_program.
"""
return self._clone_for_test(self._origin_main_program)
@LazyInitialized
def _train_program(self):
"""
Lazy initialized property of train_program.
"""
train_program = self._append_backward_desc(self._origin_main_program)
# Note: Only set grad type once after initializing train program. So we
# put it here.
self._set_grad_type(self._params, train_program)
return train_program
@LazyInitialized
def _infer_program_id(self):
return _hash_with_id(self._infer_program, self)
@LazyInitialized
def _train_program_id(self):
program_id = _hash_with_id(self._train_program, self)
core._set_cached_executor_build_strategy(program_id,
self._build_strategy)
return program_id
def _verify_program(self, main_program):
"""
Verify that the program parameter is initialized, prune some unused params,
and remove redundant op callstack.
"""
# 1. Check all params from main program can be found in self._params
self._check_params_all_inited(main_program)
# 2. Prune the parameters not used anywhere in the program.
self._prune_unused_params(main_program)
return main_program
@switch_to_static_graph
def _append_backward_desc(self, main_program):
# make sure all status of is_test are False in train mode.
program = _change_is_test_status(main_program.clone(), is_test=False)
targets = []
for out in self._outputs.tolist():
if isinstance(out, framework.Variable):
targets.append(program.global_block().var(out.name))
if targets and self._params:
backward.gradients(targets=targets, inputs=[])
return program
def _prune_unused_params(self, program):
"""
Prune the parameters not used anywhere in the program.
The `@declarative` may only decorated a sub function which
contains some unused parameters created in `__init__`.
So prune these parameters to avoid unnecessary operations in
`run_program_op`.
"""
required_params = []
for param in self._params:
found_param = False
for block in program.blocks:
for op in block.ops:
if param.name in op.input_arg_names or param.name in op.output_arg_names:
required_params.append(param)
found_param = True
break
if found_param:
break
self._params = required_params
def _get_double_grads(self, program):
double_grads = []
for block in program.blocks:
for name in block.vars:
if "@GRAD" in name:
var_desc = block.vars[name].desc
var_base = core.VarBase(var_desc.dtype(),
var_desc.shape(),
var_desc.name(),
var_desc.type(), False)
double_grads.append(var_base)
return self._valid_vars(double_grads)
def __call__(self, inputs):
in_vars, out_vars = self._prepare(inputs)
attrs = ('global_block', self.program.desc.block(0), 'start_op_index',
0, 'end_op_index', self._infer_program.desc.block(0).op_size(),
'is_test', not self.training, 'program_id', self.program_id)
_C_ops.run_program(
self._valid_vars(in_vars),
self._valid_vars(self._params),
self._valid_vars(out_vars), self._tmp_scope_vec, self._double_grads,
*attrs)
restored_nest_out = self._restore_out(out_vars)
return self._remove_no_value(restored_nest_out)
@property
def program(self):
return self._train_program if self.training else self._infer_program
@property
def program_id(self):
return self._train_program_id if self.training else self._infer_program_id
def _prepare(self, inputs):
"""
Prepare inputs, outputs, attrs.
"""
assert isinstance(inputs, (tuple, list))
# Flatten inputs with nested structure into single list.
flatten_inputs = flatten(inputs)
# Convert variable into VarBase and feed in training data.
input_vars = []
expected_place = framework._current_expected_place()
for i, value in enumerate(flatten_inputs):
if isinstance(value, np.ndarray):
var = core.VarBase(
value=value,
name=self._inputs[i].desc.name(),
persistable=False,
place=expected_place,
zero_copy=True)
elif isinstance(value, core.VarBase):
# NOTE(Aurelius84): If var is on CPUPlace, it will be transformed multi times
# into CUDAPlace when it's as input of multi Ops. so we move it in advance
# to avoid this problem.
if value.stop_gradient and not value.place._equals(
expected_place):
var = value._copy_to(expected_place, False)
var.stop_gradient = True
else:
var = value
var.name = self._inputs[i].desc.name()
else:
continue
input_vars.append(var)
def create_out(var_id):
var = self._outputs[var_id]
assert isinstance(var, framework.Variable)
var_desc = var.desc
var_base = core.VarBase(var_desc.dtype(),
var_desc.shape(),
var_desc.name(), var_desc.type(), False)
return var_base
# Create VarBase to receive output data.
out_vars = list(map(create_out, self._outputs.var_ids))
return input_vars, out_vars
def _create_scope_vec(self):
# Hold forward variables
tmp_scope_vec = core.VarBase(core.VarDesc.VarType.FP32, [],
"program_out_scope",
core.VarDesc.VarType.STEP_SCOPES, True)
inner_scope = core.Scope()
tmp_scope_vec.value().set_scope(inner_scope)
return tmp_scope_vec
def _restore_out(self, out_vars):
"""
Restores same nested outputs by only replacing the Variable with VarBase.
"""
flatten_outputs = self._outputs.tolist()
for i, idx in enumerate(self._outputs.var_ids):
flatten_outputs[idx] = out_vars[i]
outs = self._outputs.restore(flatten_outputs)
if outs is not None and len(outs) == 1:
outs = outs[0]
return outs
@switch_to_static_graph
def _clone_for_test(self, main_program):
return main_program.clone(for_test=True)
def _is_no_value(self, var):
if isinstance(var, core.VarBase) and var.shape == [1]:
# NOTE: .numpy() will insert MemcpySync operation, it hits performance.
if var.numpy()[0] == RETURN_NO_VALUE_MAGIC_NUM:
return True
return False
def _remove_no_value(self, out_vars):
"""
Removes invalid value for various-length return statement
"""
if isinstance(out_vars, core.VarBase):
if self._is_no_value(out_vars):
return None
return out_vars
elif isinstance(out_vars, (tuple, list)):
if isinstance(out_vars, tuple):
res = tuple(
var for var in out_vars if not self._is_no_value(var))
else:
# isinstance(out_vars, list)
res = [var for var in out_vars if not self._is_no_value(var)]
has_removed = (len(out_vars) > len(res))
# len(out_vars) > len(res) means we have removed var. This is
# preventing out_vars is empty or just one element at the beginning
if len(res) == 0 and has_removed:
return None
elif len(res) == 1 and has_removed:
return res[0]
return res
return out_vars
def _set_grad_type(self, params, train_program):
# NOTE: if user set sparse gradient mode, the param's gradient
# will be SelectedRows, not LoDTensor. But tracer will just
# set param grad VarBase by forward VarBase(LoDTensor)
# If we don't change grad_var type here, RunProgramOp need
# transform SelectedRows to LoDTensor forcibly, it may not
# be user wanted result.
for param in params:
grad_name = param.name + core.grad_var_suffix()
grad_var = train_program.desc.block(0).find_var(
cpt.to_bytes(grad_name))
# NOTE: cannot find var desc maybe no problem, such as in batch_norm
if grad_var is None:
continue
param._set_grad_type(grad_var.type())
def _remove_op_call_stack(self, main_program):
"""
Remove op's python call stack with redundant low-level error messages related to
transforamtions to avoid confusing users.
"""
assert isinstance(main_program, framework.Program)
for block in main_program.blocks:
for op in block.ops:
if op.has_attr("op_callstack"):
op._remove_attr("op_callstack")
return main_program
def _check_params_all_inited(self, main_program):
"""
Check all params from main program are already initialized, see details as follows:
1. all parameters in self._params should be type `framework.ParamBase` which are created in dygraph.
2. all parameters from transformed program can be found in self._params.
Because they share same data with ParamBase of original dygraph.
"""
if not isinstance(self._params, (list, tuple)):
raise TypeError(
"Type of self._params in PartialProgramLayer should be list or tuple, but received %s."
% type(self._params))
param_and_buffer_names_set = set()
for i, var in enumerate(self._params):
# self._params constains parameters and buffers with persistable=True.
if not isinstance(var, core.VarBase):
raise TypeError(
'Type of self._params[{}] in PartialProgramLayer should be Parameter or Variable, but received {}.'.
format(i, type(var)))
param_and_buffer_names_set.add(var.name)
for block in main_program.blocks:
for name, var in six.iteritems(block.vars):
if isinstance(var, framework.Parameter):
if name not in param_and_buffer_names_set:
raise ValueError(
"\n\tWe don't support to define layer with parameters in the function "
"decorated by `@declarative`.\n\tBecause that will re-defined parameters "
"every time when you run the function.\n\t"
"But we found parameter(%s) was created in the decorated function.\n\t"
"Please define the layer with parameters in `__init__` function."
% name)
def _valid_vars(self, vars):
"""
Note: run_program_op.InferShape requires `X`/'Out' not be null.
But it's common in dy2static, fake varBase is created to handle the
problem.
"""
return vars if vars else self.__fake_vars
def _create_fake_var():
"""
Create a fake_var (force on CPU) to handle empty input or output
"""
return [
core.VarBase(core.VarDesc.VarType.FP32, [], "Fake_var",
core.VarDesc.VarType.RAW, False)
]
def partial_program_from(concrete_program):
inputs = concrete_program.inputs
if inputs and isinstance(inputs[0], layers.Layer):
inputs = inputs[1:]
return PartialProgramLayer(
concrete_program.main_program, inputs, concrete_program.outputs,
concrete_program.parameters, **concrete_program.kwargs)