framework.py 115.6 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# 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
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13 14
# 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.

15 16
from __future__ import print_function

Y
Yu Yang 已提交
17
import collections
X
Xin Pan 已提交
18
from collections import defaultdict
W
WangZhen 已提交
19
from collections import Iterable
Q
qiaolongfei 已提交
20
import contextlib
S
rename  
sneaxiy 已提交
21
from .wrapped_decorator import signature_safe_contextmanager
P
peizhilin 已提交
22
import os
F
fengjiayi 已提交
23
import re
24
import traceback
25
import six
26

Y
Yu Yang 已提交
27
import numpy as np
28
import subprocess
S
sneaxiy 已提交
29
import multiprocessing
Q
qiaolongfei 已提交
30

M
minqiyang 已提交
31
from .. import compat as cpt
32
from .proto import framework_pb2
33
try:
P
peizhilin 已提交
34
    if os.name == 'nt':
P
peizhilin 已提交
35
        import sys
P
peizhilin 已提交
36 37 38 39 40
        third_lib_path = os.path.abspath(os.path.dirname(
            __file__)) + os.sep + '..' + os.sep + 'libs'
        os.environ['path'] += ';' + third_lib_path
        sys.path.append(third_lib_path)

41
    from . import core
42
except ImportError as e:
P
peizhilin 已提交
43
    if os.name == 'nt':
44
        executable_path = os.path.abspath(os.path.dirname(sys.executable))
P
peizhilin 已提交
45
        raise ImportError(
46 47 48 49 50
            """NOTE: You may need to run \"set PATH=%s;%%PATH%%\"
        if you encounters \"DLL load failed\" errors. If you have python
        installed in other directory, replace \"%s\" with your own
        directory. The original error is: \n %s""" %
            (executable_path, executable_path, cpt.get_exception_message(e)))
P
peizhilin 已提交
51 52 53 54 55 56
    else:
        raise ImportError(
            """NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
        if you encounters \"libmkldnn.so not found\" errors. If you have python
        installed in other directory, replace \"/usr/local/lib\" with your own
        directory. The original error is: \n""" + cpt.get_exception_message(e))
57
except Exception as e:
58
    raise e
59
from . import unique_name
Y
Yu Yang 已提交
60

61
__all__ = [
62 63 64 65
    'Program',
    'default_startup_program',
    'default_main_program',
    'program_guard',
66
    'name_scope',
S
sneaxiy 已提交
67 68 69
    'cuda_places',
    'cpu_places',
    'cuda_pinned_places',
70
]
Y
Yu Yang 已提交
71

Q
qiaolongfei 已提交
72 73 74 75
EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
W
Wu Yi 已提交
76 77
CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()

L
lujun 已提交
78 79
_dygraph_tracer_ = None
_dygraph_current_expected_place_ = None
80 81


L
lujun 已提交
82 83
def _in_dygraph_mode():
    return _dygraph_tracer_ is not None
84 85


L
lujun 已提交
86 87
def _dygraph_tracer():
    return _dygraph_tracer_
88

W
Wu Yi 已提交
89

M
minqiyang 已提交
90
def _current_expected_place():
L
lujun 已提交
91
    return _dygraph_current_expected_place_
M
minqiyang 已提交
92 93


S
sneaxiy 已提交
94 95 96 97 98
def _cpu_num():
    return int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))


def cuda_places(device_ids=None):
S
add doc  
sneaxiy 已提交
99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
    '''
    Create a list of :code:`fluid.CUDAPlace` objects.

    If :code:`device_ids` is None, environment variable of
    :code:`FLAGS_selected_gpus` would be checked first. If
    :code:`FLAGS_selected_gpus=0,1,2`, the returned list would
    be [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
    If :code:`FLAGS_selected_gpus` is not set, all visible
    gpu places would be returned.  

    If :code:`device_ids` is not None, it should be the device
    ids of gpus. For example, if :code:`device_ids=[0,1,2]`, 
    the returned list would be 
    [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
    
    Args: 
        device_ids (None|list(int)|tuple(int)): gpu device id list.

    Returns:
        out (list(fluid.CUDAPlace)): gpu place list.
    '''
S
sneaxiy 已提交
120 121 122 123 124 125 126 127 128 129 130 131 132 133
    assert core.is_compiled_with_cuda(), \
        "Not compiled with CUDA"
    if device_ids is None:
        gpus_env = os.getenv("FLAGS_selected_gpus")
        if gpus_env:
            device_ids = [int(s) for s in gpus_env.split(",")]
        else:
            device_ids = six.moves.range(core.get_cuda_device_count())
    elif not isinstance(device_ids, (list, tuple)):
        device_ids = [device_ids]
    return [core.CUDAPlace(dev_id) for dev_id in device_ids]


def cpu_places(device_count=None):
S
add doc  
sneaxiy 已提交
134 135 136 137 138 139 140 141 142 143 144 145 146 147
    '''
    Create a list of :code:`fluid.CPUPlace` objects.
    
    If :code:`device_count` is None, the device count would
    be determined by environment variable :code:`CPU_NUM`. 
    If :code:`CPU_NUM` is not set, the device count would
    be determined by :code:`multiprocessing.cpu_count()`. 

    Args:
        device_count (None|int): device number.

    Returns:
        out (list(fluid.CPUPlace)): cpu place list.
    '''
S
sneaxiy 已提交
148 149 150 151 152 153
    if device_count is None:
        device_count = _cpu_num()
    return [core.CPUPlace()] * device_count


def cuda_pinned_places(device_count=None):
S
add doc  
sneaxiy 已提交
154 155 156 157 158 159 160 161 162 163 164 165 166 167
    '''
    Create a list of :code:`fluid.CUDAPinnedPlace` objects.

    If :code:`device_count` is None, the device count would
    be determined by environment variable :code:`CPU_NUM`. 
    If :code:`CPU_NUM` is not set, the device count would
    be determined by :code:`multiprocessing.cpu_count()`. 

    Args:
        device_count (None|int): device number.

    Returns:
        out (list(fluid.CUDAPinnedPlace)): cuda pinned place list.
    '''
S
sneaxiy 已提交
168 169 170 171 172 173 174
    assert core.is_compiled_with_cuda(), \
        "Not compiled with CUDA"
    if device_count is None:
        device_count = _cpu_num()
    return [core.cuda_pinned_places()] * device_count


175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
class NameScope(object):
    def __init__(self, name="", parent=None):
        self._children = dict()
        self._name = name
        self._parent = parent

    def child(self, prefix):
        if prefix not in self._children:
            new_child = NameScope(prefix, self)
            self._children[prefix] = [new_child]
        else:
            new_child = NameScope(prefix + "_%d" % len(self._children[prefix]),
                                  self)
            self._children[prefix].append(new_child)
        return new_child

    def parent(self):
        return self._parent

    def name(self):
        return self._name


_name_scope = NameScope()


S
rename  
sneaxiy 已提交
201
@signature_safe_contextmanager
202 203 204 205 206 207 208 209 210 211 212 213
def name_scope(prefix=None):
    """
    Generate hierarchical name prefix for the operators.

    Note: This should only used for debugging and visualization purpose.
    Don't use it for serious analysis such as graph/program transformations.

    Args:
        prefix(str): prefix.

    Examples:
        .. code-block:: python
T
Tink_Y 已提交
214

215 216 217 218
          with name_scope("encoder"):
             ...
          with name_scope("decoder"):
             ...
T
Tink_Y 已提交
219 220
          with name_scope("attention"):
             ...
221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
    """
    # TODO(panyx0718): Only [0-9a-z].
    assert prefix, "namescope prefix cannot be empty."
    global _name_scope
    _name_scope = _name_scope.child(prefix)
    yield
    _name_scope = _name_scope.parent()


def _full_name_scope():
    global _name_scope
    scope = _name_scope
    name = ""
    while scope:
        name = scope.name() + "/" + name
        scope = scope.parent()
    return name


W
Wu Yi 已提交
240 241 242
def generate_control_dev_var_name():
    import random
    return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random())
Q
qiaolongfei 已提交
243 244 245 246


def grad_var_name(var_name):
    """
247 248
    Returns:
        str: gradient name for a certain var name
Q
qiaolongfei 已提交
249 250 251
    """
    return var_name + GRAD_VAR_SUFFIX

Y
Yu Yang 已提交
252

253
def convert_np_dtype_to_dtype_(np_dtype):
254 255
    """
    Convert the data type in numpy to the data type in Paddle
256

257
    Args:
258
        np_dtype(np.dtype): the data type in numpy.
259

260 261
    Returns:
        core.VarDesc.VarType: the data type in Paddle.
262 263

    """
264 265
    dtype = np.dtype(np_dtype)
    if dtype == np.float32:
266
        return core.VarDesc.VarType.FP32
267
    elif dtype == np.float64:
268
        return core.VarDesc.VarType.FP64
269
    elif dtype == np.float16:
270
        return core.VarDesc.VarType.FP16
271
    elif dtype == np.int32:
272
        return core.VarDesc.VarType.INT32
273
    elif dtype == np.int16:
274
        return core.VarDesc.VarType.INT16
275
    elif dtype == np.int64:
276
        return core.VarDesc.VarType.INT64
277
    elif dtype == np.bool:
278
        return core.VarDesc.VarType.BOOL
279 280
    elif dtype == np.uint16:
        return core.VarDesc.VarType.INT16
281 282
    elif dtype == np.uint8:
        return core.VarDesc.VarType.UINT8
Q
qingqing01 已提交
283 284
    elif dtype == np.int8:
        return core.VarDesc.VarType.INT8
285
    else:
M
minqiyang 已提交
286
        raise ValueError("Not supported numpy dtype %s" % dtype)
287 288 289


def dtype_is_floating(dtype):
290 291 292
    """
    Check the data type is floating or not.
    Args:
293
        dtype(np.dtype|core.VarDesc.VarType): data type.
294 295 296 297 298
            Could be numpy format or Paddle format

    Returns(bool): True if data type is a float value

    """
299
    if not isinstance(dtype, core.VarDesc.VarType):
300 301
        dtype = convert_np_dtype_to_dtype_(dtype)

302 303 304 305
    return dtype in [
        core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32,
        core.VarDesc.VarType.FP64
    ]
306 307


Y
Yang Yang(Tony) 已提交
308
def _debug_string_(proto, throw_on_error=True):
309 310 311 312 313 314 315 316 317 318 319
    """
    Get the debug string of a protobuf message. The message could be not
    initialized.
    Args:
        proto(google.protobuf.message.Message): The protobuf message
        throw_on_error(bool): True if raise an error when the protobuf message
            is not initialized.

    Returns(str): The debug string of the protobuf message

    """
Y
Yu Yang 已提交
320
    error_fields = list()
Y
Yang Yang(Tony) 已提交
321
    if not proto.IsInitialized(error_fields) and throw_on_error:
C
caoying03 已提交
322 323
        raise ValueError("{0} are not initialized.\nThe message is {1}:\n".
                         format(error_fields, proto))
Y
Yu Yang 已提交
324 325 326
    return proto.__str__()


X
Xin Pan 已提交
327
class Variable(object):
328
    """
329 330 331
    In Fluid, every input and output of an operator is a variable. In most
    cases, variables are used for holding different kinds of data or training
    labels. A variable belongs to a block. All variable has its own name and
332
    two variables in different blocks could have the same name.
333

334 335
    There are many kinds of variables. Each kind of them has its own attributes
    and usages. Please reference the framework.proto for details.
336

337
    Most of a Variable's member variables can be setted to be None. It mean
338
    it is not available or will be specified later.
339 340

    Args:
341
        block(Block): The block that the variable belongs to.
342 343
        type(core.VarDesc.VarType): Variable type. Please reference the
            framework.proto for details.
344 345
        name(str|None): The name of the variable. If setted None, it will be
            generated automatically. Default: None
346
        shape(tuple|list|None): The shape of the variable. -1 means the batch size.
347
            Some kinds of variable do not contain shape, just set it to None.
348 349 350
            Default: None
        dtype(np.dtype|core.VarDesc.VarType|str|None): The data type of variable.
            Default: None
351
        lod_level (int|None): The level of lod tensor. 0 means it is not a time
352
            series data.
353
            Default: None
354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375
        capacity (int|None): The capacity of Channel variable. Ignored for other
            types. Default: None
        persistable (bool|None): True if the variable is persistable. A persistable
            variable will not be deleted after an iteration ending. Defaults: None.
        error_clip (BaseErrorClipAttr|None): The error clip attributes of the
            corresponding gradient variable. Default: None
        stop_gradient (bool): True if the variable will stop to calculate its
            gradients when backward. Default: False.
        is_data (bool): True if the variable is an input data. Default: False

    Notes:
        The constructor of Variable should not be invoked directly. Please
        use `Block.create_var` to create a variable.

    Examples:
        .. code-block:: python

            cur_program = Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
376 377
    """

Y
Yu Yang 已提交
378 379
    def __init__(self,
                 block,
Y
Yu Yang 已提交
380
                 type=core.VarDesc.VarType.LOD_TENSOR,
Y
Yu Yang 已提交
381 382 383 384
                 name=None,
                 shape=None,
                 dtype=None,
                 lod_level=None,
385
                 capacity=None,
Q
QI JUN 已提交
386
                 persistable=None,
F
fengjiayi 已提交
387
                 error_clip=None,
Y
Yu Yang 已提交
388
                 stop_gradient=False,
F
fengjiayi 已提交
389
                 is_data=False,
Y
Yu Yang 已提交
390
                 **kwargs):
Y
Yu Yang 已提交
391 392
        self.block = block
        if name is None:
Y
Yu Yang 已提交
393
            name = unique_name.generate('_generated_var')
D
Dong Zhihong 已提交
394

Y
Yu Yang 已提交
395
        if dtype is not None:
396
            if not isinstance(dtype, core.VarDesc.VarType):
397
                dtype = convert_np_dtype_to_dtype_(dtype)
398

L
lujun 已提交
399
        if _in_dygraph_mode():
M
minqiyang 已提交
400
            # record vars in tracer rather than blocks
M
minqiyang 已提交
401 402
            self._ivar = kwargs.get("ivar", None)
            if not self._ivar:
403 404 405
                self._ivar = core.VarBase(
                    name, dtype if dtype else core.VarDesc.VarType.FP32,
                    list(shape) if shape else [],
X
fix  
Xin Pan 已提交
406 407
                    _current_expected_place(), stop_gradient, True
                    if persistable else False)
M
minqiyang 已提交
408
            if persistable:
L
lujun 已提交
409
                _dygraph_tracer().trace_var(name, self)
M
minqiyang 已提交
410
        else:
411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482
            self.error_clip = error_clip

            is_new_var = False
            name = cpt.to_text(name)
            self.desc = self.block.desc.find_var(cpt.to_bytes(name))

            if self.desc is None:
                self.desc = self.block.desc.var(cpt.to_bytes(name))
                is_new_var = True

            if is_new_var:
                self.desc.set_type(type)
            elif self.desc.type() != type:
                raise ValueError(
                    "Variable {0} has been created before. The "
                    "previous type is {1}; the new type is {2}. They"
                    " are not matched".format(self.name, self.desc.type(),
                                              type))

            if shape is not None:
                if is_new_var:
                    self.desc.set_shape(shape)
                else:
                    old_shape = self.shape
                    shape = tuple(shape)
                    if shape != old_shape:
                        raise ValueError(
                            "Variable {0} has been created before. the previous "
                            "shape is {1}; the new shape is {2}. They are not "
                            "matched.".format(self.name, old_shape, shape))
            if dtype is not None:
                if is_new_var:
                    self.desc.set_dtype(dtype)
                else:
                    old_dtype = self.dtype
                    if dtype != old_dtype:
                        raise ValueError(
                            "Variable {0} has been created before. "
                            "The previous data type is {1}; the new "
                            "data type is {2}. They are not "
                            "matched.".format(self.name, old_dtype, dtype))

            if lod_level is not None:
                if is_new_var:
                    self.desc.set_lod_level(lod_level)
                else:
                    if lod_level != self.lod_level:
                        raise ValueError(
                            "Variable {0} has been created before. "
                            "The previous lod_level is {1}; the new "
                            "lod_level is {2}. They are not "
                            "matched".format(self.name, self.lod_level,
                                             lod_level))
            if persistable is not None:
                if is_new_var:
                    self.desc.set_persistable(persistable)
                else:
                    if persistable != self.persistable:
                        raise ValueError(
                            "Variable {0} has been created before."
                            "The previous persistable is {1}; the new "
                            "persistable is {2}. They are not matched".format(
                                self.name, self.persistable, persistable))

            if capacity is not None:
                if is_new_var:
                    self.desc.set_capacity(capacity)
                else:
                    # TODO(abhinavarora) : Compare with set capacity once,
                    # get_capacity is implemented
                    pass

M
minqiyang 已提交
483
            self.block.vars[name] = self
484 485 486
            self.op = None
            self.stop_gradient = stop_gradient
            self.is_data = is_data
Y
Yu Yang 已提交
487

488
    def numpy(self):
M
minqiyang 已提交
489
        new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
P
Paddle CI 已提交
490
        return np.array(new_ivar.value().get_tensor())
491

492
    def backward(self):
X
Xin Pan 已提交
493
        self._ivar._run_backward()
494

495
    def gradient(self):
M
minqiyang 已提交
496
        return np.array(self._ivar._grad_value())
497

498
    def clear_gradient(self):
X
Xin Pan 已提交
499
        self._ivar._clear_gradient()
X
Xin Pan 已提交
500

501
    def __str__(self):
Y
Yang Yang(Tony) 已提交
502 503
        return self.to_string(True)

F
update  
fengjiayi 已提交
504
    def to_string(self, throw_on_error, with_details=False):
505 506 507 508
        """
        Get debug string.

        Args:
509 510
            throw_on_error(bool): True if raise an exception when self is
                not initialized.
F
update  
fengjiayi 已提交
511
            with_details(bool): more details about variables and parameters
512 513
                (e.g. trainable, optimize_attr, ...) will be printed when
                with_details is True. Default False;
514

515 516
        Returns:
            str: The debug string.
517
        """
L
lujun 已提交
518 519
        if _in_dygraph_mode():
            # TODO(panyx0718): add more dygraph debug info.
520 521 522
            return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
                                                     self.shape)

F
update  
fengjiayi 已提交
523 524
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
525
        protostr = self.desc.serialize_to_string()
526
        proto = framework_pb2.VarDesc.FromString(six.binary_type(protostr))
F
update  
fengjiayi 已提交
527 528 529 530
        res_str = _debug_string_(proto, throw_on_error)
        if with_details:
            additional_attr = ("error_clip", "stop_gradient")
            for attr_name in additional_attr:
531 532
                res_str += "%s: %s\n" % (
                    attr_name, six.binary_type(getattr(self, attr_name)))
F
update  
fengjiayi 已提交
533
        return res_str
534 535 536

    __repr__ = __str__

537
    def set_desc(self, input):
538 539 540 541 542 543 544 545 546
        """
        Set the variable description.

        Args:
            input(core.VarDesc): The new VarDesc.

        Returns:
            None
        """
547 548
        self.desc = input

549
    @property
550
    def stop_gradient(self):
L
lujun 已提交
551
        if _in_dygraph_mode():
M
minqiyang 已提交
552 553 554
            return self._ivar.stop_gradient
        else:
            return self.stop_gradient
555

556 557
    @stop_gradient.setter
    def stop_gradient(self, s):
L
lujun 已提交
558
        if _in_dygraph_mode():
M
minqiyang 已提交
559
            self._ivar.stop_gradient = s
560 561
        else:
            self.stop_gradient = s
562

563 564
    @property
    def persistable(self):
L
lujun 已提交
565
        if _in_dygraph_mode():
566 567 568
            return self._ivar.persistable
        else:
            return self.desc.persistable()
569

Y
Yu Yang 已提交
570 571
    @persistable.setter
    def persistable(self, p):
L
lujun 已提交
572
        if _in_dygraph_mode():
573 574 575
            return self._ivar.persistable
        else:
            self.desc.set_persistable(p)
Y
Yu Yang 已提交
576

Y
Yu Yang 已提交
577 578
    @property
    def name(self):
L
lujun 已提交
579
        if _in_dygraph_mode():
580 581 582
            return self._ivar.name
        else:
            return cpt.to_text(self.desc.name())
Y
Yu Yang 已提交
583

T
typhoonzero 已提交
584 585
    @name.setter
    def name(self, new_name):
L
lujun 已提交
586
        if _in_dygraph_mode():
587 588 589
            self._ivar.name = new_name
        else:
            self.desc.set_name(new_name)
T
typhoonzero 已提交
590

Y
Yu Yang 已提交
591 592 593
    @property
    def shape(self):
        # convert to tuple, make it as same as numpy API.
L
lujun 已提交
594
        if _in_dygraph_mode():
595 596 597
            return self._ivar.shape
        else:
            return tuple(self.desc.shape())
Y
Yu Yang 已提交
598 599

    @property
F
fengjiayi 已提交
600
    def dtype(self):
L
lujun 已提交
601
        if _in_dygraph_mode():
602 603 604
            return self._ivar.dtype
        else:
            return self.desc.dtype()
Y
Yu Yang 已提交
605 606 607

    @property
    def lod_level(self):
L
lujun 已提交
608
        # TODO(minqiyang): Support lod_level in dygraph mode
609
        return self.desc.lod_level()
Y
Yu Yang 已提交
610

Y
Yu Yang 已提交
611 612
    @property
    def type(self):
L
lujun 已提交
613
        if _in_dygraph_mode():
614 615 616
            return self._ivar.dtype
        else:
            return self.desc.type()
Y
Yu Yang 已提交
617

W
Wu Yi 已提交
618
    def _set_error_clip(self, error_clip):
619 620 621 622 623 624 625 626 627
        """
        Set the error_clip.

        Args:
            error_clip(BaseErrorClipAttr) : The new error_clip.

        Returns:
            None
        """
628 629
        self.error_clip = error_clip

630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716
    def _slice_indices(self, slice, length):
        """
        Reference implementation for the slice.indices method.
        """
        # Compute step and length as integers.
        step = 1 if slice.step is None else slice.step

        # Raise ValueError for negative length or zero step.
        if length < 0:
            raise ValueError("length should not be negative")
        if step == 0:
            raise ValueError("slice step cannot be zero")

        # Find lower and upper bounds for start and stop.
        lower = -1 if step < 0 else 0
        upper = length - 1 if step < 0 else length

        # Compute start.
        if slice.start is None:
            start = upper if step < 0 else lower
        else:
            start = slice.start
            start = max(start + length, lower) if start < 0 else min(start,
                                                                     upper)

        # Compute stop.
        if slice.stop is None:
            stop = lower if step < 0 else upper
        else:
            stop = slice.stop
            stop = max(stop + length, lower) if stop < 0 else min(stop, upper)

        return start, stop, step

    def _detectEllipsis(self, item):
        has_ellipsis = False
        start = 0
        end = len(self.shape)
        for index, o in enumerate(item):
            if o is Ellipsis:
                if has_ellipsis:
                    raise ValueError("Index can have one ellipsis only.")
                has_ellipsis = True
                start = index
            else:
                if has_ellipsis:
                    end = index
        return has_ellipsis, start, end

    def _reconstructSliceinfo(self, item):
        has_ellipsis, start, end = self._detectEllipsis(item)
        if has_ellipsis:
            newitem = []
            for i in range(start):
                newitem.append(item[i])
            for i in range(start, end):
                newitem.append(slice(None, None, None))
            for i in range(end, len(item)):
                newitem.append(item[i])
            return newitem
        else:
            return None

    def _detectContinuesSlice(self, item):
        starts = []
        ends = []
        for index, o in enumerate(item):
            if isinstance(o, int):
                start = int(o)
                if (index > 0 and index >= self.shape[index]) \
                        or (index < 0 and (index + self.shape[index]) < 0):
                    raise IndexError("invalid index")
                start = max(start + self.shape[index], 0) if start < 0 else min(
                    start, self.shape[index])
                starts.append(start)
                ends.append(start + 1)
            elif isinstance(o, slice):
                start, stop, step = self._slice_indices(o, self.shape[index])
                if step == 1 or step == -1:
                    starts.append(start)
                    ends.append(stop)
                else:
                    return False, None
            else:
                raise IndexError("Valid index accept int or slice or ellipsis")
        return True, [starts, ends]

717
    def cloneVar(self, copy=False):
718 719 720 721 722
        if not copy:
            return self.block.create_var(
                name=unique_name.generate(".".join(self.name)),
                dtype=self.dtype,
                persistable=self.persistable,
723
                stop_gradient=self.stop_gradient, )
724 725 726 727
        else:
            return self

    def _sliceVar(self, axes, starts, ends):
728
        new_var = self.cloneVar()
729 730 731 732 733 734 735 736 737 738
        self.block.append_op(
            type="slice",
            inputs={'Input': [self]},
            outputs={'Out': [new_var]},
            attrs={'axes': axes,
                   'starts': starts,
                   'ends': ends})
        return new_var

    def _concatVar(self, inputs, axis):
739
        new_var = self.cloneVar()
740 741 742 743 744 745 746 747 748 749
        self.block.append_op(
            type="concat",
            inputs={'X': inputs},
            outputs={'Out': [new_var]},
            attrs={'axis': axis, })
        return new_var

    def _sliceAndConcatVar(self, item, axis):
        if isinstance(item, slice):
            if self.shape[axis] < 0:
750
                return self.cloneVar(True)
751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
            start, stop, step = self._slice_indices(item, self.shape[axis])
            if step == 1:
                return self._sliceVar([axis], [start], [stop])
            else:
                vars = []
                if step > 0:
                    while start < stop:
                        vars.append(
                            self._sliceVar([axis], [start], [start + 1]))
                        start += step
                else:
                    while start > stop:
                        vars.append(
                            self._sliceVar([axis], [start], [start + 1]))
                        start += step
                return self._concatVar(vars, axis)
        elif isinstance(item, int):
            if self.shape[axis] < 0:
769
                return self.cloneVar(True)
770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791
            index = int(item)
            if (index > 0 and index >= self.shape[axis])\
                    or (index < 0 and (index + self.shape[axis]) < 0):
                raise IndexError("invalid index")
            return self._sliceVar([axis], [index], [index + 1])
        else:
            raise IndexError("Valid index accept int or slice or tuple")

    def __getitem__(self, item):
        """
        Slice the variable.

        Args:
            item(int/slice/tuple) : the index.

        Returns:
            Sliced variable
        """
        new_var = None
        if isinstance(item, tuple):
            if len(item) > len(self.shape):
                raise IndexError("Too many indexes")
W
wopeizl 已提交
792 793 794 795 796 797
            fixedSize = True
            for i in range(len(self.shape)):
                if self.shape[i] == -1:
                    fixedSize = False
                    break

798
            newitem = self._reconstructSliceinfo(item) or item
W
wopeizl 已提交
799 800
            if fixedSize:
                check, info = self._detectContinuesSlice(newitem)
801
                if check:
W
wopeizl 已提交
802 803 804 805 806 807 808 809
                    starts = info[0]
                    ends = info[1]
                    axes = [i for i in range(len(starts))]
                    return self._sliceVar(axes, starts, ends)
                else:
                    new_var = self
                    for index, o in enumerate(newitem):
                        new_var = new_var._sliceAndConcatVar(o, index)
810 811 812 813 814 815 816 817
            else:
                new_var = self
                for index, o in enumerate(newitem):
                    new_var = new_var._sliceAndConcatVar(o, index)
        else:
            new_var = self._sliceAndConcatVar(item, 0)
        return new_var

Y
Yu Yang 已提交
818

F
fengjiayi 已提交
819 820 821
def get_all_op_protos():
    """
    Get all registered op proto from PaddlePaddle C++ end.
822

823 824
    Returns:
       list: list of OpProto.
F
fengjiayi 已提交
825 826 827 828
    """
    protostrs = core.get_all_op_protos()
    ret_values = []
    for pbstr in protostrs:
829
        op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr))
F
fengjiayi 已提交
830 831 832 833 834
        ret_values.append(op_proto)
    return ret_values


class OpProtoHolder(object):
835 836 837 838
    """
    A global variable to hold all OpProtos from C++ as a map
    """

F
fengjiayi 已提交
839 840 841 842 843 844 845 846 847
    @classmethod
    def instance(cls):
        if not hasattr(cls, '_instance'):
            cls._instance = cls()
        return cls._instance

    def __init__(self):
        assert not hasattr(
            self.__class__,
848
            '_instance'), 'Please use `instance()` to get OpProtoHolder object!'
F
fengjiayi 已提交
849 850 851 852 853 854
        op_protos = get_all_op_protos()
        self.op_proto_map = {}
        for proto in op_protos:
            self.op_proto_map[proto.type] = proto

    def get_op_proto(self, type):
855 856 857 858 859 860 861 862
        """
        Get OpProto by a type string.
        Args:
            type(str): The type that operator registered in C++ side.

        Returns(framework_pb2.OpProto): The OpProto

        """
Y
Yu Yang 已提交
863 864
        if type not in self.op_proto_map:
            raise ValueError("Operator \"%s\" has not been registered." % type)
F
fengjiayi 已提交
865 866
        return self.op_proto_map[type]

867 868 869 870
    @staticmethod
    def generated_op_attr_names():
        return {
            core.op_proto_and_checker_maker.kOpRoleAttrName(),
S
sneaxiy 已提交
871
            core.op_proto_and_checker_maker.kOpRoleVarAttrName(),
872 873
            core.op_proto_and_checker_maker.kOpNameScopeAttrName(),
            core.op_proto_and_checker_maker.kOpCreationCallstackAttrName()
874 875
        }

F
fengjiayi 已提交
876

X
Xin Pan 已提交
877
class Operator(object):
878
    """
879 880 881 882 883 884 885
    In Fluid, all the operation are represented by Operator, and Operator
    is regarded as a build in an instruction of a Block. Users can use the
    build in instructions to describe their neural network.

    Args:
        block(Block): The block has the current operator.
        desc(core.OpDesc): The protobuf description of Operator.
C
chengduoZH 已提交
886
        type(str): The type of operator. Default None.
887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906
        inputs(dict): The input of this Operator. it is a dictionary, for every
            element, key is the input parameter name, and value is a list of
            variables. Default None.
        outputs(dict): The output of this Operator. it is a dictionary, for
            every element, key is the input parameter name, and value is a list
            of variables. Default None.
        attrs(dict): The attributes of this Operator. it is a dictionary, for
            every element, key is attribute name, and value is the attribute value.
            The attribute type should be as same as the type registered in C++ side.
            Default None.

    Returns:
        Operator: The initialized Operator.

    Raises:
        ValueError: If the passed input, output and attrs doesn't match the
            initializing Operator's that registered in C++ side.

    Notes:
        The constructor of operator should not be invoked directly. Use
W
Wu Yi 已提交
907
        Block.append_op or Block._prepend_op instead.
908 909 910 911 912 913 914 915 916 917

    Examples:
        .. code-block:: python

            cur_program = Program()
            cur_block = cur_program.current_block()
            # var1 += var2 + var3
            cur_block.append_op(type="sum",
                                inputs={"X": [var1, var2, var3]},
                                outputs={"Out": [var1]})
918
    """
919
    OP_WITHOUT_KERNEL_SET = {
920 921 922
        'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
        'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
        'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
923
    }
924

Y
Yu Yang 已提交
925 926
    def __init__(self,
                 block,
Y
Yu Yang 已提交
927
                 desc,
Y
Yu Yang 已提交
928 929 930
                 type=None,
                 inputs=None,
                 outputs=None,
M
minqiyang 已提交
931
                 attrs=None):
L
lujun 已提交
932
        if _in_dygraph_mode():
933 934
            if type is None:
                raise ValueError(
935
                    "`type` to initialized an Operator can not be None.")
936
            self.iop = core.OpBase(type)
M
minqiyang 已提交
937

938 939
            # TODO(minqiyang): remove these lines after we take apart all
            # backward grads and forward variables
X
Xin Pan 已提交
940
            self.inputs = defaultdict(list)
X
Xin Pan 已提交
941
            if inputs is not None:
X
Xin Pan 已提交
942 943 944 945 946
                for k, v in six.iteritems(inputs):
                    if isinstance(v, Variable):
                        self.inputs[k].append(v._ivar)
                    elif isinstance(v, list) or isinstance(v, tuple):
                        self.inputs[k].extend([var._ivar for var in v])
M
minqiyang 已提交
947

X
Xin Pan 已提交
948
            self.outputs = defaultdict(list)
X
Xin Pan 已提交
949
            if outputs is not None:
X
Xin Pan 已提交
950 951 952 953 954
                for k, v in six.iteritems(outputs):
                    if isinstance(v, Variable):
                        self.outputs[k].append(v._ivar)
                    elif isinstance(v, list) or isinstance(v, tuple):
                        self.outputs[k].extend([var._ivar for var in v])
F
fengjiayi 已提交
955

956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050
            self.attrs = attrs if attrs else {}
        else:
            self.block = block
            self.desc = desc
            # note: not add self.attrs here:
            # https://github.com/PaddlePaddle/Paddle/pull/12583#pullrequestreview-145093173
            op_attrs = attrs
            if op_attrs is None:
                op_attrs = dict()
            del attrs

            op_maker = core.op_proto_and_checker_maker

            if op_maker.kOpRoleAttrName() not in op_attrs:
                op_attrs[op_maker.kOpRoleAttrName(
                )] = self.block.program.op_role

            role_var_name = op_maker.kOpRoleVarAttrName()
            if len(self.block.program.
                   op_role_var) != 0 and role_var_name not in op_attrs:
                op_attrs[role_var_name] = self.block.program.op_role_var

            if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
                del op_attrs[role_var_name]

            if len(self.desc.type()) != 0:
                return
            if type is None:
                raise ValueError(
                    "`type` to initilized an Operator can not be None.")
            else:
                callstack_var_name = op_maker.kOpCreationCallstackAttrName()
                op_attrs[callstack_var_name] = list(
                    reversed(traceback.format_stack()))[1:]

            self.desc.set_type(type)
            proto = OpProtoHolder.instance().get_op_proto(type)

            namescope_var_name = op_maker.kOpNameScopeAttrName()
            op_attrs[namescope_var_name] = _full_name_scope()

            def find_name(var_list, name):
                for var_name in var_list:
                    if var_list[var_name] is not None and var_name == name:
                        return True
                return False

            if inputs is not None:
                for in_proto in proto.inputs:
                    found = find_name(inputs, in_proto.name)
                    assert found or in_proto.dispensable, "Input {} not found".format(
                        in_proto.name)

                    if found:
                        in_args = inputs[in_proto.name]
                        if not isinstance(in_args, list):
                            in_args = [in_args]
                        if not in_proto.duplicable and len(in_args) > 1:
                            raise ValueError(
                                "Input %s expects only one input, but %d are given."
                                % (in_proto.name, len(in_args)))
                        in_arg_names = []
                        for arg in in_args:
                            if isinstance(arg, six.string_types):
                                in_arg_names.append(arg)
                            elif isinstance(arg, six.binary_type):
                                in_arg_names.append(arg.decode())
                            else:
                                in_arg_names.append(cpt.to_text(arg.name))
                        self.desc.set_input(in_proto.name, in_arg_names)
                    else:
                        self.desc.set_input(in_proto.name, [])

            if outputs is not None:
                for m in proto.outputs:
                    if (m.name not in outputs) and m.dispensable:
                        continue
                    if not ((m.name in outputs) or m.dispensable):
                        raise ValueError(("Incorrect setting for output(s) of "
                                          "operator \"%s\", should set: [%s].")
                                         % (type, m.name))
                for out_proto in proto.outputs:
                    if out_proto.name not in outputs:
                        continue
                    out_args = outputs[out_proto.name]
                    if not isinstance(out_args, list):
                        out_args = [out_args]
                    if not out_proto.duplicable and len(out_args) > 1:
                        raise ValueError(
                            "Output %s expects only one output, but %d are given."
                            % (out_proto.name, len(out_args)))
                    out_arg_names = []
                    for arg in out_args:
                        out_arg_names.append(cpt.to_text(arg.name))
                        # TODO(minqiyang): could we remove variable's op in static mode?
L
lujun 已提交
1051
                        if not _in_dygraph_mode():
1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070
                            arg.op = self
                    self.desc.set_output(out_proto.name, out_arg_names)

            if op_attrs is not None:
                if not isinstance(op_attrs, dict):
                    raise TypeError("'attrs' should be a dict.")
                for attr in proto.attrs:
                    attr_name = attr.name
                    if (attr_name not in op_attrs) or (
                            op_attrs[attr_name] is None):
                        continue
                    attr_val = op_attrs[attr_name]
                    self._update_desc_attr(attr_name, attr_val)

            self.desc.check_attrs()
            if self._has_kernel(type):
                self.desc.infer_var_type(self.block.desc)
                self.desc.infer_shape(self.block.desc)

W
Wu Yi 已提交
1071
    def _has_kernel(self, op_type):
1072 1073
        return op_type not in self.OP_WITHOUT_KERNEL_SET

Y
Yang Yang(Tony) 已提交
1074
    def to_string(self, throw_on_error):
1075
        """
1076 1077
        Get debug string.

1078
        Args:
1079 1080
            throw_on_error(bool): Whether to raise exception if self is not
                initialized.
1081

1082 1083
        Returns:
            str: The debug string.
1084 1085

        """
1086
        protostr = self.desc.serialize_to_string()
1087
        proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
Y
Yang Yang(Tony) 已提交
1088 1089 1090 1091
        return _debug_string_(proto, throw_on_error)

    def __str__(self):
        return self.to_string(True)
1092 1093 1094

    __repr__ = __str__

F
fengjiayi 已提交
1095 1096
    @property
    def type(self):
L
lujun 已提交
1097
        if _in_dygraph_mode():
1098 1099 1100
            return self.iop.type
        else:
            return self.desc.type()
F
fengjiayi 已提交
1101 1102

    def input(self, name):
1103
        """
1104
        Get the input arguments according to the input parameter name.
1105

1106 1107
        Args:
            name(str): The input parameter name.
1108

1109 1110 1111
        Returns:
            list: return the list of argument names that associated with \
                the specific parameter name.
1112
        """
F
fengjiayi 已提交
1113 1114
        return self.desc.input(name)

W
Wu Yi 已提交
1115
    def _rename_input(self, old_name, new_name):
1116 1117 1118 1119 1120 1121 1122 1123 1124 1125
        """
        Rename the `old_name` to `new_name`.

        Args:
            old_name(str): The old name of the Operator's input.
            new_name(str): The new name of the Operator's input.

        Returns:
            None
        """
W
Wu Yi 已提交
1126
        self.desc._rename_input(old_name, new_name)
T
typhoonzero 已提交
1127

W
Wu Yi 已提交
1128
    def _rename_output(self, old_name, new_name):
1129 1130 1131 1132 1133 1134 1135 1136 1137 1138
        """
        Rename the `old_name` to `new_name`.

        Args:
            old_name(str): The old name of the Operator's output.
            new_name(str): The new name of the Operator's output.

        Returns:
            None
        """
W
Wu Yi 已提交
1139
        self.desc._rename_output(old_name, new_name)
T
typhoonzero 已提交
1140

F
fengjiayi 已提交
1141 1142 1143 1144
    @property
    def input_names(self):
        return self.desc.input_names()

T
typhoonzero 已提交
1145 1146 1147 1148 1149 1150 1151 1152
    @property
    def input_arg_names(self):
        return self.desc.input_arg_names()

    @property
    def output_arg_names(self):
        return self.desc.output_arg_names()

F
fengjiayi 已提交
1153
    def output(self, name):
1154
        """
1155
        Get output arguments by the output parameter name.
1156

1157 1158
        Args:
            name(str): The output parameter name.
1159

1160 1161 1162
        Returns:
            list: return the list of argument names associated with \
                the specific parameter name.
1163
        """
F
fengjiayi 已提交
1164 1165 1166 1167 1168 1169
        return self.desc.output(name)

    @property
    def output_names(self):
        return self.desc.output_names()

1170 1171 1172 1173 1174 1175 1176 1177
    @property
    def idx(self):
        for i, op in enumerate(self.block.ops):
            if op == self:
                return i
        raise ValueError(
            "Can't find op itself in it's block. It could be a bug of Paddle.")

F
fengjiayi 已提交
1178
    def has_attr(self, name):
1179
        """
1180 1181
        Whether this Operator has the attribute with name or not.

1182
        Args:
1183
            name(str): the attribute name.
1184

1185 1186
        Returns:
            bool: True if has this attribute.
1187 1188

        """
F
fengjiayi 已提交
1189 1190 1191
        return self.desc.has_attr(name)

    def attr_type(self, name):
1192
        """
1193
        Get the type of attribute by attribute's name.
1194

1195 1196
        Args:
            name(str): the attribute name.
1197

1198 1199
        Returns:
            core.AttrType: the attribute type.
1200
        """
F
fengjiayi 已提交
1201 1202
        return self.desc.attr_type(name)

W
Wu Yi 已提交
1203
    def _set_attr(self, name, val):
1204 1205 1206 1207 1208 1209 1210 1211 1212 1213
        """
        Set the value of attribute by attribute's name.

        Args:
            name(str): the attribute name.
            val(bool|int|str|float|list): the value of the attribute.

        Raises:
            ValueError: If the type of value doesn't match with desc.attr_type(name).
        """
G
gongweibao 已提交
1214 1215
        self._update_desc_attr(name, val)

1216 1217 1218
    def _remove_attr(self, name):
        self.desc.remove_attr(name)

G
gongweibao 已提交
1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229
    def _update_desc_attr(self, name, val):
        """
        Update the value of desc's attribute by attribute's name.

        Args:
            name(str): the attribute name.
            val(bool|int|str|float|list): the value of the attribute.

        Raises:
            ValueError: If the type of value doesn't match with desc.attr_type(name).
        """
Q
Qiyang Min 已提交
1230 1231
        if isinstance(val, Block):
            self.desc.set_block_attr(name, val.desc)
Y
Yancey1989 已提交
1232 1233
        elif isinstance(val, list) and val and all(
                isinstance(v, Block) for v in val):
1234
            self.desc.set_blocks_attr(name, [v.desc for v in val])
Q
Qiyang Min 已提交
1235 1236 1237 1238
        elif isinstance(val, core.BlockDesc) or \
                isinstance(val, core.ProgramDesc):
            self.desc.set_serialized_attr(name, val.serialize_to_string())
        else:
W
Wu Yi 已提交
1239
            self.desc._set_attr(name, val)
Y
yuyang18 已提交
1240

F
fengjiayi 已提交
1241 1242 1243 1244 1245
    @property
    def attr_names(self):
        return self.desc.attr_names()

    def attr(self, name):
1246
        """
1247 1248
        Get the attribute by name.

1249
        Args:
1250
            name(str): the attribute name.
1251

1252 1253
        Returns:
            bool|int|str|float|list: The attribute value. The return value
1254 1255
            can be any valid attribute type.
        """
F
fengjiayi 已提交
1256
        return self.desc.attr(name)
Y
Yu Yang 已提交
1257

W
Wu Yi 已提交
1258
    def _block_attr_id(self, name):
1259
        """
G
gongweibao 已提交
1260
        Get the block attribute's id by name.
1261

1262 1263
        Args:
            name(str): the attribute name.
1264

1265 1266
        Returns:
            int: the block index.
1267
        """
W
Wu Yi 已提交
1268
        return self.desc._block_attr_id(name)
G
gongweibao 已提交
1269

W
Wu Yi 已提交
1270
    def _block_attr(self, name):
G
gongweibao 已提交
1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
        """
        Get the block attribute  by name.

        Args:
            name(str): the attribute name.

        Returns:
            block: the block attribute.
        """

W
Wu Yi 已提交
1281
        id = self._block_attr_id(name)
G
gongweibao 已提交
1282 1283 1284
        assert (id >= 0 and id < len(self.block.program.blocks))
        return self.block.program.blocks[id]

W
Wu Yi 已提交
1285
    def _blocks_attr(self, name):
G
gongweibao 已提交
1286 1287 1288 1289 1290 1291 1292 1293 1294 1295
        """
        Get the blocks attribute  by name.

        Args:
            name(str): the attribute name.

        Returns:
            list: list of the blocks attribute.
        """
        attrs = []
W
Wu Yi 已提交
1296
        for i in self._blocks_attr_ids(name):
G
gongweibao 已提交
1297 1298 1299 1300 1301
            assert (i >= 0 and i < len(self.block.program.blocks))
            attrs.append(self.block.program.blocks[i])

        return attrs

W
Wu Yi 已提交
1302
    def _blocks_attr_ids(self, name):
G
gongweibao 已提交
1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
        """
        Get the blocks attribute's ids by name.

        Args:
            name(str): the attribute name.

        Returns:
            list: list of the blocks ids.
        """

W
Wu Yi 已提交
1313
        return self.desc._blocks_attr_ids(name)
Y
Yu Yang 已提交
1314

J
JiayiFeng 已提交
1315
    def all_attrs(self):
F
fengjiayi 已提交
1316
        """
1317 1318 1319
        Get the attribute dict.

        Returns:
G
gongweibao 已提交
1320
            dict: The Operator's attribute dict, name->attr.
F
fengjiayi 已提交
1321 1322 1323 1324
        """
        attr_names = self.attr_names
        attr_map = {}
        for n in attr_names:
G
gongweibao 已提交
1325 1326
            attr_type = self.desc.attr_type(n)
            if attr_type == core.AttrType.BLOCK:
W
Wu Yi 已提交
1327
                attr_map[n] = self._block_attr(n)
G
gongweibao 已提交
1328 1329 1330
                continue

            if attr_type == core.AttrType.BLOCKS:
W
Wu Yi 已提交
1331
                attr_map[n] = self._blocks_attr(n)
G
gongweibao 已提交
1332 1333 1334 1335
                continue

            attr_map[n] = self.attr(n)

F
fengjiayi 已提交
1336 1337
        return attr_map

Y
Yu Yang 已提交
1338

Y
Yu Yang 已提交
1339
class Block(object):
1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353
    """
    In Fluid, a Program is consistence of multi-Block, and Block stores
    VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name.
    One block could have some child blocks, and child block's name scopes
    should inherit the parent's so that OpDesc in child block can reference
    a VarDesc that is stored in the parent block.
    Please reference the framework.proto for details.

    Args:
        program(Program): The Program that the Block belongs to.
        idx(int): The block's id in the Program.

    Notes:
        The constructor of Block should not be invoked directly. Please
W
Wu Yi 已提交
1354
        use `Program._create_block()` to create a block.
1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368

    Examples:
        .. code-block:: python

            cur_program = Program()
            cur_block = cur_program.current_block()
            var = cur_block.create_var(name="X",
                                       shape=[-1, 23, 48],
                                       dtype='float32')
            cur_block.append_op(type="abs",
                                inputs={"X": [var]},
                                outputs={"Out": [var]})
    """

Y
Yu Yang 已提交
1369
    def __init__(self, program, idx):
Y
Yu Yang 已提交
1370
        self.desc = program.desc.block(idx)
1371
        self.vars = collections.OrderedDict()  # var_name --> var
Q
qiaolongfei 已提交
1372
        self.ops = list()  # operator list
Y
Yu Yang 已提交
1373
        self.program = program
1374
        self.removed_vars = collections.OrderedDict()
Y
Yu Yang 已提交
1375

1376
    def __str__(self):
Y
Yang Yang(Tony) 已提交
1377 1378
        return self.to_string(True)

F
fengjiayi 已提交
1379 1380
    def to_string(self, throw_on_error, with_details=False):
        """
1381 1382
        Get debug string.

F
fengjiayi 已提交
1383 1384
        Args:
            throw_on_error(bool): raise exception when self is not initialized
1385
                when throw_on_error is True.
F
update  
fengjiayi 已提交
1386
            with_details(bool): more details about variables and parameters
1387 1388
                (e.g. trainable, optimize_attr, ...) will be printed when
                with_details is True. Default False.
F
fengjiayi 已提交
1389

1390 1391
        Returns:
            str: The debug string.
F
fengjiayi 已提交
1392 1393 1394 1395
        """
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
        if with_details:
F
fengjiayi 已提交
1396
            re_add_indent = re.compile(r"\n(.)")
F
fengjiayi 已提交
1397 1398
            res_str = "blocks {\n  idx: %d\n  parent_idx: %d" % (
                self.idx, self.parent_idx)
1399
            for var in list(self.vars.values()):
F
fengjiayi 已提交
1400
                res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
F
update  
fengjiayi 已提交
1401
                    r"\n    \1", var.to_string(throw_on_error, with_details))
F
fengjiayi 已提交
1402
            for op in self.ops:
F
fengjiayi 已提交
1403 1404
                res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
                    r"\n    \1", op.to_string(throw_on_error))
F
fengjiayi 已提交
1405 1406 1407
            res_str += "\n}"
        else:
            protostr = self.desc.serialize_to_string()
1408 1409
            proto = framework_pb2.BlockDesc.FromString(
                six.binary_type(protostr))
F
fengjiayi 已提交
1410 1411
            res_str = _debug_string_(proto, throw_on_error)
        return res_str
1412 1413 1414

    __repr__ = __str__

Y
Yu Yang 已提交
1415 1416
    @property
    def parent_idx(self):
Y
Yu Yang 已提交
1417
        return self.desc.parent
Y
Yu Yang 已提交
1418

Y
Yu Yang 已提交
1419 1420 1421 1422
    @property
    def forward_block_idx(self):
        return self.desc.get_forward_block_idx()

W
Wu Yi 已提交
1423
    def _set_forward_block_idx(self, idx):
1424 1425 1426 1427 1428 1429 1430 1431 1432
        """
        Set the forward block Idx.

        Args:
            idx(int): the block index.

        Returns:
            None
        """
W
Wu Yi 已提交
1433
        self.desc._set_forward_block_idx(idx)
Y
Yu Yang 已提交
1434

Y
Yu Yang 已提交
1435 1436
    @property
    def idx(self):
Y
Yu Yang 已提交
1437
        return self.desc.id
Y
Yu Yang 已提交
1438

Q
Qiao Longfei 已提交
1439
    def var(self, name):
1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452
        """
        Get a Variable by name from this block.

        Args:
            name(str): the Variable's name.

        Raises:
            ValueError: The If input's type is not str, or this block
                doesn't have a Variable with the giving name.

        Returns:
            Variable: the Variable with the giving name.
        """
1453
        if not isinstance(name, six.string_types):
M
minqiyang 已提交
1454 1455 1456
            raise TypeError(
                "var require string as parameter, but get %s instead." %
                (type(name)))
Y
Yu Yang 已提交
1457 1458
        v = self.vars.get(name, None)
        if v is None:
Q
Qiao Longfei 已提交
1459
            raise ValueError("var %s not in this block" % name)
Y
Yu Yang 已提交
1460
        return v
Q
Qiao Longfei 已提交
1461

X
Xin Pan 已提交
1462
    def _find_var_recursive(self, name):
1463 1464 1465 1466 1467 1468 1469
        """
        Get a Variable by name from this block recursively.

        Args:
            name(str): the Variable's name.

        Returns:
X
Xin Pan 已提交
1470
            Variable: the Variable with the giving name. Or None if not found.
1471
        """
Y
Yu Yang 已提交
1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495
        frontier = list()
        visited = set()

        frontier.append(self)

        prog = self.program

        while len(frontier) != 0:  # BFS
            cur = frontier[0]
            frontier = frontier[1:]

            if id(cur) in visited:
                continue

            if cur.has_var(name):
                return cur.var(name)

            if cur.parent_idx != -1:
                frontier.append(prog.block(cur.parent_idx))

            if cur.forward_block_idx != -1:
                frontier.append(prog.block(cur.forward_block_idx))

            visited.add(id(cur))
X
Xin Pan 已提交
1496
        return None
Y
Yu Yang 已提交
1497

X
Xin Pan 已提交
1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516
    def _var_recursive(self, name):
        """
        Get a Variable by name from this block recursively.

        Args:
            name(str): the Variable's name.

        Raises:
            ValueError: this block and this parent block doesn't
                have a Variable with the giving name.

        Returns:
            Variable: the Variable with the giving name.
        """
        var = self._find_var_recursive(name)
        if var:
            return var
        else:
            raise ValueError("Var {0} is not found recursively".format(name))
F
fengjiayi 已提交
1517

Q
Qiao Longfei 已提交
1518
    def all_parameters(self):
1519
        return list(self.iter_parameters())
1520

1521
    def iter_parameters(self):
M
minqiyang 已提交
1522
        return (item[1] for item in six.iteritems(self.vars)
1523
                if isinstance(item[1], Parameter))
Q
Qiao Longfei 已提交
1524

Y
Yu Yang 已提交
1525
    def create_var(self, *args, **kwargs):
1526
        var = Variable(block=self, *args, **kwargs)
1527 1528
        if 'initializer' in kwargs:
            kwargs['initializer'](var, self)
Q
Qiao Longfei 已提交
1529
        return var
Y
Yu Yang 已提交
1530

Q
Qiao Longfei 已提交
1531 1532 1533
    def has_var(self, name):
        return name in self.vars

W
Wu Yi 已提交
1534
    def _rename_var(self, name, new_name):
T
typhoonzero 已提交
1535 1536
        """
        Rename variable in vars and ops' inputs and outputs
1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548

        Args:
            name(str): the name that need to be renamed.
            new_name(str): the name that need to rename to.

        Raises:
            ValueError: If this block doesn't have this the giving name,
                or the type of the var with the giving name is not Parameter
                or Variable.

        Returns:
            Variable: the Variable with the giving name.
T
typhoonzero 已提交
1549
        """
M
minqiyang 已提交
1550 1551
        name = cpt.to_text(name)
        new_name = cpt.to_text(new_name)
M
minqiyang 已提交
1552

T
typhoonzero 已提交
1553
        if not self.has_var(name):
1554
            raise ValueError("var %s is not in current block" % name)
T
wip  
typhoonzero 已提交
1555 1556
        v = self.var(name)
        if type(v) == Parameter:
T
typhoonzero 已提交
1557
            var_type = "Parameter"
T
wip  
typhoonzero 已提交
1558 1559 1560 1561 1562 1563 1564
            stop_gradient = v.stop_gradient
            trainable = v.trainable
            optimize_attr = v.optimize_attr
            regularizer = v.regularizer
            gradient_clip_attr = v.gradient_clip_attr
            error_clip = v.error_clip
        elif type(v) == Variable:
T
typhoonzero 已提交
1565
            var_type = "Variable"
T
wip  
typhoonzero 已提交
1566 1567 1568 1569
            error_clip = v.error_clip
            stop_gradient = v.stop_gradient
        else:
            raise ValueError("unsupported var type: %s", type(v))
T
typhoonzero 已提交
1570
        orig_var_type = v.type
M
minqiyang 已提交
1571
        self.desc._rename_var(cpt.to_bytes(name), cpt.to_bytes(new_name))
W
Wu Yi 已提交
1572
        # NOTE: v is destroyed by C++ after calling _rename_var.
M
minqiyang 已提交
1573
        d = self.desc.find_var(cpt.to_bytes(new_name))
T
typhoonzero 已提交
1574
        if var_type == "Parameter":
T
wip  
typhoonzero 已提交
1575 1576 1577 1578
            var = Parameter(
                self,
                d.shape(),
                d.dtype(),
T
typhoonzero 已提交
1579
                type=orig_var_type,
T
wip  
typhoonzero 已提交
1580 1581 1582 1583 1584 1585 1586
                name=new_name,
                stop_gradient=stop_gradient,
                trainable=trainable,
                optimize_attr=optimize_attr,
                regularizer=regularizer,
                gradient_clip_attr=gradient_clip_attr,
                error_clip=error_clip)
T
typhoonzero 已提交
1587
        elif var_type == "Variable":
T
wip  
typhoonzero 已提交
1588 1589
            var = Variable(
                self,
T
typhoonzero 已提交
1590
                type=orig_var_type,
T
wip  
typhoonzero 已提交
1591 1592 1593 1594
                name=new_name,
                error_clip=error_clip,
                stop_gradient=stop_gradient)

W
Wu Yi 已提交
1595
        # rename the python side, _sync_with_cpp will only add
T
wip  
typhoonzero 已提交
1596 1597 1598
        # new vars/ops to python side.
        self.vars[new_name] = var
        del self.vars[name]
W
Wu Yi 已提交
1599
        self._sync_with_cpp()
1600
        return var
T
typhoonzero 已提交
1601

W
Wu Yi 已提交
1602 1603
    def _remove_var(self, name):
        self._sync_with_cpp()
M
minqiyang 已提交
1604
        self.desc._remove_var(cpt.to_bytes(name))
1605 1606
        del self.vars[name]

Y
Yu Yang 已提交
1607 1608
    def create_parameter(self, *args, **kwargs):
        global_block = self.program.global_block()
Q
Qiao Longfei 已提交
1609
        param = Parameter(global_block, *args, **kwargs)
1610
        if 'initializer' in kwargs:
1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630

            def _is_inited_by(block, var):
                init_ops = []
                for op in block.ops:
                    if var.name in op.output_arg_names:
                        init_ops.append(op)
                return init_ops

            initializer = kwargs['initializer']
            init_ops = _is_inited_by(global_block, param)
            init_ops_len = len(init_ops)
            if init_ops_len > 1:
                raise RuntimeError("param " + param.name +
                                   " is inited by multiple init ops " + str(
                                       init_ops))
            elif init_ops_len == 1:
                #TODO already inited, do nothing, should log a warning
                pass
            else:
                initializer(param, self)
Q
Qiao Longfei 已提交
1631
        return param
Y
Yu Yang 已提交
1632

Y
Yu Yang 已提交
1633
    def append_op(self, *args, **kwargs):
1634 1635 1636 1637 1638 1639
        """
        Appends a new Operator according to the giving arguments.

        Returns:
            Operator: the append Operator.
        """
L
lujun 已提交
1640
        if _in_dygraph_mode():
1641 1642 1643 1644 1645 1646 1647 1648
            op = Operator(
                block=self,
                desc=None,
                type=kwargs.get("type", None),
                inputs=kwargs.get("inputs", None),
                outputs=kwargs.get("outputs", None),
                attrs=kwargs.get("attrs", None))

M
minqiyang 已提交
1649 1650 1651
            # record ops in tracer rather than blocks
            #
            # TODO(minqiyang): add op stop_gradient support in static mode too.
L
lujun 已提交
1652 1653
            # currently, we only support stop_gradient in dygraph mode.
            _dygraph_tracer().trace_op(op, kwargs.get("stop_gradient", False))
M
minqiyang 已提交
1654
        else:
1655 1656 1657 1658 1659 1660 1661 1662 1663
            op_desc = self.desc.append_op()
            op = Operator(
                block=self,
                desc=op_desc,
                type=kwargs.get("type", None),
                inputs=kwargs.get("inputs", None),
                outputs=kwargs.get("outputs", None),
                attrs=kwargs.get("attrs", None))

M
minqiyang 已提交
1664
            self.ops.append(op)
M
minqiyang 已提交
1665

1666 1667
        return op

W
Wu Yi 已提交
1668
    def _insert_op(self, index, *args, **kwargs):
1669 1670 1671 1672 1673 1674 1675 1676 1677
        """
        Insert a Operator according to the giving arguments.

        Args:
            index(int): the place that the operator to insert.

        Returns:
            Operator: the insert Operator.
        """
W
Wu Yi 已提交
1678 1679
        self._sync_with_cpp()
        op_desc = self.desc._insert_op(index)
Q
qiaolongfei 已提交
1680 1681 1682 1683
        op = Operator(block=self, desc=op_desc, *args, **kwargs)
        self.ops.insert(index, op)
        return op

W
Wu Yi 已提交
1684
    def _remove_op(self, index):
1685 1686 1687 1688 1689 1690 1691 1692 1693
        """
        Remove the specific position operator.

        Args:
            index(int): the position that the operator to insert.

        Returns:
            None
        """
W
Wu Yi 已提交
1694 1695
        self._sync_with_cpp()
        self.desc._remove_op(index, index + 1)
1696 1697
        del self.ops[index]

W
Wu Yi 已提交
1698
    def _slice_ops(self, start, end):
1699 1700 1701 1702 1703 1704 1705 1706 1707 1708
        """
        Return the Operator between start and end.

        Args:
            start(int): the start position.
            end(int): the end position.

        Returns:
            list: the Operators between start and end.
        """
Q
qiaolongfei 已提交
1709
        return self.ops[start:end]
Y
Yancey1989 已提交
1710

W
Wu Yi 已提交
1711
    def _prepend_op(self, *args, **kwargs):
L
lujun 已提交
1712
        if _in_dygraph_mode():
1713 1714 1715 1716 1717 1718 1719
            op = Operator(
                self,
                None,
                type=kwargs.get("type", None),
                inputs=kwargs.get("inputs", None),
                outputs=kwargs.get("outputs", None),
                attrs=kwargs.get("attrs", None))
L
lujun 已提交
1720
            _dygraph_tracer().trace_op(op, kwargs.get("stop_gradient", False))
M
minqiyang 已提交
1721
        else:
1722 1723 1724 1725 1726 1727 1728 1729
            op_desc = self.desc._prepend_op()
            op = Operator(
                self,
                op_desc,
                type=kwargs.get("type", None),
                inputs=kwargs.get("inputs", None),
                outputs=kwargs.get("outputs", None),
                attrs=kwargs.get("attrs", None))
M
minqiyang 已提交
1730
            self.ops.insert(0, op)
1731

Y
Yu Yang 已提交
1732 1733
        return op

W
Wu Yi 已提交
1734
    def _sync_with_cpp(self):
1735
        """
1736 1737
        Sync from the desc on the c++ end. This method is used to synchronize
        the c++ desc instance generated by backward.
1738
        """
Q
Qiao Longfei 已提交
1739 1740 1741 1742 1743
        # sync variables from cpp
        for var in self.desc.all_vars():
            if not self.has_var(var.name()):
                self.create_var(name=var.name(), desc=var, type=var.type())

1744
        # sync variables removed from c++ end
1745
        for var in list(self.vars.keys()):
M
minqiyang 已提交
1746
            if not self.desc.find_var(cpt.to_bytes(var)):
1747 1748
                self.vars.pop(var)

Q
Qiao Longfei 已提交
1749
        # sync operators from cpp
1750 1751 1752 1753
        ops_in_cpp = []
        for op_idx in range(0, self.desc.op_size()):
            ops_in_cpp.append(self.desc.op(op_idx))

Y
Yu Yang 已提交
1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769
        if len(self.ops) != 0:
            first_op_in_python = self.ops[0].desc
            last_op_in_python = self.ops[len(self.ops) - 1].desc
            start_index = None
            end_index = None
            for index in range(len(ops_in_cpp)):
                if first_op_in_python == ops_in_cpp[index]:
                    start_index = index
                if last_op_in_python == ops_in_cpp[index]:
                    end_index = index
            assert start_index is not None
            assert end_index is not None
            assert start_index <= end_index
        else:
            start_index = 0
            end_index = -1
Q
Qiao Longfei 已提交
1770 1771 1772 1773 1774

        # sync ops append to the head of cpp_ops
        for index in range((start_index - 1 - 1), -1, -1):
            op_desc = ops_in_cpp[index]
            op = Operator(self, op_desc)
Q
qiaolongfei 已提交
1775
            self.ops.insert(0, op)
Q
Qiao Longfei 已提交
1776 1777 1778 1779 1780 1781 1782

        # sync ops append to the end of cpp_ops
        for index in range((end_index + 1), len(ops_in_cpp)):
            op_desc = ops_in_cpp[index]
            op = Operator(self, op_desc)
            self.ops.append(op)

1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795
        # sync ops removed from c++ end
        if end_index != -1 and end_index < len(self.ops):
            ops_in_cpp_index = 0
            ops_in_python_index = 0
            while ops_in_python_index < len(
                    self.ops) and ops_in_cpp_index < len(ops_in_cpp):
                if self.ops[ops_in_python_index].desc != ops_in_cpp[
                        ops_in_cpp_index]:
                    del self.ops[ops_in_python_index]
                else:
                    ops_in_cpp_index += 1
                    ops_in_python_index += 1

Q
Qiao Longfei 已提交
1796 1797 1798 1799
        assert len(self.ops) == len(ops_in_cpp)
        for index in range(len(self.ops)):
            assert self.ops[index].desc == ops_in_cpp[index]

W
Wu Yi 已提交
1800
    def _copy_param_info_from(self, other):
1801
        """
1802 1803
        Copy the information of parameters from the other block.

1804
        Args:
1805 1806 1807 1808 1809
            other(Block): the other block.

        Raises:
            ValueError: If type of input is not Block, or the `other` and this
                block is not in the same topology.
1810 1811 1812 1813 1814

        Returns:
            None
        """
        if not isinstance(other, Block):
W
Wu Yi 已提交
1815 1816
            raise TypeError(
                "_copy_param_info_from should be invoked with Block")
1817
        for p in other.iter_parameters():
1818 1819 1820
            assert isinstance(p, Parameter)
            v = self.vars.get(p.name, None)
            if v is None:
W
Wu Yi 已提交
1821
                raise ValueError("_copy_param_info_from should be invoked with "
1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833
                                 "same topology")
            assert isinstance(v, Variable)
            new_p = Parameter(
                block=self,
                shape=v.shape,
                dtype=v.dtype,
                type=v.type,
                lod_level=v.lod_level,
                stop_gradient=p.stop_gradient,
                trainable=p.trainable,
                optimize_attr=p.optimize_attr,
                regularizer=p.regularizer,
F
fengjiayi 已提交
1834
                gradient_clip_attr=p.gradient_clip_attr,
F
fengjiayi 已提交
1835
                error_clip=p.error_clip,
1836 1837 1838
                name=v.name)
            self.vars[new_p.name] = new_p

1839
    def _clone_variable(self, var, force_persistable=True):
1840 1841
        """
        Clone a variable into current block.
1842

1843 1844
        Args:
            var: the variable to be cloned.
1845 1846 1847
            force_persistable(bool): True means setting the result variable to being persistable.
                                     False means setting the persistable the same with that of input var.
                                     default: True.
1848 1849

        Returns:
1850
            Variable: the new  variable cloned from 'var' in current block.
1851 1852
        """
        assert isinstance(var, Variable)
T
update  
typhoonzero 已提交
1853 1854 1855 1856 1857
        ret_var = None
        # make STEP_SCOPES var can be safely cloned.
        if var.type == core.VarDesc.VarType.STEP_SCOPES:
            ret_var = self.create_var(
                name=var.name, persistable=var.persistable, type=var.type)
T
tangwei12 已提交
1858 1859
        elif var.type == core.VarDesc.VarType.RAW:
            ret_var = self.create_var(
T
tangwei12 已提交
1860
                name=var.name, persistable=var.persistable, type=var.type)
T
typhoonzero 已提交
1861 1862 1863 1864 1865 1866
        elif var.type == core.VarDesc.VarType.SELECTED_ROWS:
            ret_var = self.create_var(
                name=var.name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
1867
                persistable=True if force_persistable else var.persistable,
F
fengjiayi 已提交
1868
                is_data=var.is_data)
T
update  
typhoonzero 已提交
1869 1870 1871 1872 1873 1874 1875
        else:
            ret_var = self.create_var(
                name=var.name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
                lod_level=var.lod_level,
1876
                persistable=True if force_persistable else var.persistable,
F
fengjiayi 已提交
1877
                is_data=var.is_data)
T
update  
typhoonzero 已提交
1878
        return ret_var
1879

Y
Yu Yang 已提交
1880

1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975
class IrNode(object):
    """
    Python IrNode. Beneath it is a core.Node, which is used for Ir Pass.
    """

    def __init__(self, node):
        """
        Construct an IrNode using core.Node.

        Args:
            node(core.Node): C++ Node.
        """
        assert isinstance(node,
                          core.Node), 'node must be the instance of core.Node.'
        self.node = node

    def name(self):
        """
        Return the node name.

        Returns:
            str: node name.
        """
        return self.node.name()

    def node_type(self):
        """
        Return the node type.

        Returns:
            core.Node.Type: node type(core.Node.Type.Operation or core.Node.Type.Variable).
        """
        return self.node.node_type()

    def var(self):
        """
        Return the node variable description.

        Returns:
            core.VarDesc: node variable description.
        """
        return self.node.var()

    def op(self):
        """
        Return the node operator description.

        Returns:
            core.OpDesc: node operator description.
        """
        return self.node.op()

    def id(self):
        """
        Return the node id.

        Returns:
            int: node id.
        """
        return self.node.id()

    def is_op(self):
        """
        If the node is an operator, then return true.

        Returns:
            bool: indicate whether the node is an operator.
        """
        return self.node.is_op()

    def is_var(self):
        """
        If the node is a variable, then return true.

        Returns:
            bool: indicate whether the node is a variable.
        """
        return self.node.is_var()

    def is_ctrl_var(self):
        """
        If the node is a control dependence variable, then return true.

        Returns:
            bool: indicate whether the node is a control dependence variable.
        """
        return self.node.is_ctrl_var()

    def clear_inputs(self):
        """
        Clear the node inputs. After executing the `clear_inputs` function,
        the node inputs will be empty.
        """
        self.node.clear_inputs()

1976
    def remove_input_by_id(self, node_id):
1977 1978 1979 1980 1981 1982
        """
        Remove a node from inputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
1983
        self.node.remove_input(node_id)
1984

1985
    def remove_input(self, node):
1986 1987 1988 1989
        """
        Remove a node from inputs.

        Args:
1990
            node(IrNode): the node being removed.
1991
        """
1992
        self.node.remove_input(node.node)
1993

1994
    def append_input(self, node):
1995 1996 1997 1998
        """
        Append a node in inputs.

        Args:
1999
            node(IrNode): the node being appended.
2000
        """
2001
        self.node.append_input(node.node)
2002 2003 2004 2005 2006 2007 2008 2009

    def clear_outputs(self):
        """
        Clear the node outputs. After executing the `clear_outputs` function,
        the node outputs will be empty.
        """
        self.node.clear_outputs()

2010
    def remove_output_by_id(self, node_id):
2011 2012 2013 2014 2015 2016
        """
        Remove a node from outputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
2017
        self.node.remove_output(node_id)
2018

2019
    def remove_output(self, node):
2020 2021 2022 2023
        """
        Remove a node from outputs.

        Args:
2024
            node(IrNode): the node being removed.
2025
        """
2026
        self.node.remove_output(node.node)
2027

2028
    def append_output(self, node):
2029 2030 2031 2032
        """
        Append a node in outputs.

        Args:
2033
            node(IrNode): the node being appended.
2034
        """
2035
        self.node.append_output(node.node)
2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096

    @property
    def inputs(self):
        """
        Return the node inputs.

        Returns:
            list(IrNode): node inputs wrapped by IrNode.
        """
        return [IrNode(n) for n in self.node.inputs]

    @property
    def outputs(self):
        """
        Return the node outputs.

        Returns:
            list(IrNode): node outputs wrapped by IrNode.
        """
        return [IrNode(n) for n in self.node.outputs]


class IrVarNode(IrNode):
    """
    Python IrVarNode. Beneath it is a core.Node, it inherits from IrNode.
    """

    def __init__(self, node):
        """
        Construct an IrVarNode using core.Node.

        Args:
            node(core.Node): C++ Node.
        """
        assert isinstance(node, core.Node) and node.is_var(), \
            'node must be the instance of core.Node and it must be a variable node.'
        super(IrVarNode, self).__init__(node)
        self.node = node

    def set_shape(self, shape):
        """
        Set the node variable shape.

        Args:
            shape(list): shape to be set.
        """
        assert self.node.var() is not None, \
            "The node variable description cannot be None."
        self.node.var().set_shape(shape)

    def persistable(self):
        """
        If the variable node is a persistable variable, then return true.

        Returns:
            bool: indicate whether the variable is persistable.
        """
        assert self.node.var() is not None, \
            "The node variable description cannot be None."
        return self.node.var().persistable()

2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129
    def type(self):
        """
        Return the variable type.

        Returns:
            core.VarDesc.VarType: the variable type.
        """
        assert self.node.var() is not None, \
            "The node variable description cannot be None."
        return self.node.var().type()

    def dtype(self):
        """
        Return the variable data type.

        Returns:
            core.VarDesc.VarType: the variable data type.
        """
        assert self.node.var() is not None, \
            "The node variable description cannot be None."
        return self.node.var().dtype()

    def shape(self):
        """
        Return the variable shape.

        Returns:
            list: the variable shape.
        """
        assert self.node.var() is not None, \
            "The node variable description cannot be None."
        return self.node.var().shape()

2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179
    @property
    def inputs(self):
        """
        Return the node inputs.

        Returns:
            list(IrOpNode): node inputs wrapped by IrOpNode.
        """
        return [IrOpNode(n) for n in self.node.inputs]

    @property
    def outputs(self):
        """
        Return the node outputs.

        Returns:
            list(IrOpNode): node outputs wrapped by IrOpNode.
        """
        return [IrOpNode(n) for n in self.node.outputs]


class IrOpNode(IrNode):
    """
    Python IrOpNode. Beneath it is a core.Node, it inherits from IrNode.
    """

    def __init__(self, node):
        """
        Construct an IrOpNode using core.Node.

        Args:
            node(core.Node): C++ Node.
        """
        assert isinstance(node, core.Node) and node.is_op(), \
            'node must be the instance of core.Node and it must be a operator node.'
        super(IrOpNode, self).__init__(node)
        self.node = node

    def rename_input(self, old_input_name, new_input_name):
        """
        Rename the input of this node.

        Args:
            old_input_name(str): the old input name.
            new_input_name(str): the new input name.
        """
        assert self.node.op() is not None, \
            "The node operator description cannot be None."
        self.node.op()._rename_input(old_input_name, new_input_name)

2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218
    def input(self, name):
        """
        Get the argument name list by the parameter name for input.

        Args:
            name(str): the parameter name.

        Returns:
            list(str): the argument name list.
        """
        assert self.node.op() is not None, \
            "The node operator description cannot be None."
        return self.node.op().input(name)

    def output(self, name):
        """
        Get the argument name list by the parameter name for output.

        Args:
            name(str): the parameter name.

        Returns:
            list(str): the argument name list.
        """
        assert self.node.op() is not None, \
            "The node operator description cannot be None."
        return self.node.op().output(name)

    def set_type(self, new_type):
        """
        Change the operator type into new type.

        Args:
            new_type(str): new operator type to be set.
        """
        assert self.node.op() is not None, \
            "The node operator description cannot be None."
        return self.node.op().set_type(new_type)

2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246
    def set_attr(self, name, val):
        """
        Set the value of attribute by attribute's name.

        Args:
            name(str): the attribute name.
            val(bool|int|str|float|list): the value of the attribute.
        """
        self._update_desc_attr(name, val)

    def _update_desc_attr(self, name, val):
        """
        Update the value of the op desc's attribute by attribute's name.
        """
        assert self.node.op() is not None, \
            "The node operator description cannot be None."
        desc = self.node.op()
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and \
            all(isinstance(v, Block) for v in val):
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
            isinstance(val, core.ProgramDesc):
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)

2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268
    def input_arg_names(self):
        """
        Return input arguments' names of this op node.

        Returns:
            list(str): input arguments' names of this op node.
        """
        assert self.node.op() is not None, \
            "The node operator description cannot be None."
        return self.node.op().input_arg_names()

    def output_arg_names(self):
        """
        Return output arguments' names of this op node.

        Returns:
            list(str): output arguments' names of this op node.
        """
        assert self.node.op() is not None, \
            "The node operator description cannot be None."
        return self.node.op().output_arg_names()

2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289
    @property
    def inputs(self):
        """
        Return the node inputs.

        Returns:
            list(IrVarNode): node inputs wrapped by IrVarNode.
        """
        return [IrVarNode(n) for n in self.node.inputs]

    @property
    def outputs(self):
        """
        Return the node outputs.

        Returns:
            list(IrVarNode): node outputs wrapped by IrVarNode.
        """
        return [IrVarNode(n) for n in self.node.outputs]


2290 2291
class IrGraph(object):
    """
2292
    Python IrGraph. Beneath it is a core.Graph, which is used for
2293
    creating a c++ Ir Pass Graph. An IrGraph is just a graph view of
2294 2295
    a Program. In an IrGraph, both Variables and Operators are graph
    nodes.
2296 2297 2298 2299
    """

    def __init__(self, graph, for_test=False):
        """
2300 2301
        Construct an IrGraph using core.Graph.

2302 2303 2304 2305 2306 2307 2308 2309 2310
        Args:
            graph(core.Graph): C++ Graph.
            for_test(bool): True for the test graph and false for the train graph.
        """
        assert isinstance(
            graph, core.Graph), 'graph must be the instance of core.Graph.'
        self.graph = graph
        self._for_test = for_test

2311 2312 2313 2314
    def clone(self):
        """
        Create a new and duplicated IrGraph.

2315 2316 2317
        Warns:
            The method only clones the graph structure, not its attributes.

2318 2319 2320
        Returns:
            IrGraph: A new and duplicated graph.
        """
2321
        g = self.graph.clone()
2322 2323
        return IrGraph(g, self._for_test)

2324
    def is_test(self):
2325 2326 2327
        """
        If the graph is used for testing, the function returns true. Otherwise, returns false.
        """
2328 2329
        return self._for_test

W
WangZhen 已提交
2330
    def all_nodes(self):
2331 2332 2333
        """
        Return all nodes included in the graph as a set.
        """
2334
        return {IrNode(node) for node in self.graph.nodes()}
2335

2336
    def all_var_nodes(self):
2337 2338 2339
        """
        Return all variable nodes included in the graph as a set.
        """
2340
        return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()}
2341

2342
    def all_persistable_nodes(self):
2343 2344 2345
        """
        Return all persistable variable nodes included in the graph as a set.
        """
W
WangZhen 已提交
2346 2347 2348 2349 2350
        persistable_nodes = set()
        for node in self.graph.nodes():
            if node.is_var() and node.var() is not None and node.var(
            ).persistable():
                persistable_nodes.add(node)
2351
        return {IrVarNode(p) for p in persistable_nodes}
W
WangZhen 已提交
2352

2353
    def all_op_nodes(self):
2354 2355 2356
        """
        Return all operator nodes included in the graph as a set.
        """
2357
        return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
2358

2359
    def create_persistable_node(self, name, var_type, shape, var_dtype):
2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370
        """
        Create a persistable variable node in the graph. In IrGraph,
        it can not distinguish between persistable variables and parameters.

        Args:
            name(str): the name of the persistable variable node.
            vart_type(core.VarDesc.VarType): the type of the persistable variable node.
            shape(list): the shape of the persistable variable node.
            var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.

        Returns:
2371
            IrVarNode: the created persistable variable node.
2372
        """
2373 2374 2375 2376 2377
        var_desc = core.VarDesc(name)
        var_desc.set_type(var_type)
        var_desc.set_shape(shape)
        var_desc.set_dtype(var_dtype)
        var_desc.set_persistable(True)
2378
        return IrVarNode(self.graph.create_var_node(var_desc))
2379 2380

    def create_var_node(self, name, var_type, shape, var_dtype):
2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391
        """
        Create a variable node in the graph. The created variable node is
        not persistable.

        Args:
            name(str): the name of the variable node.
            vart_type(core.VarDesc.VarType): the type of the variable node.
            shape(list): the shape of the variable node.
            var_dtype(core.VarDesc.VarType): the data type of the variable node.

        Returns:
2392
            IrVarNode: the created variable node.
2393 2394
        """

2395 2396 2397 2398
        var_desc = core.VarDesc(name)
        var_desc.set_type(var_type)
        var_desc.set_shape(shape)
        var_desc.set_dtype(var_dtype)
2399
        return IrVarNode(self.graph.create_var_node(var_desc))
2400 2401

    def create_var_node_from_desc(self, var_desc):
2402 2403 2404 2405 2406 2407 2408 2409
        """
        Create a variable node by using an existing VarDesc in the graph.
        Depend on the giving VarDesc, the created variable node may be persistable.

        Args:
            var_desc(core.VarDesc): the giving variable description.

        Returns:
2410
            IrVarNode: the created variable node.
2411
        """
2412
        return IrVarNode(self.graph.create_var_node(var_desc))
2413 2414

    def create_op_node(self, op_type, attrs, inputs, outputs):
2415 2416 2417 2418 2419 2420 2421 2422 2423 2424
        """
        Create a operator node in the graph.

        Args:
            op_type(str): the type of the operator node.
            attrs(dict): the attributes of the operator node.
            inputs(dict): the inputs of the operator node.
            outputs(dict): the outpus of the operator node.

        Returns:
2425
            IrOpNode: the created operator node.
2426
        """
2427 2428
        op_desc = core.OpDesc()
        op_desc.set_type(op_type)
2429
        for attr, value in six.iteritems(attrs):
2430
            self._update_desc_attr(op_desc, attr, value)
2431
        for input_name, var_nodes in six.iteritems(inputs):
2432 2433 2434 2435
            if not isinstance(var_nodes, list):
                var_nodes = [var_nodes]
            op_desc.set_input(input_name,
                              [var_node.name() for var_node in var_nodes])
2436
        for output_name, var_nodes in six.iteritems(outputs):
2437 2438 2439 2440
            if not isinstance(var_nodes, list):
                var_nodes = [var_nodes]
            op_desc.set_output(output_name,
                               [var_node.name() for var_node in var_nodes])
2441
        return IrOpNode(self.graph.create_op_node(op_desc))
2442 2443

    def create_op_node_from_desc(self, op_desc):
2444 2445 2446 2447 2448 2449 2450
        """
        Create a operator node by using an existing OpDesc in the graph.

        Args:
            op_desc(core.VarDesc): the giving operator description.

        Returns:
2451
            IrOpNode: the created operator node.
2452
        """
2453
        return IrOpNode(self.graph.create_op_node(op_desc))
2454 2455

    def update_input_link(self, old_input_node, new_input_node, op_node):
2456 2457 2458 2459
        """
        Update the input's link of a operator node.

        Args:
2460 2461 2462
            old_input_node(IrNode): the old input node of the giving op_node.
            new_input_node(IrNode): the new input node of the giving op_node.
            op_node(IrOpNode): the operator node that is needed to update input's link.
2463
        """
2464 2465
        assert old_input_node.node in self.graph.nodes() and new_input_node.node in \
        self.graph.nodes() and op_node.node in self.graph.nodes(), \
W
WangZhen 已提交
2466
        'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
2467 2468 2469 2470
        old_input_node.remove_output(op_node)
        op_node.remove_input(old_input_node)
        new_input_node.append_output(op_node)
        op_node.append_input(new_input_node)
2471
        op_node.rename_input(old_input_node.name(), new_input_node.name())
2472 2473

    def link_to(self, node_in, node_out):
2474 2475 2476 2477
        """
        Connect two nodes.

        Args:
2478 2479
            node_in(IrNode): the input node.
            node_out(IrNode): the output node.
2480
        """
2481
        assert node_in.node in self.graph.nodes() and node_out.node in self.graph.nodes(), \
W
WangZhen 已提交
2482
            'The two arguments(node_in&node_out) must be in the graph nodes.'
2483 2484
        node_in.append_output(node_out)
        node_out.append_input(node_in)
2485 2486

    def safe_remove_nodes(self, remove_nodes):
2487 2488 2489 2490 2491 2492 2493
        """
        Remove nodes safely since links connected to these removed nodes are
        also removed.

        Args:
            remove_nodes(set): the nodes prepared to be removed.
        """
2494
        if not isinstance(remove_nodes, set):
W
WangZhen 已提交
2495 2496 2497 2498
            if isinstance(remove_nodes, Iterable):
                remove_nodes = set(remove_nodes)
            else:
                remove_nodes = {remove_nodes}
2499 2500
        original_nodes = {n.node for n in remove_nodes}
        core.graph_safe_remove_nodes(self.graph, original_nodes)
2501

Z
Zhen Wang 已提交
2502 2503 2504 2505 2506 2507 2508 2509
    def resolve_hazard(self):
        ordered_nodes = core.topology_sort(self.graph)
        var_nodes = dict()
        for node in ordered_nodes:
            if node.is_op() and node.op() is not None:
                for each_var_name in node.op().input_arg_names():
                    if each_var_name not in var_nodes:
                        var_nodes[each_var_name] = [
2510
                            self._find_node_by_name(node.inputs, each_var_name)
Z
Zhen Wang 已提交
2511 2512 2513 2514
                        ]
                for each_var_name in node.op().output_arg_names():
                    if each_var_name not in var_nodes:
                        var_nodes[each_var_name] = [
2515
                            self._find_node_by_name(node.outputs, each_var_name)
Z
Zhen Wang 已提交
2516 2517 2518
                        ]
                    else:
                        var_nodes[each_var_name].append(
2519 2520
                            self._find_node_by_name(node.outputs,
                                                    each_var_name))
Z
Zhen Wang 已提交
2521 2522
        self.graph.resolve_hazard(var_nodes)

W
WangZhen 已提交
2523
    def has_circle(self):
2524 2525 2526 2527 2528 2529
        """
        Check if the graph has a circle.

        Returns:
            bool: True if the graph has a circle else False.
        """
W
WangZhen 已提交
2530 2531 2532
        return core.has_circle(self.graph)

    def graph_num(self):
2533 2534 2535 2536 2537 2538
        """
        Count the number of unconnected graphs in this graph.

        Returns:
            int: the number of unconnected graphs.
        """
W
WangZhen 已提交
2539 2540 2541
        return core.graph_num(self.graph)

    def topology_sort(self):
2542 2543 2544 2545 2546 2547
        """
        Perform the topology sort operation on the graph.

        Notes: the `graph` cannot contain a circle.

        Returns:
Z
Zhen Wang 已提交
2548
            list(IrNode): nodes in topology order.
2549
        """
2550
        ordered_nodes = core.topology_sort(self.graph)
Z
Zhen Wang 已提交
2551
        return [IrNode(n) for n in ordered_nodes]
W
WangZhen 已提交
2552 2553

    def build_adjacency_list(self):
2554 2555 2556 2557
        """
        Build an adjacency list of operations for the `graph`.

        Returns:
2558
            dict{IrNode: set(IrNode)}: the adjacency list.
2559
        """
2560 2561 2562 2563 2564
        adj_list = core.build_adjacency_list(self.graph)
        wrapped_adj_list = dict()
        for k, v in six.iteritems(adj_list):
            wrapped_adj_list[IrNode(k)] = {IrNode(n) for n in v}
        return wrapped_adj_list
W
WangZhen 已提交
2565

2566 2567 2568 2569 2570 2571 2572 2573
    def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True):
        """
        Draw the graph. If `dot` command is installed, the drawn graph
        will be saved as pdf file type, otherwise dot file type is used.

        Args:
            save_path(str): the save path of drawn graph.
            name(str): the name of drawn graph.
2574
            marked_nodes(set(IrNode)): nodes that are needed to be marked.
2575 2576 2577 2578 2579
            Default value is None.
            remove_ctr_var(bool): If it is set True, all control variable nodes
            in the graph will be removed. Default value is True.
        """

2580 2581 2582 2583 2584 2585 2586 2587 2588
        def _convert_to_pdf(dot_file_path):
            pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
            exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
                            + ' -o ' + pdf_save_path, shell=True)
            if exited_code != 0:
                print('The dot command is needed for creating pdf files.')
                print('The {} is saved as the dot filetype.'.format(
                    dot_file_path))

2589
        remove_ctr_vars = set()
2590
        if remove_ctr_var:
2591
            for node in self.all_var_nodes():
2592 2593 2594
                if node.is_ctrl_var():
                    remove_ctr_vars.add(node)
            self.safe_remove_nodes(remove_ctr_vars)
2595 2596
        print('Total ops num = {}.'.format(len(self.all_op_nodes())))

2597 2598
        if marked_nodes is not None:
            if not isinstance(marked_nodes, set):
2599 2600 2601 2602 2603 2604
                if isinstance(marked_nodes, Iterable):
                    marked_nodes = set(marked_nodes)
                else:
                    marked_nodes = {marked_nodes}
            marked_nodes = {n.node for n in marked_nodes}
            remove_ctr_vars = {n.node for n in remove_ctr_vars}
2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615
            marked_nodes = marked_nodes - remove_ctr_vars
            if self.graph.has('__graphviz__marked_node__'):
                self.graph.erase('__graphviz__marked_node__')
            self.graph.set('__graphviz__marked_node__', marked_nodes)
        viz_dot_path = os.path.join(save_path, name) + '.dot'
        viz_pass = core.get_pass('graph_viz_pass')
        viz_pass.set('graph_viz_path', viz_dot_path)
        viz_pass.apply(self.graph)
        _convert_to_pdf(viz_dot_path)

    def to_program(self):
2616 2617 2618
        """
        Convert the graph into a Program.

Z
Zhen Wang 已提交
2619
        WARN: When the graph includes backward operator nodes, the
2620 2621 2622 2623 2624 2625
        conversion process may be failed. Usually, this function is
        only used to convert a test graph.

        Returns:
            Program: a program converted from the graph.
        """
2626
        convert_pass = core.get_pass('graph_to_program_pass')
2627 2628
        desc = core.ProgramDesc()
        convert_pass.set_not_owned('program', desc)
2629 2630 2631 2632
        convert_pass.apply(self.graph)
        program = Program._construct_from_desc(desc)
        return program

2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643
    def _find_node_by_name(self, nodes, node_name):
        """
        Find a node in the giving nodes set by the name.
        """
        target_node = None
        for n in nodes:
            if n.name() == node_name:
                target_node = n
        assert target_node is not None, "Cannot find the target node in the giving set."
        return target_node

2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659
    def _update_desc_attr(self, desc, name, val):
        """
        Update the value of desc's attribute by attribute's name.
        """
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and all(
                isinstance(v, Block) for v in val):
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
                isinstance(val, core.ProgramDesc):
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)


Y
Yu Yang 已提交
2660
class Program(object):
D
dzhwinter 已提交
2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671
    """
    Python Program. Beneath it is a ProgramDesc, which is used for
    create c++ Program. A program is a self-contained programing
    language like container. It has at least one Block, when the
    control flow op like conditional_block, while_op is included,
    it will contains nested block.
    Please reference the framework.proto for details.

    Notes: we have default_startup_program and default_main_program
    by default, a pair of them will shared the parameters.
    The default_startup_program only run once to initialize parameters,
Y
yuyang18 已提交
2672
    default_main_program run in every mini batch and adjust the weights.
D
dzhwinter 已提交
2673 2674

    Returns:
Y
yuyang18 已提交
2675
        A empty program.
D
dzhwinter 已提交
2676 2677

    Examples:
Y
yuyang18 已提交
2678 2679 2680 2681 2682 2683
        >>> main_program = fluid.Program()
        >>> startup_program = fluid.Program()
        >>> with fluid.program_guard(main_program=main_program, startup_program=startup_program):
        >>>     fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
        >>>     fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
        >>>     fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu")
D
dzhwinter 已提交
2684 2685 2686

    """

2687 2688
    def __init__(self):
        self.desc = core.ProgramDesc()
Y
Yu Yang 已提交
2689 2690
        self.blocks = [Block(self, 0)]
        self.current_block_idx = 0
D
dzhwinter 已提交
2691
        self._seed = 0
Y
yuyang18 已提交
2692
        self._current_role = core.op_proto_and_checker_maker.OpRole.Forward
Y
yuyang18 已提交
2693
        self._op_role_var = []
T
tangwei12 已提交
2694

2695 2696
        # for distribute training
        # _is_distributed = True if under distributed training
T
tangwei12 已提交
2697
        self._is_distributed = False
2698
        # _is_chief = True if the trainer is the first one, usually No.0
T
tangwei12 已提交
2699
        self._is_chief = False
2700 2701 2702
        # _parameters_on_pservers records all the parameters distributed on parameter servers.
        self._parameters_on_pservers = None
        # _endpoints is a list about parameter servers ip:port, such as ["ip:port","ip:port"]
T
tangwei12 已提交
2703
        self._endpoints = []
2704 2705 2706
        # if current role is parameter server, the _ps_endpoint is its "ip:port"
        self._ps_endpoint = None
        # trainers_endpoints, it is used for distribution.
2707
        self._trainers_endpoints = []
2708
        # the distributed lookup table names
T
tangwei12 已提交
2709
        self._distributed_lookup_table = None
2710 2711 2712 2713

        # use Deep gradient comrepssion or not
        self._enable_dgc = False

D
dzhwinter 已提交
2714
        # @deprecated(the python memory optimize transpiler is deprecated)
D
dzhwinter 已提交
2715
        # whether the program is optimized by memory_optimize_transpiler
D
dzhwinter 已提交
2716
        self.__is_mem_optimized = False
D
dzhwinter 已提交
2717

2718 2719 2720
        # if this program has been optimized by distributed optimizer
        # fleet_opt will be given a value
        self._fleet_opt = None
D
dongdaxiang 已提交
2721
        self._program_config = None
2722

D
dzhwinter 已提交
2723
    @property
D
dzhwinter 已提交
2724
    def _is_mem_optimized(self):
D
dzhwinter 已提交
2725 2726
        # if the program is optimized, operator input/outputs
        # maybe same, which conflict with save_inference_model.
D
dzhwinter 已提交
2727
        return self.__is_mem_optimized
D
dzhwinter 已提交
2728

D
dzhwinter 已提交
2729 2730 2731
    @_is_mem_optimized.setter
    def _is_mem_optimized(self, target):
        self.__is_mem_optimized = target
Y
yuyang18 已提交
2732 2733 2734

    @property
    def op_role(self):
Y
yuyang18 已提交
2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747
        """
        The operator role. In a enum {Forward, Backward, Optimize}.

        Notes: this is a low level API. It is used only for ParallelExecutor to
        duplicate or schedule operator to devices.

        For example, the forward operator should be executed on every device.
        The backward operator should be executed on every device and the
        parameter gradient of backward (use :code:`op_role_var` to get this
        variable) operator should be merged to one device. The optimization
        operators should be executed on only one device and broadcast the
        optimization result, i.e., the new parameter, to every other device.
        """
Y
yuyang18 已提交
2748 2749 2750
        return self._current_role

    @op_role.setter
D
dzhwinter 已提交
2751
    def op_role(self, role):
Y
yuyang18 已提交
2752 2753 2754 2755
        self._current_role = role

    @property
    def op_role_var(self):
Y
yuyang18 已提交
2756 2757 2758 2759 2760 2761 2762
        """
        The auxiliary variables for :code:`op_role` property.

        See Also: :code:`Program.op_role`'s documentation for details.

        Notes: This is a very low-level API. Users should not use it directly.
        """
Y
yuyang18 已提交
2763 2764 2765 2766
        return self._op_role_var

    @op_role_var.setter
    def set_op_role_var(self, var_name):
Y
yuyang18 已提交
2767
        self._op_role_var = [var_name]
Y
yuyang18 已提交
2768

2769 2770 2771 2772 2773 2774 2775 2776 2777
    @contextlib.contextmanager
    def _backward_role_guard(self):
        tmp_role = self._current_role

        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Backward
        yield
        self._current_role = tmp_role

S
rename  
sneaxiy 已提交
2778
    @signature_safe_contextmanager
W
Wu Yi 已提交
2779
    def _optimized_guard(self, param_and_grads):
Y
yuyang18 已提交
2780 2781 2782 2783 2784 2785 2786
        """
        A with guard to set :code:`Optimization` :code:`OpRole` and
        :code:`OpRoleVar` automatically.

        Notes: This is a very low level API. Users should not use it directly.

        Args:
2787
            param_and_grads(list): The variables (names) to be optimized.
Y
yuyang18 已提交
2788 2789 2790 2791

        Examples:

            >>> p, g = backward(...)
W
Wu Yi 已提交
2792
            >>> with program._optimized_guard([p,g]):
Y
yuyang18 已提交
2793 2794
            >>>     p = p - 0.001 * g
        """
X
Xin Pan 已提交
2795 2796 2797
        tmp_role = self._current_role
        tmp_var = self._op_role_var

Y
yuyang18 已提交
2798 2799
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Optimize
2800 2801 2802 2803
        self._op_role_var = [
            var.name if isinstance(var, Variable) else var
            for var in param_and_grads
        ]
Y
yuyang18 已提交
2804
        yield
X
Xin Pan 已提交
2805 2806
        self._op_role_var = tmp_var
        self._current_role = tmp_role
Y
Yu Yang 已提交
2807

S
rename  
sneaxiy 已提交
2808
    @signature_safe_contextmanager
X
Xin Pan 已提交
2809
    def _lr_schedule_guard(self, is_with_opt=False):
2810 2811 2812 2813 2814 2815 2816
        """
        A with guard to set :code:`LRSched` :code:`OpRole` and
        :code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
        set to the target learning rate.

        Notes: This is a very low level API. Users should not use it directly.

X
Xin Pan 已提交
2817 2818 2819 2820
        Args:
            is_with_opt: Only set to true if these ops a in the middle
                 of a bunch of optimize ops so that it can be treated
                 correctly. For example, sgd->lr_op->sgd->lr_op->sgd.
2821 2822 2823 2824 2825 2826 2827

        Examples:

            >>> p, g = backward(...)
            >>> with program.lr_schedule_guard():
            >>>     lr = lr * decay
        """
2828 2829 2830 2831

        tmp_role = self._current_role
        tmp_var = self._op_role_var

2832 2833
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.LRSched
X
Xin Pan 已提交
2834 2835
        if is_with_opt:
            self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
2836 2837 2838
        # TODO(typhoonzero): how to set target learning rate var
        self._op_role_var = []
        yield
2839 2840
        self._op_role_var = tmp_var
        self._current_role = tmp_role
2841

2842
    def __str__(self):
Y
yuyang18 已提交
2843 2844 2845 2846 2847 2848 2849 2850 2851
        """
        Get the protobuf debug string of this Program.

        Returns:
            (str): The protobuf debug string.

        Raises:
            ValueError: If any of required fields is not set.
        """
Y
Yang Yang(Tony) 已提交
2852 2853
        return self.to_string(True)

F
fengjiayi 已提交
2854 2855 2856
    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
Y
yuyang18 已提交
2857

F
fengjiayi 已提交
2858
        Args:
Y
yuyang18 已提交
2859 2860
            throw_on_error(bool): raise Value error when any of required fields
                is not set.
F
fengjiayi 已提交
2861

Y
yuyang18 已提交
2862 2863 2864 2865
            with_details(bool): True if more details about variables and
                parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need
                to print.

H
haowang101779990 已提交
2866 2867
        Returns:
            str : The debug string.
Y
yuyang18 已提交
2868 2869 2870 2871

        Raises:
            ValueError: If any of required fields is not set and throw_on_error is
                True.
F
fengjiayi 已提交
2872 2873 2874 2875 2876 2877 2878 2879 2880 2881

        """
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
        if with_details:
            res_str = ""
            for block in self.blocks:
                res_str += block.to_string(throw_on_error, with_details)
        else:
            protostr = self.desc.serialize_to_string()
2882 2883
            proto = framework_pb2.ProgramDesc.FromString(
                six.binary_type(protostr))
F
fengjiayi 已提交
2884 2885
            res_str = _debug_string_(proto, throw_on_error)
        return res_str
2886

W
Wu Yi 已提交
2887
    def _get_desc(self):
Y
yuyang18 已提交
2888 2889 2890 2891 2892 2893 2894
        """
        Get the C++ side of `ProgramDesc` object pointer. The C++ object is
        exposed by :code:`pybind`.

        Notes: This is a very low level API. Users should not use this API
        directly.
        """
2895 2896
        return self.desc

X
version  
Xin Pan 已提交
2897 2898 2899
    def _version(self):
        return self.desc._version()

2900
    def clone(self, for_test=False):
Y
yuyang18 已提交
2901 2902 2903
        """
        Create a new, duplicated program.

2904

Y
yuyang18 已提交
2905 2906 2907 2908
        Some operators, e.g., :code:`batch_norm`, behave differently between
        training and testing. They have an attribute, :code:`is_test`, to
        control this behaviour. This method will change the :code:`is_test`
        attribute of them to :code:`True` when :code:`for_test=True`.
2909

Y
yuyang18 已提交
2910 2911 2912 2913
        * Set for_test to False when we want to clone the program for training.
        * Set for_test to True when we want to clone the program for testing.

        Notes: This API DOES NOT prune any operator. Use
L
Luo Tao 已提交
2914 2915 2916 2917 2918
        :code:`clone(for_test=True)` before backward and optimization please. e.g.

            >>> test_program = fluid.default_main_program().clone(for_test=True)
            >>> optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
            >>> optimizer.minimize()
2919 2920

        Args:
Y
yuyang18 已提交
2921 2922
            for_test(bool): True if change the :code:`is_test` attribute of
                operators to :code:`True`.
2923

D
dzhwinter 已提交
2924
        Returns:
Y
yuyang18 已提交
2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977
            Program: The new, duplicated Program object.

        Examples:

            1. To clone a test program, the sample code is:

            >>> import paddle.fluid as fluid
            >>> train_program = fluid.Program()
            >>> startup_program = fluid.Program()
            >>> with fluid.program_guard(train_program, startup_program):
            >>>     img = fluid.layers.data(name='image', shape=[784])
            >>>     hidden = fluid.layers.fc(input=img, size=200, act='relu')
            >>>     hidden = fluid.layers.dropout(hidden, dropout_prob=0.5)
            >>>     loss = fluid.layers.cross_entropy(
            >>>                 input=fluid.layers.fc(hidden, size=10, act='softmax'),
            >>>                 label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
            >>>
            >>> test_program = train_program.clone(for_test=True)
            >>>
            >>> sgd = fluid.optimizer.SGD(learning_rate=1e-3)
            >>> with fluid.program_guard(train_program, startup_program):
            >>>     sgd.minimize(loss)

            2. The :code:`clone` method can be avoid if you create program for
            training and program for testing individually.

            >>> import paddle.fluid as fluid
            >>>
            >>> def network(is_test):
            >>>     img = fluid.layers.data(name='image', shape=[784])
            >>>     hidden = fluid.layers.fc(input=img, size=200, act='relu')
            >>>     hidden = fluid.layers.dropout(hidden, dropout_prob=0.5, is_test=is_test)
            >>>     loss = fluid.layers.cross_entropy(
            >>>                 input=fluid.layers.fc(hidden, size=10, act='softmax'),
            >>>                 label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
            >>>     return loss
            >>>
            >>> train_program = fluid.Program()
            >>> startup_program = fluid.Program()
            >>> test_program = fluid.Program()
            >>>
            >>> with fluid.program_guard(train_program, startup_program):
            >>>     with fluid.unique_name.guard():
            >>>         loss = network(is_test=False)
            >>>         sgd = fluid.optimizer.SGD(learning_rate=1e-3)
            >>>         sgd.minimize(loss)
            >>>
            >>> # the test startup program is not used.
            >>> with fluid.program_guard(test_program, fluid.Program()):
            >>>     with fluid.unique_name.guard():
            >>>         loss = network(is_test=True)

            The two code snippets above will generate same programs.
2978 2979
        """
        if for_test:
X
Xin Pan 已提交
2980
            p = self._inference_optimize(prune_read_op=False)
2981
        else:
2982
            p = Program()
G
gongweibao 已提交
2983 2984
            p.current_block_idx = self.current_block_idx
            p._seed = self._seed
2985
            p.desc = core.ProgramDesc(self.desc)
M
minqiyang 已提交
2986 2987 2988
            p.blocks = [
                Block(p, i) for i in six.moves.range(self.desc.num_blocks())
            ]
G
gongweibao 已提交
2989 2990 2991 2992

            p._current_role = self._current_role
            p._op_role_var = self._op_role_var

W
Wu Yi 已提交
2993
            p._sync_with_cpp()
2994

W
Wu Yi 已提交
2995
        p._copy_param_info_from(self)
W
Wu Yi 已提交
2996
        p._copy_data_info_from(self)
2997
        p._copy_dist_param_info_from(self)
Y
Yu Yang 已提交
2998
        return p
2999

W
Wu Yi 已提交
3000
    def _prune(self, targets):
Y
yuyang18 已提交
3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015
        """
        Prune operators and variables which are not needed to generate
        :code:`targets`.

        Notes: This is a very low level API. Users should not use this API
        directly. This API is in flux and not stable.

        Args:
            targets(list|Variable|Operator): A list of variables or operators
                need to be pruned

        Returns:
            Program:  A new, pruned program.

        """
3016 3017 3018 3019 3020 3021
        if not isinstance(targets, list):
            targets = [targets]
        targets_idx = []
        for t in targets:
            if not isinstance(t, Operator):
                if isinstance(t, Variable):
3022 3023
                    # After transpiler processing, the op that output this
                    # variable maybe has been changed, so t.op is not reliable
3024
                    # and we need to find the current op that generate this
3025 3026 3027 3028 3029 3030 3031 3032
                    # variable here.
                    t.op = None
                    global_block = self.global_block()
                    for idx, op in enumerate(global_block.ops):
                        if t.name in op.output_arg_names:
                            t.op = op
                            break

3033
                    t = t.op
3034 3035 3036 3037
                    if t is None:
                        raise ValueError(
                            "The target variable must have an "
                            "associated operator that generates it.")
3038
                else:
3039 3040
                    raise ValueError("All targets of prune() can only be "
                                     "Variable or Operator.")
3041 3042 3043 3044

            targets_idx.append([t.block.idx, t.idx])
        res = Program()
        res.desc = core.prune(self.desc, targets_idx)
M
minqiyang 已提交
3045 3046 3047
        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
W
Wu Yi 已提交
3048
        res._sync_with_cpp()
3049 3050
        return res

X
Xin Pan 已提交
3051
    def _inference_optimize(self, prune_read_op=True):
Y
yuyang18 已提交
3052
        """
F
fengjiayi 已提交
3053 3054 3055 3056 3057
        This method will create a new program and do following adjustments on it:
        1. Remove all reader variables and their creator ops if exist.

        2. Remove the :code:`read_op` if exists.

3058
        3. change the :code:`is_test`
Y
yuyang18 已提交
3059 3060 3061
        attribute of operators to :code:`True`. All the :code:`Parameter`
        information will be lost.

3062
        Args:
X
Xin Pan 已提交
3063 3064
            prune_read_op(bool): remove the read ops that are added by py_reader
                                 for cpp inference library
3065

Y
yuyang18 已提交
3066 3067 3068 3069 3070 3071
        Notes: This API is a very low level API. Use
        :code:`Program.clone(for_test=True)` instead.

        Returns:
            Program: The new program.
        """
3072
        res = Program()
3073
        res.desc = core.ProgramDesc(self.desc)
F
fengjiayi 已提交
3074 3075 3076 3077

        # remove all readers and the read_op if exist
        read_op_idx = 0
        root_block = res.desc.block(0)
X
Xin Pan 已提交
3078
        if prune_read_op:
3079 3080 3081 3082 3083 3084 3085 3086 3087
            while True:
                if read_op_idx >= root_block.op_size() or root_block.op(
                        read_op_idx).type() == 'read':
                    break
                read_op_idx += 1
            if read_op_idx < root_block.op_size():
                root_block._remove_op(0, read_op_idx + 1)
            for var in root_block.all_vars():
                if var.type() == core.VarDesc.VarType.READER:
M
minqiyang 已提交
3088
                    root_block._remove_var(cpt.to_bytes(var.name()))
F
fengjiayi 已提交
3089 3090

        # change all `is_test` attributes to True
M
minqiyang 已提交
3091
        for i in six.moves.range(res.desc.num_blocks()):
3092
            block = res.desc.block(i)
M
minqiyang 已提交
3093
            for j in six.moves.range(block.op_size()):
3094 3095
                op = block.op(j)
                if op.has_attr('is_test'):
W
Wu Yi 已提交
3096
                    op._set_attr('is_test', True)
M
minqiyang 已提交
3097 3098 3099
        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
W
Wu Yi 已提交
3100
        res._sync_with_cpp()
3101 3102
        return res

3103 3104
    @staticmethod
    def parse_from_string(binary_str):
Y
yuyang18 已提交
3105 3106 3107 3108 3109 3110 3111
        """
        Deserialize a program desc from protobuf binary string.

        Notes: All information about parameters will be lost after serialization
        and deserialization.

        Args:
3112
            binary_str_type(str): The binary prootbuf string.
Y
yuyang18 已提交
3113 3114 3115 3116

        Returns:
            Program: A deserialized program desc.
        """
3117 3118
        p = Program()
        p.desc = core.ProgramDesc(binary_str)
M
minqiyang 已提交
3119
        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
W
Wu Yi 已提交
3120
        p._sync_with_cpp()
3121
        return p
Y
Yu Yang 已提交
3122

3123
    @staticmethod
3124
    def _construct_from_desc(desc):
3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139
        """
        Construct a program from program desc.

        Args:
            desc(core.ProgramDesc): The program desc for constructing.

        Returns:
            Program: A program.
        """
        p = Program()
        p.desc = desc
        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
        p._sync_with_cpp()
        return p

D
dzhwinter 已提交
3140 3141
    @property
    def random_seed(self):
Y
yuyang18 已提交
3142 3143 3144 3145 3146 3147
        """
        The default random seed for random operators in Program. Zero means get
        the random seed from random device.

        Notes: It must be set before the operators have been added.
        """
D
dzhwinter 已提交
3148 3149
        return self._seed

Q
qiaolongfei 已提交
3150 3151
    @property
    def num_blocks(self):
Y
yuyang18 已提交
3152 3153 3154
        """
        The number of blocks in this program.
        """
Q
qiaolongfei 已提交
3155 3156
        return self.desc.num_blocks()

D
dzhwinter 已提交
3157 3158 3159 3160 3161 3162
    @random_seed.setter
    def random_seed(self, seed):
        if not isinstance(seed, int):
            raise ValueError("Seed must be a integer.")
        self._seed = seed

Y
Yu Yang 已提交
3163
    def __repr__(self):
3164
        return self.__str__()
3165

Y
Yu Yang 已提交
3166
    def global_block(self):
Y
yuyang18 已提交
3167 3168 3169
        """
        Get the first block of this program.
        """
Y
Yu Yang 已提交
3170 3171
        return self.blocks[0]

Q
Qiao Longfei 已提交
3172
    def block(self, index):
Y
yuyang18 已提交
3173 3174 3175 3176 3177 3178 3179 3180
        """
        Get the :code:`index` block of this program
        Args:
            index(int): The index of block to get

        Returns:
            Block: The :code:`index` block
        """
Q
Qiao Longfei 已提交
3181 3182
        return self.blocks[index]

Y
Yu Yang 已提交
3183
    def current_block(self):
Y
yuyang18 已提交
3184 3185 3186 3187
        """
        Get the current block. The :code:`current` block is the block to append
        operators.
        """
Y
Yu Yang 已提交
3188 3189
        return self.blocks[self.current_block_idx]

W
Wu Yi 已提交
3190
    def _create_block(self, parent_idx=None):
Y
yuyang18 已提交
3191 3192 3193 3194 3195 3196 3197 3198 3199 3200
        """
        Create a new block with the :code:`parent_idx` and change the current block
        to new block.

        Args:
            parent_idx(int): The parent block index.

        Returns:
            Block: The new block.
        """
Y
Yu Yang 已提交
3201
        new_block_idx = len(self.blocks)
F
update  
fengjiayi 已提交
3202 3203 3204
        parent = self.current_block() if parent_idx is None else self.block(
            parent_idx)
        self.desc.append_block(parent.desc)
Y
Yu Yang 已提交
3205 3206 3207 3208
        self.current_block_idx = new_block_idx
        self.blocks.append(Block(self, self.current_block_idx))
        return self.current_block()

W
Wu Yi 已提交
3209
    def _rollback(self):
Y
yuyang18 已提交
3210 3211 3212 3213 3214
        """
        Exit a code block, i.e., roll back to the parent block.
        Returns:
            None
        """
Y
Yu Yang 已提交
3215 3216
        self.current_block_idx = self.current_block().parent_idx

W
Wu Yi 已提交
3217
    def _sync_with_cpp(self):
Y
yuyang18 已提交
3218 3219 3220 3221 3222 3223 3224 3225 3226 3227
        """
        Synchronize Python instance to its binding C++ object instance.
        If the program is modified in C++ space, this method should be invoked.

        Notes: This is a very low level API. Users should not invoke it
        directly.

        Returns:
            None
        """
Q
Qiao Longfei 已提交
3228 3229 3230
        for block_idx in range(len(self.blocks), self.desc.num_blocks()):
            self.blocks.append(Block(self, block_idx))
        for block in self.blocks:
W
Wu Yi 已提交
3231
            block._sync_with_cpp()
Q
Qiao Longfei 已提交
3232

W
Wu Yi 已提交
3233
    def _copy_param_info_from(self, other):
3234
        """
3235
        Copy the information of parameters from other program.
D
dzhwinter 已提交
3236

Y
yuyang18 已提交
3237 3238 3239
        Notes: This is a very low level API. Users should not invoke it
        directly.

3240 3241 3242 3243 3244 3245 3246
        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
W
Wu Yi 已提交
3247
            raise TypeError("_copy_param_info_from should be invoked with "
3248 3249 3250
                            "Program")

        if len(self.blocks) != len(other.blocks):
W
Wu Yi 已提交
3251
            raise ValueError("_copy_param_info_from should be invoked with two "
3252
                             "program, with represent the same topology")
W
Wu Yi 已提交
3253
        self.global_block()._copy_param_info_from(other.global_block())
3254

3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269
    def _copy_dist_param_info_from(self, other):
        """
        Copy the information of distributed information from other program.

        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
            raise TypeError("_copy_dist_param_info_from should be invoked with "
                            "Program")
        self._is_distributed = other._is_distributed
        self._is_chief = other._is_chief
3270
        self._parameters_on_pservers = other._parameters_on_pservers
3271
        self._endpoints = other._endpoints
3272
        self._ps_endpoint = other._ps_endpoint
3273 3274
        self._distributed_lookup_table = other._distributed_lookup_table

W
Wu Yi 已提交
3275
    def _copy_data_info_from(self, other):
F
fengjiayi 已提交
3276 3277
        """
        Copy the information of data variables from other program.
D
dzhwinter 已提交
3278

Y
yuyang18 已提交
3279 3280 3281
        Notes: This is a very low level API. Users should not invoke it
        directly.

F
fengjiayi 已提交
3282 3283 3284 3285 3286 3287 3288
        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
W
Wu Yi 已提交
3289
            raise TypeError("_copy_param_info_from should be invoked with "
F
fengjiayi 已提交
3290 3291 3292
                            "Program")

        if len(self.blocks) != len(other.blocks):
W
Wu Yi 已提交
3293
            raise ValueError("_copy_param_info_from should be invoked with two "
F
fengjiayi 已提交
3294
                             "program, with represent the same topology")
3295
        for var in list(other.global_block().vars.values()):
F
fengjiayi 已提交
3296 3297 3298
            if var.is_data:
                self.global_block().var(var.name).is_data = True

3299
    def list_vars(self):
Y
yuyang18 已提交
3300 3301 3302 3303 3304 3305
        """
        Get all variables from this Program. A iterable object is returned.

        Returns:
            iterable: The generator will yield every variable in this program.
        """
3306
        for each_block in self.blocks:
3307
            for each_var in list(each_block.vars.values()):
3308 3309
                yield each_var

Y
Yu Yang 已提交
3310

Y
Yu Yang 已提交
3311
class Parameter(Variable):
3312
    """
3313
    Parameter is derived from Variable. A parameter is a persistable
3314
    Variable, and will be updated by optimizers after each iteration.
3315
    The training of a neural network is essentially the updating of
3316 3317
    its parameters.

3318
    Relative to a general Variable, a Parameter has several its own
3319 3320
    member variables:

3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332
    Args:
        trainable(bool): True if the parameter need to be updated after
            iterations.
        optimize_attr(map): Parameter attributes related with optimizing.
            Currently, it only contains 'learning_rate'.
            Default: {'learning_rate': 1.0}
        regularizer(WeightDecayRegularizer): The Regularizer which will
            be applied on the parameter. Default: None
        gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy
            which will be applied on the parameter. Default: None
        do_model_average(bool): True if the model average strategy will
            be applied on this parameter.
3333 3334
    """

Y
Yu Yang 已提交
3335 3336 3337 3338 3339 3340 3341 3342 3343 3344
    def __init__(self, block, shape, dtype, **kwargs):
        if shape is None or dtype is None:
            raise ValueError("Parameter must set shape and dtype")
        if len(shape) == 0:
            raise ValueError("Parameter shape cannot be empty")

        for each in shape:
            if each < 0:
                raise ValueError("Parameter shape should not be related with "
                                 "batch-size")
3345 3346 3347

        Variable.__init__(
            self, block, persistable=True, shape=shape, dtype=dtype, **kwargs)
Y
Yu Yang 已提交
3348 3349 3350 3351
        self.trainable = kwargs.get('trainable', True)

        self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})

3352 3353
        self.regularizer = kwargs.get('regularizer', None)

F
fengjiayi 已提交
3354
        self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None)
Y
Yu Yang 已提交
3355

W
wanghaoshuang 已提交
3356
        self.do_model_average = kwargs.get('do_model_average', None)
W
wanghaoshuang 已提交
3357

F
fengjiayi 已提交
3358 3359 3360
    def __str__(self):
        return self.to_string(True)

F
update  
fengjiayi 已提交
3361 3362 3363
    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
D
dzhwinter 已提交
3364

F
update  
fengjiayi 已提交
3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378
        Args:
            throw_on_error(bool): raise exception when self is not initialized
                when throw_on_error is True
            with_details(bool): more details about variables and parameters
                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True

        Returns(str): The debug string.

        """
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
        if with_details:
            res_str = Variable.to_string(self, throw_on_error, True)
            additional_attr = ("trainable", "optimize_attr", "regularizer",
W
wanghaoshuang 已提交
3379
                               "gradient_clip_attr", "do_model_average")
F
update  
fengjiayi 已提交
3380
            for attr_name in additional_attr:
3381 3382
                res_str += "%s: %s\n" % (
                    attr_name, six.binary_type(getattr(self, attr_name)))
F
update  
fengjiayi 已提交
3383 3384
        else:
            res_str = Variable.to_string(self, throw_on_error, False)
F
fengjiayi 已提交
3385 3386 3387 3388
        return res_str

    __repr__ = __str__

Y
Yu Yang 已提交
3389

Y
Yu Yang 已提交
3390
# program is a global instance.
Y
Yu Yang 已提交
3391 3392
_main_program_ = Program()
_startup_program_ = Program()
3393

3394

3395
def default_startup_program():
Y
Yu Yang 已提交
3396
    """
Y
yuyang18 已提交
3397 3398 3399 3400 3401 3402 3403 3404 3405
    Get default/global startup program.

    The layer function in :code:`fluid.layers` will create parameters, readers,
    NCCL handles as global variables. The :code:`startup_program` will
    initialize them by the operators in startup program. The layer function will
    append these initialization operators into startup program.

    This method will return the :code:`default` or the :code:`current` startup
    program. Users can use :code:`fluid.program_guard` to switch program.
3406

Y
Yu Yang 已提交
3407 3408 3409
    Returns:
        Program: startup program
    """
Y
Yu Yang 已提交
3410
    return _startup_program_
3411

3412

3413
def default_main_program():
Y
Yu Yang 已提交
3414
    """
Y
yuyang18 已提交
3415 3416 3417 3418 3419 3420 3421 3422 3423
    Get default/global main program. The main program is used for training or
    testing.

    All layer function in :code:`fluid.layers` will append operators and
    variables to the :code:`default_main_program`.

    The :code:`default_main_program` is the default program in a lot of APIs.
    For example, the :code:`Executor.run()` will execute the
    :code:`default_main_program` when the program is not specified.
3424

Y
Yu Yang 已提交
3425 3426 3427
    Returns:
        Program: main program
    """
Y
Yu Yang 已提交
3428
    return _main_program_
Y
Yu Yang 已提交
3429 3430 3431 3432 3433


def switch_main_program(program):
    """
    Switch the main program to a new program.
3434

Y
Yu Yang 已提交
3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448
    Args:
        program(Program): The new main program

    Returns:
        Program: The previous main program
    """
    global _main_program_
    prev_program = _main_program_
    _main_program_ = program
    return prev_program


def switch_startup_program(program):
    """
3449
    Switch the startup program to a new program
Y
Yu Yang 已提交
3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461
    Args:
        program(Program): The new startup program

    Returns:
        Program: The previous startup program
    """
    global _startup_program_
    prev_program = _startup_program_
    _startup_program_ = program
    return prev_program


S
rename  
sneaxiy 已提交
3462
@signature_safe_contextmanager
Y
Yu Yang 已提交
3463 3464
def program_guard(main_program, startup_program=None):
    """
Y
yuyang18 已提交
3465 3466 3467
    Change the global main program and startup program with `with` statement.
    Layer functions in the Python `with` block will append operators and
    variables to the new main programs.
3468

Y
Yu Yang 已提交
3469
    Examples:
Y
yuyang18 已提交
3470 3471 3472 3473 3474 3475 3476 3477 3478 3479

        >>> import paddle.fluid as fluid
        >>> main_program = fluid.Program()
        >>> startup_program = fluid.Program()
        >>> with fluid.program_guard(main_program, startup_program):
        >>>     data = fluid.layers.data(...)
        >>>     hidden = fluid.layers.fc(...)

    Notes: The temporary :code:`Program` can be used if the user does not need
    to construct either of startup program or main program.
3480

Y
Yu Yang 已提交
3481
    Examples:
Y
yuyang18 已提交
3482 3483 3484 3485 3486 3487

        >>> import paddle.fluid as fluid
        >>> main_program = fluid.Program()
        >>> # does not care about startup program. Just pass a temporary value.
        >>> with fluid.program_guard(main_program, fluid.Program()):
        >>>     data = ...
3488

Y
Yu Yang 已提交
3489
    Args:
Y
yuyang18 已提交
3490
        main_program(Program): New main program inside `with` statement.
3491
        startup_program(Program): New startup program inside `with` statement.
Y
Yu Yang 已提交
3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504
            None means do not change startup program.
    """
    if not isinstance(main_program, Program):
        raise TypeError("main_program should be Program")
    main_program = switch_main_program(main_program)
    if startup_program is not None:
        if not isinstance(startup_program, Program):
            raise TypeError("startup_program should be Program")
        startup_program = switch_startup_program(startup_program)
    yield
    switch_main_program(main_program)
    if startup_program is not None:
        switch_startup_program(startup_program)
X
xuwei06 已提交
3505 3506


W
Wu Yi 已提交
3507
def _get_var(name, program=None):
X
xuwei06 已提交
3508
    """
Y
yuyang18 已提交
3509
    Get a variable by name from the global block of a program.
F
fengjiayi 已提交
3510

X
xuwei06 已提交
3511 3512 3513
    Args:
        name(str): name of the variable
        program(Program|None): program object.
T
tangwei12 已提交
3514
        If None, default_global_program() will be used.
X
xuwei06 已提交
3515 3516 3517 3518 3519 3520 3521

    Returns:
        Variable
    """
    if program is None:
        program = default_main_program()
    assert isinstance(name, str)
3522
    assert isinstance(program, Program)
X
xuwei06 已提交
3523 3524

    return program.global_block().var(name)
3525 3526


S
rename  
sneaxiy 已提交
3527
@signature_safe_contextmanager
L
lujun 已提交
3528 3529 3530 3531
def _dygraph_guard(tracer):
    global _dygraph_tracer_
    tmp_trace = _dygraph_tracer_
    _dygraph_tracer_ = tracer
M
minqiyang 已提交
3532

3533
    yield
P
Paddle CI 已提交
3534

L
lujun 已提交
3535
    _dygraph_tracer_ = tmp_trace
P
Paddle CI 已提交
3536 3537


S
rename  
sneaxiy 已提交
3538
@signature_safe_contextmanager
L
lujun 已提交
3539 3540 3541 3542
def _dygraph_place_guard(place):
    global _dygraph_current_expected_place_
    tmp_place = _dygraph_current_expected_place_
    _dygraph_current_expected_place_ = place
M
minqiyang 已提交
3543

3544
    yield
M
minqiyang 已提交
3545

L
lujun 已提交
3546
    _dygraph_current_expected_place_ = tmp_place