math.py 62.1 KB
Newer Older
W
WuHaobo 已提交
1
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
2 3 4 5 6 7 8 9 10 11 12 13
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
14 15 16
"""
math functions
"""
17
from __future__ import print_function
18

19
from paddle.common_ops_import import *
20
from ..fluid import layers
L
Li Fuchen 已提交
21 22 23
from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable
from ..fluid.layer_helper import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
24
from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
25
import sys
26 27 28

# TODO: define math functions
# yapf: disable
29 30 31 32 33
from ..fluid.layers import abs    #DEFINE_ALIAS
from ..fluid.layers import acos    #DEFINE_ALIAS
from ..fluid.layers import asin    #DEFINE_ALIAS
from ..fluid.layers import ceil    #DEFINE_ALIAS
from ..fluid.layers import cos    #DEFINE_ALIAS
34 35
from ..fluid.layers import sinh    #DEFINE_ALIAS
from ..fluid.layers import cosh    #DEFINE_ALIAS
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
from ..fluid.layers import elementwise_add    #DEFINE_ALIAS
from ..fluid.layers import elementwise_div    #DEFINE_ALIAS
from ..fluid.layers import elementwise_floordiv    #DEFINE_ALIAS
from ..fluid.layers import elementwise_mod    #DEFINE_ALIAS
from ..fluid.layers import elementwise_mul    #DEFINE_ALIAS
from ..fluid.layers import elementwise_pow    #DEFINE_ALIAS
from ..fluid.layers import elementwise_sub    #DEFINE_ALIAS
from ..fluid.layers import exp    #DEFINE_ALIAS
from ..fluid.layers import floor    #DEFINE_ALIAS
from ..fluid.layers import log    #DEFINE_ALIAS
from ..fluid.layers import reciprocal    #DEFINE_ALIAS
from ..fluid.layers import reduce_max    #DEFINE_ALIAS
from ..fluid.layers import reduce_min    #DEFINE_ALIAS
from ..fluid.layers import reduce_prod    #DEFINE_ALIAS
from ..fluid.layers import reduce_sum    #DEFINE_ALIAS
from ..fluid.layers import round    #DEFINE_ALIAS
from ..fluid.layers import rsqrt    #DEFINE_ALIAS
from ..fluid.layers import scale    #DEFINE_ALIAS
from ..fluid.layers import sign    #DEFINE_ALIAS
from ..fluid.layers import square    #DEFINE_ALIAS
from ..fluid.layers import stanh    #DEFINE_ALIAS
from ..fluid.layers import atan    #DEFINE_ALIAS
from ..fluid.layers import erf    #DEFINE_ALIAS
59 60 61
from ..fluid.layers import sqrt    #DEFINE_ALIAS
from ..fluid.layers import sin    #DEFINE_ALIAS
from ..fluid.layers import tanh    #DEFINE_ALIAS
62

63 64 65 66
from ..fluid.layers import increment    #DEFINE_ALIAS
from ..fluid.layers import multiplex    #DEFINE_ALIAS
from ..fluid.layers import sums    #DEFINE_ALIAS

67
__all__ = [
68 69 70 71 72 73
        'abs',
        'acos',
        'asin',
        'atan',
        'ceil',
        'cos',
74
        'cosh',
75 76 77 78 79 80 81 82 83
        'cumsum',
        'elementwise_add',
        'elementwise_div',
        'elementwise_floordiv',
        'elementwise_mod',
        'elementwise_pow',
        'elementwise_sub',
        'exp',
        'floor',
84
        'increment',
85 86
        'log',
        'mul',
87
        'multiplex',
88 89 90 91 92 93 94 95 96 97 98
        'pow',
        'reciprocal',
        'reduce_max',
        'reduce_min',
        'reduce_prod',
        'reduce_sum',
        'round',
        'rsqrt',
        'scale',
        'sign',
        'sin',
99
        'sinh',
100 101 102 103
        'sqrt',
        'square',
        'stanh',
        'sum',
104
        'sums',
105 106 107
        'tanh',
        'elementwise_sum',
        'max',
108
        'maximum',
109
        'min',
110
        'minimum',
111 112
        'mm',
        'div',
113
        'multiply',
114 115 116
        'add',
        'atan',
        'logsumexp',
117
        'inverse',
118 119 120 121 122
        'log1p',
        'erf',
        'addcmul',
        'addmm',
        'clamp',
L
Li Fuchen 已提交
123
        'trace',
124
        'kron'
125 126 127
]
# yapf: enable.

128
@templatedoc()
W
WuHaobo 已提交
129
def pow(input, exponent, name=None):
130
    """
131 132
	:alias_main: paddle.pow
	:alias: paddle.pow,paddle.tensor.pow,paddle.tensor.math.pow
S
swtkiwi 已提交
133

134 135 136 137 138 139 140
    This is Pow Activation Operator.

    :math:`out = input^{exponent}`

    Args:
        input(Variable): A ``Tensor`` or ``LoDTensor`` . The data type is ``float32`` or ``float64``.
        exponent(float32|Variable): A scalar with type ``float32`` or a ``Tensor`` with shape [1] and type ``float32``.
141
        name(str, optional): The default value is None. Normally there is no need for user to set this property.
142 143 144 145 146 147 148 149 150 151
            For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        Variable: A ``Tensor`` or ``LoDTensor``. The data type is same as ``input``.

    Examples:

        .. code-block:: python

            import paddle
152
            import paddle.fluid as fluid
153

154
            x = fluid.data(name="x", shape=[32,32], dtype="float32")
155 156

            # example 1: argument exponent is float
W
WuHaobo 已提交
157
            y_1 = paddle.pow(x, 2.0)
158 159 160
            # y_1 is x^{2.0}

            # example 2: argument exponent is Variable
161
            exponent_tensor = fluid.layers.fill_constant([1], "float32", 3.0)
W
WuHaobo 已提交
162
            y_2 = paddle.pow(x, exponent_tensor)
163 164
            # y_2 is x^{3.0}
    """
W
WuHaobo 已提交
165 166 167
    if in_dygraph_mode():
        return core.ops.pow(input, "exponent", exponent)

168 169 170 171 172 173 174 175 176
    helper = LayerHelper('pow', **locals())
    inputs = {'X': input}
    attrs = {}
    if isinstance(exponent, Variable):
        exponent.stop_gradient = True
        inputs['FactorTensor'] = exponent
    else:
        attrs['factor'] = exponent

W
WuHaobo 已提交
177 178 179 180 181
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    check_dtype(
        out.dtype, out.name,
        convert_dtype(input.dtype), 'pow',
        '(The out data type in pow must be the same with input data type.)')
182 183 184 185 186 187

    helper.append_op(
        type='pow', inputs=inputs, outputs={'Out': out}, attrs=attrs)
    return out


188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219
@dygraph_only
def _elementwise_op_in_dygraph(x,
                               y,
                               axis=-1,
                               act=None,
                               use_mkldnn=False,
                               op_name=None):
    op = getattr(core.ops, op_name)
    out = op(x, y, 'axis', axis, 'use_mkldnn', use_mkldnn)

    return dygraph_utils._append_activation_in_dygraph(
        out, act, use_mkldnn=use_mkldnn)


def _elementwise_op(helper):
    op_type = helper.layer_type
    original_op_type = helper.kwargs.get('original_op_type', op_type)
    x = helper.kwargs.get('x', None)
    y = helper.kwargs.get('y', None)

    assert x is not None, 'x cannot be None in {}'.format(original_op_type)
    assert y is not None, 'y cannot be None in {}'.format(original_op_type)
    check_variable_and_dtype(
        x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'],
        original_op_type)
    check_variable_and_dtype(
        y, 'y', ['float16', 'float32', 'float64', 'int32', 'int64'],
        original_op_type)

    axis = helper.kwargs.get('axis', -1)
    use_mkldnn = helper.kwargs.get('use_mkldnn', False)
    name = helper.kwargs.get('name', None)
W
WuHaobo 已提交
220 221 222 223 224
    if name is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)
225 226 227 228 229 230 231 232 233 234 235

    helper.append_op(
        type=op_type,
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out},
        attrs={'axis': axis,
               'use_mkldnn': use_mkldnn})
    return helper.append_activation(out)


W
WuHaobo 已提交
236
def add(x, y, alpha=1, name=None):
237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327
    """
Examples:

    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.array([2, 3, 4]).astype('float32'),
                "y": np.array([1, 5, 2]).astype('float32')
            }

        x = fluid.data(name="x", shape=[3], dtype='float32')
        y = fluid.data(name="y", shape=[3], dtype='float32')
        z1 = paddle.add(x, y)
        z2 = paddle.add(x, y, alpha=10)
        # z = x + y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z1.name, z2.name])

        print(z_value[0]) # [3., 8., 6.]
        print(z_value[1]) # [12. 53. 24.]


    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.ones((2, 3, 4, 5)).astype('float32'),
                "y": np.zeros((4, 5)).astype('float32')
            }

        x = fluid.data(name="x", shape=[2, 3, 4, 5], dtype='float32')
        y = fluid.data(name="y", shape=[4, 5], dtype='float32')
        z = paddle.add(x, y, name='z')
        # z = x + y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])

        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
                "y": np.random.randint(1, 5, size=[5]).astype('float32')
            }

        x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
        y = fluid.data(name="y", shape=[5], dtype='float32')
        z = paddle.add(x, y)
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])
        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        x = fluid.data(name="x", shape=[3], dtype="float32")
        y = fluid.data(name='y', shape=[3], dtype='float32')
W
WuHaobo 已提交
328
        z = paddle.add(x, y)
329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        data1 = np.array([2, 3, 4], dtype='float32')
        data2 = np.array([1, 5, 2], dtype='float32')
        z_value = exe.run(feed={'x': data1,
                                'y': data2},
                                fetch_list=[z])
        print(z_value[0]) # [3. 8. 6.]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        with fluid.dygraph.guard():
            np_x = np.array([2, 3, 4]).astype('float64')
            np_y = np.array([1, 5, 2]).astype('float64')
            x = fluid.dygraph.to_variable(np_x)
            y = fluid.dygraph.to_variable(np_y)
            z = paddle.add(x, y, alpha=-0.5)
            np_z = z.numpy()
            print(np_z)  # [1.5, 0.5, 3. ]

    """
    op_type = 'elementwise_add'
    axis = -1
    act = None
    if alpha != 1:
        y = scale(y, scale=alpha)
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)

    return _elementwise_op(LayerHelper(op_type, **locals()))


W
WuHaobo 已提交
368
def div(x, y, name=None):
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437
    """
Examples:

    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.array([2, 3, 4]).astype('float32'),
                "y": np.array([1, 5, 2]).astype('float32')
            }

        x = fluid.data(name="x", shape=[3], dtype='float32')
        y = fluid.data(name="y", shape=[3], dtype='float32')
        z = paddle.div(x, y)
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])

        print(z_value) # [2., 0.6, 2.]


    .. code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.ones((2, 3, 4, 5)).astype('float32'),
                "y": np.zeros((4, 5)).astype('float32')
            }

        x = fluid.data(name="x", shape=[2, 3, 4, 5], dtype='float32')
        y = fluid.data(name="y", shape=[4, 5], dtype='float32')
        z = paddle.div(x, y, name='z')
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])

        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        def gen_data():
            return {
                "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
                "y": np.random.randint(1, 5, size=[5]).astype('float32')
            }

        x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
        y = fluid.data(name="y", shape=[5], dtype='float32')
W
WuHaobo 已提交
438
        z = paddle.div(x, y)
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
        # z = x / y

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        z_value = exe.run(feed=gen_data(),
                            fetch_list=[z.name])
        print(z_value[0])
        print(z_value[0].shape) # z.shape=[2,3,4,5]


    ..  code-block:: python

        import paddle
        import paddle.fluid as fluid
        import numpy as np

        with fluid.dygraph.guard(fluid.CPUPlace()):
            np_x = np.array([2, 3, 4]).astype('float64')
            np_y = np.array([1, 5, 2]).astype('float64')
            x = fluid.dygraph.to_variable(np_x)
            y = fluid.dygraph.to_variable(np_y)
            z = paddle.div(x, y)
            np_z = z.numpy()
            print(np_z)  # [2., 0.6, 2.]

    """
    op_type = 'elementwise_div'
    axis = -1
    act = None
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)

    return _elementwise_op(LayerHelper(op_type, **locals()))


476 477 478 479 480 481 482 483 484 485 486 487
def multiply(x, y, axis=-1, name=None):
    """
	:alias_main: paddle.multiply
	:alias: paddle.multiply,paddle.tensor.multiply,paddle.tensor.math.multiply

Examples:

    .. code-block:: python

        import paddle
        import numpy as np

488
        paddle.disable_static()
489 490
        x_data = np.array([[1, 2], [3, 4]], dtype=np.float32)
        y_data = np.array([[5, 6], [7, 8]], dtype=np.float32)
491 492
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
493 494 495 496 497
        res = paddle.multiply(x, y)
        print(res.numpy()) # [[5, 12], [21, 32]]

        x_data = np.array([[[1, 2, 3], [1, 2, 3]]], dtype=np.float32)
        y_data = np.array([1, 2], dtype=np.float32)
498 499
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
500 501 502 503 504 505 506 507 508 509 510 511
        res = paddle.multiply(x, y, axis=1)
        print(res.numpy()) # [[[1, 2, 3], [2, 4, 6]]]

    """
    op_type = 'elementwise_mul'
    act = None
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)

    return _elementwise_op(LayerHelper(op_type, **locals()))

512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613
def maximum(x, y, axis=-1, name=None):
    """
Examples:

    .. code-block:: python

        import paddle
        import numpy as np

        paddle.disable_static()
  
        x_data = np.array([[1, 2], [3, 4]], dtype=np.float32)
        y_data = np.array([[5, 6], [7, 8]], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.maximum(x, y)
        print(res.numpy())
        #[[5. 6.]
        # [7. 8.]]

        x_data = np.array([[[1, 2, 3], [1, 2, 3]]], dtype=np.float32)
        y_data = np.array([1, 2], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.maximum(x, y, axis=1)
        print(res.numpy())
        #[[[1. 2. 3.]
        #  [2. 2. 3.]]]

        x_data = np.array([2, 3, 5], dtype=np.float32)
        y_data = np.array([1, 4, np.nan], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.maximum(x, y)
        print(res.numpy())
        #[ 2.  4. nan]

        x_data = np.array([5, 3, np.inf], dtype=np.float32)
        y_data = np.array([1, 4, 5], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.maximum(x, y)
        print(res.numpy())
        #[ 5.  4. inf]
    """
    op_type = 'elementwise_max'
    act = None
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)
    return _elementwise_op(LayerHelper(op_type, **locals()))

def minimum(x, y, axis=-1, name=None):
    """
Examples:

    .. code-block:: python

        import paddle
        import numpy as np
        paddle.disable_static()
  
        x_data = np.array([[1, 2], [3, 4]], dtype=np.float32)
        y_data = np.array([[5, 6], [7, 8]], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.minimum(x, y)
        print(res.numpy())
        #[[1. 2.]
        # [3. 4.]]

        x_data = np.array([[[1, 2, 3], [1, 2, 3]]], dtype=np.float32)
        y_data = np.array([1, 2], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.minimum(x, y, axis=1)
        print(res.numpy())
        #[[[1. 1. 1.]
        #  [2. 2. 2.]]]

        x_data = np.array([2, 3, 5], dtype=np.float32)
        y_data = np.array([1, 4, np.nan], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.minimum(x, y)
        print(res.numpy())
        #[ 1.  3. nan]

        x_data = np.array([5, 3, np.inf], dtype=np.float32)
        y_data = np.array([1, 4, 5], dtype=np.float32)
        x = paddle.to_variable(x_data)
        y = paddle.to_variable(y_data)
        res = paddle.minimum(x, y)
        print(res.numpy())
        #[1. 3. 5.]
    """
    op_type = 'elementwise_min'
    act = None
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)
    return _elementwise_op(LayerHelper(op_type, **locals()))
614

615 616 617
for func in [
        add,
        div,
618 619 620
        maximum,
        minimum,
        multiply
621
]:
622
    proto_dict = {'add': 'elementwise_add', 'div': 'elementwise_div', 'maximum': 'elementwise_max', 'minimum': 'elementwise_min', 'multiply': 'elementwise_mul'}
623 624
    op_proto = OpProtoHolder.instance().get_op_proto(proto_dict[func.__name__])
    if func.__name__ in ['add']:
S
Shibo Tao 已提交
625 626 627
        alias_main = ':alias_main: paddle.%(func)s' % {'func': func.__name__}
        alias = ':alias: paddle.%(func)s, paddle.tensor.%(func)s, paddle.tensor.math.%(func)s' % {'func': func.__name__}

628 629 630 631 632 633 634 635 636 637 638 639 640
        additional_args_lines = [
            "alpha (int|float, optional): The alpha factor of the input. Default is 1. If alpha is not 1, the equation becomes Out = X + alpha * Y.",
            "name (string, optional): Name of the output. \
            Default is None. It's used to print debug info for developers. Details: \
            :ref:`api_guide_Name` "
        ]
    else:
        additional_args_lines = [
            "name (string, optional): Name of the output. \
            Default is None. It's used to print debug info for developers. Details: \
            :ref:`api_guide_Name` "
        ]

S
Shibo Tao 已提交
641
    func.__doc__ = alias_main + """\n""" + alias + """\n""" + _generate_doc_string_(
642 643
        op_proto,
        additional_args_lines=additional_args_lines,
644
        skip_attrs_set={"x_data_format", "y_data_format", "axis",
645
            "use_quantizer", "mkldnn_data_type", "Scale_x", "Scale_y", "Scale_out"
646
        }) + """\n""" + str(func.__doc__)
647

648 649
def sum(input, dim=None, dtype=None, keep_dim=False, name=None):
    """
650 651
	:alias_main: paddle.sum
	:alias: paddle.sum,paddle.tensor.sum,paddle.tensor.math.sum
S
swtkiwi 已提交
652

653 654 655 656 657 658 659 660 661 662
    Computes the sum of tensor elements over the given dimension.

    Args:
        input (Variable): The input variable which is a Tensor, the data type is float32,
            float64, int32, int64.
        dim (list|int, optional): The dimensions along which the sum is performed. If
            :attr:`None`, sum all elements of :attr:`input` and return a
            Tensor variable with a single element, otherwise must be in the
            range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
            the dimension to reduce is :math:`rank + dim[i]`.
663
        dtype(str, optional): The dtype of output tensor. The default value is None, the dtype
664 665 666 667 668 669 670 671 672 673 674 675 676 677
            of output is the same as input tensor.
        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
            than the :attr:`input` unless :attr:`keep_dim` is true, default
            value is False.
        name(str, optional): The default value is None.  Normally there is no need for
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`

    Returns:
        Variable: Tensor, results of summation operation on the specified dim of input tensor,
        it's data type is the same as input's Tensor.

    Raises:
        ValueError, the :attr:`dtype` must be float64 or int64.
678

679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749
    Examples:
        .. code-block:: python

            import paddle
            import paddle.fluid as fluid
            # x is a Tensor variable with following elements:
            #    [[0.2, 0.3, 0.5, 0.9]
            #     [0.1, 0.2, 0.6, 0.7]]
            # Each example is followed by the corresponding output tensor.
            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
            out1 = paddle.sum(x)  # [3.5]
            out2 = paddle.sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
            out3 = paddle.sum(x, dim=-1)  # [1.9, 1.6]
            out4 = paddle.sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]

            # y is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1, 2], [3, 4]],
            #      [[5, 6], [7, 8]]]
            # Each example is followed by the corresponding output tensor.
            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
            out5 = paddle.sum(y, dim=[1, 2]) # [10, 26]
            out6 = paddle.sum(y, dim=[0, 1]) # [16, 20]

    """
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
    attrs = {
        'dim': dim if dim != None and dim != [] else [0],
        'keep_dim': keep_dim,
        'reduce_all': True if dim == None or dim == [] else False,
    }
    dtype_flag = False
    if dtype is not None:
        if dtype in ['float64', 'int64']:
            if (convert_dtype(input.dtype) == "float32" and dtype == "float64") or \
               (convert_dtype(input.dtype) == "int32" and dtype == "int64"):
                attrs.update({
                    'in_dtype': input.dtype,
                    'out_dtype': convert_np_dtype_to_dtype_(dtype)
                })
                dtype_flag = True
        else:
            raise ValueError(
                "The value of 'dtype' in sum op must be float64, int64, but received of {}".
                format(dtype))

    if in_dygraph_mode():
        reduce_all = True if dim == None or dim == [] else False
        dim = dim if dim != None and dim != [] else [0]
        if dtype_flag:
            return core.ops.reduce_sum(input, 'dim', dim, 'keep_dim', keep_dim,
                                       'reduce_all', reduce_all, 'in_dtype',
                                       input.dtype, 'out_dtype',
                                       convert_np_dtype_to_dtype_(dtype))
        else:
            return core.ops.reduce_sum(input, 'dim', dim, 'keep_dim', keep_dim,
                                       'reduce_all', reduce_all)
    check_variable_and_dtype(
        input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_sum')
    helper = LayerHelper('sum', **locals())
    if dtype_flag:
        out = helper.create_variable_for_type_inference(
            dtype=convert_np_dtype_to_dtype_(dtype))
    else:
        out = helper.create_variable_for_type_inference(dtype=input.dtype)
    helper.append_op(
        type='reduce_sum',
        inputs={'X': input},
        outputs={'Out': out},
        attrs=attrs)
    return out
750

751

752 753 754
@templatedoc(op_type="sum")
def elementwise_sum(inputs, name=None):
    """
755 756
	:alias_main: paddle.elementwise_sum
	:alias: paddle.elementwise_sum,paddle.tensor.elementwise_sum,paddle.tensor.math.elementwise_sum
S
swtkiwi 已提交
757

758
    ${comment}
759

760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790
    Case 1:
    ::
        Input:
            Input. Shape = [2, 3]
            Input = [[1, 2, 3],
                     [4, 5, 6]]

        Output:
            The output. Shape = [2, 3]
            Output = [[1, 2, 3],
                      [4, 5, 6]]

    Case 2:
    ::
        Input:
            First input:
            Input1. Shape = [2, 3]
            Input1 = [[1, 2, 3],
                      [4, 5, 6]]

        The second input:
            Input2. Shape = [2, 3]
            Input2 = [[7, 8, 9],
                      [10, 11, 12]]

        Output:
            The output. Shape = [2, 3]
            Output = [[8, 10, 12],
                      [14, 16, 18]]

    Args:
791 792
        inputs (Variable|list(Variable)):  A Varaible list. The shape and data type of the list elementsshould be consistent.
            Variable can be multi-dimensional Tensoror LoDTensor, and data types can be: float32, float64, int32, int64.
793 794 795 796
        name(str, optional): The default value is None. Normally there is no need for
            user to set this property. For more information, please refer to :ref:`api_guide_Name`

    Returns:
797
        Variable: the sum of input :math:`inputs` . its shape and data types are consistent with :math:`inputs` .
798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822

    Examples:
        .. code-block:: python

            import paddle
            import paddle.fluid as fluid

            input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5)
            input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3)
            sum = paddle.elementwise_sum([input0, input1])

            # You can print out 'sum' via executor.
            out = fluid.layers.Print(sum, message="the sum of input0 and input1: ")
            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(fluid.default_main_program())

            # The printed result is:
            # 1570701754	the sum of input0 and input1: 	The place is:CPUPlace
            # Tensor[elementwise_sum_0.tmp_0]
            #    shape: [2,3,]
            #    dtype: l
            #    data: 8,8,8,8,8,8,

            # the sum of input0 and input1 is 2-D Tensor with shape [2,3].
            # dtype is the corresponding C++ data type, which may vary in different environments.
823 824
            # Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
            #       so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
825 826 827 828
            #       and '__int64' on Windows. They both represent 64-bit integer variables.
    """

    helper = LayerHelper('elementwise_sum', **locals())
829 830 831 832 833 834 835 836 837 838 839
    check_type(inputs, 'inputs', (Variable, tuple, list), 'elementwise_sum')
    if isinstance(inputs, list) or isinstance(inputs, tuple):
        if len(inputs) > 0:
            for input in inputs:
                check_variable_and_dtype(input, "inputs", \
                   ['float32', 'float64', 'int32', 'int64'], 'elementwise_sum')
    else:
        check_variable_and_dtype(inputs, "inputs", \
                ['float32', 'float64', 'int32', 'int64'], 'elementwise_sum')


840 841 842 843 844 845 846 847 848 849 850
    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('inputs'))
    helper.append_op(
        type='sum',
        inputs={'X': inputs},
        outputs={'Out': out},
        attrs={'use_mkldnn': False})

    return out


W
WuHaobo 已提交
851
def mm(input, mat2, name=None):
852
    """
853 854
	:alias_main: paddle.mm
	:alias: paddle.mm,paddle.tensor.mm,paddle.tensor.math.mm
S
swtkiwi 已提交
855

856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903
    Applies matrix multiplication to two tensors.

    Currently, the input tensors' rank can be any, but when the rank of any
    inputs is bigger than 3, this two inputs' rank should be equal.


    Also note that if the raw tensor :math:`x` or :math:`mat2` is rank-1 and
    nontransposed, the prepended or appended dimension :math:`1` will be
    removed after matrix multiplication.

    Args:
        x (Variable): The input variable which is a Tensor or LoDTensor.
        mat2 (Variable): The input variable which is a Tensor or LoDTensor.
        name(str, optional): The default value is None. Normally there is no need for
            user to set this property. For more information, please refer to :ref:`api_guide_Name`

    Returns:
        Variable: The product Tensor (or LoDTensor) variable.

    Examples:
        .. code-block:: python

            # Examples to clarify shapes of the inputs and output
            # x: [B, ..., M, K], mat2: [B, ..., K, N]
            # fluid.layers.matmul(x, mat2)  # out: [B, ..., M, N]

            # x: [B, M, K], mat2: [B, K, N]
            # fluid.layers.matmul(x, mat2)  # out: [B, M, N]

            # x: [B, M, K], mat2: [K, N]
            # fluid.layers.matmul(x, mat2)  # out: [B, M, N]

            # x: [M, K], mat2: [K, N]
            # fluid.layers.matmul(x, mat2)  # out: [M, N]

            # x: [B, M, K], mat2: [K]
            # fluid.layers.matmul(x, mat2)  # out: [B, M]

            # x: [K], mat2: [K]
            # fluid.layers.matmul(x, mat2)  # out: [1]

            import paddle
            import paddle.fluid as fluid
            x = fluid.data(name='x', shape=[2, 3], dtype='float32')
            mat2 = fluid.data(name='mat2', shape=[3, 2], dtype='float32')
            out = paddle.mm(x, mat2) # out shape is [2, 2]
    """
    if in_dygraph_mode():
W
WuHaobo 已提交
904
        out = _varbase_creator(dtype=input.dtype)
905 906
        core.ops.matmul(input, mat2, out)
        return out
907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943

    def __check_input(x, y):
        var_names = {'x': x, 'y': y}
        for name, val in var_names.items():
            check_variable_and_dtype(val, name,
                                     ['float16', 'float32', 'float64'], 'mm')
        x_shape = list(x.shape)
        y_shape = list(y.shape)
        if len(x_shape) == 1:
            x_shape = [1] + x_shape
        if len(y_shape) == 1:
            y_shape = y_shape + [1]

        # check the inner 2 dimensions
        if x_shape[-1] != y_shape[-2]:
            if not ((x_shape[-1] == -1) or (y_shape[-2] == -1)):
                raise ValueError(
                    "After performing an optional transpose, Input X's width should be "
                    "equal to Y's width for multiplication "
                    "prerequisites. But received X's shape: %s, Y's shape: %s\n"
                    % (x_shape, y_shape))

        if len(y_shape) > 2 and len(x_shape) > 2:
            for i, dim_x in enumerate(x_shape[:-2]):
                # don't check neg shape
                if dim_x < 0 or y_shape[i] < 0:
                    continue
                if dim_x != y_shape[i]:
                    raise ValueError(
                        "When the matrix is larger than 2 dimensions, the higher "
                        "dimensional values of the two matrices need to be equal. "
                        "But received x_shape[%d] != y_shape[%d]. X's shape: %s, "
                        "Y's shape: %s.\n" % (i, i, x_shape, y_shape))

    __check_input(input, mat2)

    helper = LayerHelper('mm', **locals())
W
WuHaobo 已提交
944
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
945 946 947 948
    helper.append_op(
        type='matmul', inputs={'X': input,
                               'Y': mat2}, outputs={'Out': out})
    return out
949

950

Y
yaoxuefeng 已提交
951
def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
952
    """
953 954
	:alias_main: paddle.addmm
	:alias: paddle.addmm,paddle.tensor.addmm,paddle.tensor.math.addmm
S
swtkiwi 已提交
955

956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971
    **addmm**

    This operator is used to perform matrix multiplication for input $x$ and $y$.
    $input$ is added to the final result.
    The equation is:

    ..  math::
        Out = alpha * x * y + beta * input

    $Input$, $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $input$.

    Args:
        input (Variable): The input Tensor/LoDTensor to be added to the final result.
        x (Variable): The first input Tensor/LoDTensor for matrix multiplication.
        y (Variable): The second input Tensor/LoDTensor for matrix multiplication.
        beta (float): Coefficient of $input$.
Y
yaoxuefeng 已提交
972
        alpha (float): Coefficient of $x*y$.
973 974 975 976 977 978 979 980 981 982 983 984 985 986 987
        name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None.

    Returns:
        Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of addmm op.

    Examples:
        ..  code-block:: python

            import numpy as np
            import paddle

            data_x = np.ones((2, 2)).astype(np.float32)
            data_y = np.ones((2, 2)).astype(np.float32)
            data_input = np.ones((2, 2)).astype(np.float32)

988
            paddle.disable_static()
Y
yaoxuefeng 已提交
989

990 991 992
            x = paddle.to_variable(data_x)
            y = paddle.to_variable(data_y)
            input = paddle.to_variable(data_input)
Y
yaoxuefeng 已提交
993 994 995 996

            out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )

            print( out.numpy() )
997 998 999
            # [[10.5 10.5]
            # [10.5 10.5]]
    """
Y
yaoxuefeng 已提交
1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019
    input_shape = input.shape
    x_shape = x.shape
    y_shape = y.shape
    if not len(input_shape) == len(x_shape) == len(y_shape) == 2:
        raise ValueError("The dimention of input, x, y should be 2 but receive input's shape: {}, x's shape: {}, y's shape: {}".format(input_shape, x_shape, y_shape))
    if input_shape[0] != x_shape[0]:
        if input_shape[0] != 1:
            raise ValueError( "When x's dimension[0] is not equal with input's dimension[0], input's dimension[0] must be 1 but got {}".format(input_shape[0]))
        if input_shape[1] != y_shape[1] and input_shape[1] != 1:
            raise ValueError( "When y's dimension[1] is not equal with input's dimension[1], input's dimension[1] must be 1 but got {}".format(input_shape[1]))
    if input_shape[1] != y_shape[1]:
        if input_shape[1] != 1:
            raise ValueError( "When y's dimension[1] is not equal with input's dimension[1], input's dimension[1] must be 1 but got {}".format(input_shape[1]))
        if input_shape[0] != x_shape[0] and input_shape[0] != 1:
            raise ValueError( "When x's dimension[0] is not equal with input's dimension[0], input's dimension[0] must be 1 but got {}".format(input_shape[0]))
    if x_shape[1] != y_shape[0]:
        raise ValueError("The input Variable x's width must be equal with Variable y' height. But received x's shape = {}, y's shape = {}.".format(x_shape, y_shape))



1020 1021 1022 1023
    if in_dygraph_mode():
        out = core.ops.addmm(input, x, y, "Alpha", alpha, "Beta", beta)
        return out

1024 1025 1026 1027
    inputs = {'Input': input, "X": x, "Y": y}
    attrs = {'Alpha': alpha, 'Beta': beta}

    helper = LayerHelper("addmm", **locals())
Y
yaoxuefeng 已提交
1028
    check_variable_and_dtype(input, 'Input', ['float32', 'float64'], 'addmm')
1029 1030 1031 1032 1033 1034 1035
    check_variable_and_dtype(x, 'X', ['float32', 'float64'], 'addmm')
    check_variable_and_dtype(y, 'Y', ['float32', 'float64'], 'addmm')
    out = helper.create_variable_for_type_inference(dtype=x.dtype)

    helper.append_op(
        type="addmm", inputs=inputs, attrs=attrs, outputs={"Out": out})
    return out
1036 1037


W
WuHaobo 已提交
1038
def logsumexp(x, dim=None, keepdim=False, name=None):
1039
    """
1040 1041
	:alias_main: paddle.logsumexp
	:alias: paddle.logsumexp,paddle.tensor.logsumexp,paddle.tensor.math.logsumexp
S
swtkiwi 已提交
1042

1043
    This operator calculates the log of the sum of exponentials of the input Tensor.
1044

1045 1046
    .. math::
       logsumexp(x) = \log\sum exp(x)
1047 1048


1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059
    Parameters:
       x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64.
       dim (list|int, optional): The dimensions along which the sum is performed. If :attr:`None`,
         sum all elements of :attr:`input` and return a Tensor variable with a single element,
         otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
         the dimension to reduce is :math:`rank + dim[i]`.
       keep_dim (bool, optional): Whether to reserve the reduced dimension in the output Tensor.
         The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim`
         is true, default value is False.
       name (str, optional): The default value is None.  Normally there is no need for user to
         set this property.  For more information, please refer to :ref:`api_guide_Name`
1060

1061 1062
    Returns:
       Variable: The calcuated result Tensor/LoDTensor.
1063

1064
    Examples:
1065

1066
    .. code-block:: python
1067

1068 1069 1070 1071 1072 1073 1074 1075 1076 1077
        import paddle
        import paddle.fluid as fluid
        import numpy as np

        with fluid.dygraph.guard():
          np_x = np.random.uniform(0.1, 1, [10]).astype(np.float32)
          x = fluid.dygraph.to_variable(np_x)
          print(paddle.logsumexp(x).numpy())

    ..  code-block:: python
1078

1079 1080 1081 1082 1083 1084 1085 1086 1087
        import paddle
        import paddle.fluid as fluid
        import numpy as np

        with fluid.dygraph.guard():
            np_x = np.random.uniform(0.1, 1, [2, 3, 4]).astype(np.float32)
            x = fluid.dygraph.to_variable(np_x)
            print(paddle.logsumexp(x, dim=1).numpy())
            print(paddle.logsumexp(x, dim=[0, 2]).numpy())
1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099

    """
    op_type = 'logsumexp'
    assert x is not None, 'x cannot be None in {}'.format(op_type)

    # reduce_sum does not support float16
    check_variable_and_dtype(x, 'x', ['float32', 'float64'], op_type)

    exp_out = layers.exp(x)
    sum_out = layers.reduce_sum(exp_out, dim, keepdim)

    return layers.log(sum_out, name)
1100 1101


W
WuHaobo 已提交
1102
def inverse(input, name=None):
1103
    """
1104 1105
	:alias_main: paddle.inverse
	:alias: paddle.inverse,paddle.tensor.inverse,paddle.tensor.math.inverse
S
swtkiwi 已提交
1106

1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135
    Takes the inverse of the square matrix. A square matrix is a matrix with
    the same number of rows and columns. The input can be a square matrix
    (2-D Tensor) or batches of square matrices.

    Args:
        input (Variable): The input Variable which holds a Tensor. The last two
            dimensions should be equal. When the number of dimensions is
            greater than 2, it is treated as batches of square matrix. The data
            type can be float32 and float64.
        name (str, optional): The default value is None. Normally there is no need for
            user to set this property. For more information,
            please refer to :ref:`api_guide_Name`

    Returns:
        Variable: A Tensor holds the inverse of input. The shape and data type
            is the same as input.

    Examples:
        .. code-block:: python

            import numpy as np
            import paddle
            import paddle.fluid as fluid

            mat_np = np.array([[2, 0], [0, 2]]).astype("float32")

            # example for static graph
            input = fluid.data("input", shape=[2, 2], dtype="float32")
            out = paddle.inverse(input)
1136

1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163
            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            results = exe.run(feed={"input": mat_np },
                              fetch_list=[out.name])
            print(results[0]) # [[0.5, 0], [0, 0.5]]

            # example for dynamic graph
            with fluid.dygraph.guard():
                mat = fluid.dygraph.to_variable(mat_np)
                inv = paddle.inverse(mat)
                print(inv) # [[0.5, 0], [0, 0.5]]
    """
    if in_dygraph_mode():
        return core.ops.inverse(input)

    def _check_input(input):
        check_variable_and_dtype(input, 'input',
                                 ['float32', 'float64'], 'inverse')
        if len(input.shape) < 2:
            raise ValueError(
                "The input of inverse is expected to be a Tensor whose number "
                "of dimensions is no less than 2. But reviced: %d, "
                "input's shape: %s." % (len(input.shape), input.shape))

    _check_input(input)

    helper = LayerHelper('inverse', **locals())
W
WuHaobo 已提交
1164
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
1165 1166 1167 1168 1169
    helper.append_op(
        type='inverse', inputs={'Input': [input] }, outputs={'Output': [out]})
    return out


1170
def max(x, axis=None, keepdim=False, name=None):
1171
    """
S
swtkiwi 已提交
1172

1173
    Computes the maximum of tensor elements over the given axis.
1174 1175

    Args:
1176
        x(Tensor): A tensor, the data type is float32,
1177
            float64, int32, int64.
1178
        axis(list|int, optional): The axis along which the maximum is computed.
1179
            If :attr:`None`, compute the maximum over all elements of
1180
             `x` and return a Tensor variable with a single element,
1181 1182 1183
            otherwise must be in the range :math:`[-x.ndim(x), x.ndim(x))`.
            If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`.
        keepdim(bool, optional): Whether to reserve the reduced dimension in the
1184
            output Tensor. The result tensor will have one fewer dimension
1185
            than the `x` unless :attr:`keepdim` is true, default
1186
            value is False.
1187
        name(str, optional): The default value is None.  Normally there is no need for
1188 1189 1190
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`

    Returns:
1191
        Tensor, results of maximum on the specified axis of input tensor,
1192
        it's data type is the same as `x`.
1193 1194 1195

    Examples:
        .. code-block:: python
1196 1197

            import numpy as np
1198
            import paddle
1199

1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231
            paddle.disable_static()

            # data_x is a variable with shape [2, 4]
            # the axis is a int element
            data_x = np.array([[0.2, 0.3, 0.5, 0.9],
                               [0.1, 0.2, 0.6, 0.7]])
            x = paddle.to_variable(data_x)
            result1 = paddle.max(x)
            print(result1.numpy())
            #[0.9]
            result2 = paddle.max(x, axis=0)
            print(result2.numpy()) 
            #[0.2 0.3 0.6 0.9]
            result3 = paddle.max(x, axis=-1)
            print(result3.numpy())
            #[0.9 0.7]
            result4 = paddle.max(x, axis=1, keepdim=True)
            print(result4.numpy())
            #[[0.9]
            # [0.7]]

            # data_y is a variable with shape [2, 2, 2]
            # the axis is list 
            data_y = np.array([[[1.0, 2.0], [3.0, 4.0]],
                               [[5.0, 6.0], [7.0, 8.0]]])
            y = paddle.to_variable(data_y)
            result5 = paddle.max(y, axis=[1, 2])
            print(result5.numpy())
            #[4. 8.]
            result6 = paddle.max(y, axis=[0, 1])
            print(result6.numpy())
            #[7. 8.]
1232 1233
    """

1234
    if axis is not None and not isinstance(axis, list):
1235 1236 1237 1238 1239 1240 1241 1242
        if isinstance(axis, tuple):
            axis = list(axis)
        elif isinstance(axis, int):
            axis= [axis]
        else:
            raise TypeError(
                "The type of axis must be int, list or tuple, but received {}".format(type(axis)))

1243 1244 1245 1246 1247
    reduce_all = True if axis == None or axis == [] else False
    axis = axis if axis != None and axis != [] else [0]
    if in_dygraph_mode():
        return core.ops.reduce_max(x, 'dim', axis, 'keep_dim', keepdim,
                                   'reduce_all', reduce_all)
1248

1249
    helper = LayerHelper('max', **locals())
1250
    check_variable_and_dtype(
1251
        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'max')
1252

1253 1254
    out = helper.create_variable_for_type_inference(
            dtype=helper.input_dtype())
1255 1256
    helper.append_op(
        type='reduce_max',
1257
        inputs={'X': x},
1258 1259
        outputs={'Out': out},
        attrs={
1260 1261
            'dim': axis,
            'keep_dim': keepdim,
1262 1263 1264 1265
            'reduce_all': reduce_all
        })
    return out

1266
def min(x, axis=None, keepdim=False, name=None):
1267
    """
S
swtkiwi 已提交
1268

1269
    Computes the minimum of tensor elements over the given axis
1270

1271
    Args:
1272 1273
        x(Tensor): A tensor, the data type is float32, float64, int32, int64.
        axis(list|int, optional): The axis along which the minimum is computed.
1274
            If :attr:`None`, compute the minimum over all elements of
1275
            `x` and return a Tensor variable with a single element,
1276 1277 1278
            otherwise must be in the range :math:`[-x.ndim, x.ndim)`.
            If :math:`axis[i] < 0`, the axis to reduce is :math:`x.ndim + axis[i]`.
        keepdim(bool, optional): Whether to reserve the reduced dimension in the
1279
            output Tensor. The result tensor will have one fewer dimension
1280
            than the `x` unless :attr:`keepdim` is true, default
1281
            value is False.
W
WuHaobo 已提交
1282
        name(str, optional): The default value is None.  Normally there is no need for 
1283
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
1284

1285
    Returns:
1286
        Tensor, results of minimum on the specified axis of input tensor,
1287
        it's data type is the same as input's Tensor.
1288

1289 1290 1291
    Examples:
        .. code-block:: python

1292 1293
            import numpy as np
            import paddle
1294

1295
            paddle.disable_static()
1296

1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327
            # data_x is a variable with shape [2, 4]
            # the axis is a int element
            data_x = np.array([[0.2, 0.3, 0.5, 0.9],
                            [0.1, 0.2, 0.6, 0.7]])
            x = paddle.to_variable(data_x)
            result1 = paddle.min(x)
            print(result1.numpy())
            #[0.1]
            result2 = paddle.min(x, axis=0)
            print(result2.numpy())
            #[0.1 0.2 0.5 0.7]
            result3 = paddle.min(x, axis=-1)
            print(result3.numpy()) 
            #[0.2 0.1]
            result4 = paddle.min(x, axis=1, keepdim=True)
            print(result4.numpy())
            #[[0.2]
            # [0.1]]

            # data_y is a variable with shape [2, 2, 2]
            # the axis is list 
            data_y = np.array([[[1.0, 2.0], [3.0, 4.0]],
                               [[5.0, 6.0], [7.0, 8.0]]])
            y = paddle.to_variable(data_y)
            result5 = paddle.min(y, axis=[1, 2])
            print(result5.numpy()) 
            #[1. 5.]
            result6 = paddle.min(y, axis=[0, 1])
            print(result6.numpy())
            #[1. 2.]
    """
1328

1329
    if axis is not None and not isinstance(axis, list):
1330 1331 1332 1333 1334 1335 1336
        if isinstance(axis, tuple):
            axis = list(axis)
        elif isinstance(axis, int):
            axis= [axis]
        else:
            raise TypeError(
                "The type of axis must be int, list or tuple, but received {}".format(type(axis)))
1337 1338
    reduce_all = True if axis == None or axis == [] else False
    axis = axis if axis != None and axis != [] else [0]
1339
    if in_dygraph_mode():
1340
        return core.ops.reduce_min(x, 'dim', axis, 'keep_dim', keepdim,
1341
                                   'reduce_all', reduce_all)
1342 1343 1344 1345 1346 1347 1348

    helper = LayerHelper('min', **locals())
    check_variable_and_dtype(
        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'min')

    out = helper.create_variable_for_type_inference(
            dtype=helper.input_dtype())
1349 1350
    helper.append_op(
        type='reduce_min',
1351
        inputs={'X': x},
1352 1353
        outputs={'Out': out},
        attrs={
1354 1355
            'dim': axis,
            'keep_dim': keepdim,
1356 1357 1358 1359 1360
            'reduce_all': reduce_all
        })
    return out


W
WuHaobo 已提交
1361
def log1p(x, name=None):
1362
    """
1363 1364
	:alias_main: paddle.log1p
	:alias: paddle.log1p,paddle.tensor.log1p,paddle.tensor.math.log1p
S
swtkiwi 已提交
1365

1366 1367 1368 1369 1370 1371 1372 1373 1374
    Calculates the natural log of the given input tensor, element-wise.
    .. math::
        Out = \\ln(x+1)
    Args:
        x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64.
        name(str, optional): The default value is None.  Normally there is no need for 
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
    Returns:
        Variable: The natural log of the input LoDTensor or Tensor computed element-wise.
1375

1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398
    Examples:
        .. code-block:: python
            import paddle
            import paddle.fluid as fluid
            import numpy as np
            # Graph Organizing
            x = fluid.data(name="x", shape=[2,1], dtype="float32")
            res = paddle.log1p(x)
            # Create an executor using CPU as an example
            exe = fluid.Executor(fluid.CPUPlace())
            # Execute
            x_i = np.array([[0], [1]]).astype(np.float32)
            res_val, = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res])
            print(res_val) # [[0.], [0.6931472]]
    """

    if in_dygraph_mode():
        return core.ops.log1p(x)

    check_variable_and_dtype(x, 'x', ['float32', 'float64'], "log1p")
    inputs = {'X': [x]}
    helper = LayerHelper('log1p', **locals())
    dtype = helper.input_dtype(input_param_name='x')
W
WuHaobo 已提交
1399
    out = helper.create_variable_for_type_inference(dtype)
1400 1401
    helper.append_op(type="log1p", inputs={"X": x}, outputs={"Out": out})
    return out
B
Bai Yifan 已提交
1402

W
WuHaobo 已提交
1403

W
WuHaobo 已提交
1404
def addcmul(input, tensor1, tensor2, value=1.0, name=None):
B
Bai Yifan 已提交
1405
    """
1406 1407
	:alias_main: paddle.addcmul
	:alias: paddle.addcmul,paddle.tensor.addcmul,paddle.tensor.math.addcmul
S
swtkiwi 已提交
1408

B
Bai Yifan 已提交
1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441
    Calculate the element-wise multiplication of tensor1 and tensor2,
    then multiply the result by value, and add it to input. The shape of input,
    tensor1, tensor2 should be broadcastable.
    The equation is:
    ..  math::
        out = input + value * tensor1 * tensor2
    Args:
        input(Variable): The input to be added. A Tensor with type float32, float64, int32, int64.
        tensor1(Variable): The tensor to be multiplied. A Tensor with type float32, float64, int32, int64.
        tensor2(Variable): The tensor to be multiplied. A Tensor with type float32, float64, int32, int64.
        value(int|float): The multiplier for tensor1*tensor2. For float32 and float64 type input, value must be float, otherwise an integer.
        name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
                        Generally, no setting is required. Default: None.
    Returns:
        out(Variable): The output result. A Tensor with the same data type as input's.
    Examples:
        .. code-block:: python
          import paddle
          import paddle.fluid as fluid
          input = fluid.data(name='input', dtype='float32', shape=[3, 4])
          tensor1 = fluid.data(name='tenosr1', dtype='float32', shape=[1, 4])
          tensor2 = fluid.data(name='tensor2', dtype='float32', shape=[3, 4])
          data = paddle.addcmul(input, tensor1, tensor2, value=1.0)
    """

    check_variable_and_dtype(input, 'input', ['float32', 'float64', 'int32', 'int64'], 'addcmul')
    check_variable_and_dtype(tensor1, 'tensor1', ['float32', 'float64', 'int32', 'int64'], 'addcmul')
    check_variable_and_dtype(tensor2, 'tensor2', ['float32', 'float64', 'int32', 'int64'], 'addcmul')
    if convert_dtype(input.dtype) in ['float32', 'float64']:
        check_type(value, 'value', float, 'addcmul')
    if convert_dtype(input.dtype) in ['int32', 'int64']:
        check_type(value, 'value', int, 'addcmul')

W
WuHaobo 已提交
1442
    out = layers.elementwise_add(input, layers.elementwise_mul(tensor1, tensor2) * value)
B
Bai Yifan 已提交
1443
    return out
1444 1445


W
WuHaobo 已提交
1446
def clamp(input, min=None, max=None, name=None):
1447
    """
1448 1449
	:alias_main: paddle.clamp
	:alias: paddle.clamp,paddle.tensor.clamp,paddle.tensor.math.clamp
S
swtkiwi 已提交
1450

1451 1452 1453 1454 1455 1456 1457
    **clampe layer**

    This operator clamps all elements in input into the range [ min, max ] and return
    a resulting tensor as the following equation:

    .. math::

1458
        Out = MIN(MAX(x, min), max)
1459 1460

    Args:
1461 1462
        input (Variable): An input N-D Tensor or LoDTensor
            with data type float32, float64.
1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496
        min (float32|Variable): The lower bound with type ``float32`` or a ``Tensor``
            with shape [1] and type ``int32``, ``float32``, ``float64``.
        max (float32|Variable): The upper bound with type ``float32`` or a ``Tensor``
            with shape [1] and type ``int32``, ``float32``, ``float64``.
        name (str, optional): The default value is None. Normally there is no
            need for user to set this property. For more information, please
            refer to :ref:`api_guide_Name`.

    Returns:
        Variable: A Tensor or LodTensor with the same data type and data shape as input's.

    Examples:
        .. code-block:: python

            import paddle
            import paddle.fluid as fluid
            import numpy as np

            in1 = np.array([[1.2,3.5],
                            [4.5,6.4]]).astype('float32')
            with fluid.dygraph.guard():
                x1 = fluid.dygraph.to_variable(in1)
                out1 = paddle.tensor.clamp(x1, min=3.5, max=5.0)
                out2 = paddle.tensor.clamp(x1, min=2.5)
                print(out1.numpy())
                # [[3.5, 3.5]
                # [4.5, 5.0]]
                print(out2.numpy())
                # [[2.5, 3.5]
                # [[4.5, 6.4]
    """

    assert min is not None or max is not None, "either min or max should be defined."

W
WuHaobo 已提交
1497 1498 1499 1500 1501
    if in_dygraph_mode():
        min = sys.float_info.min if min is None else min
        max = sys.float_info.max if max is None else max
        return core.ops.clip(input, "min", min, "max", max)

1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528
    if min is not None:
        check_type(min, 'min', (float, Variable), 'clamp')
        if isinstance(min, Variable):
            check_dtype(min.dtype, 'min', ['float32', 'float64', 'int32'],
                        'clamp', '(When the type of min in clamp is Variable.)')
    if max is not None:
        check_type(max, 'max', (float, Variable), 'clamp')
        if isinstance(max, Variable):
            check_dtype(max.dtype, 'max', ['float32', 'float64', 'int32'],
                        'clamp', '(When the type of max in clamp is Variable.)')

    inputs = {'X': input}
    attrs = {'min': sys.float_info.min, 'max': sys.float_info.max}

    if isinstance(min, Variable):
        min.stop_gradient = True
        inputs['Min'] = min
    elif min is not None:
        attrs['min'] = min

    if isinstance(max, Variable):
        max.stop_gradient = True
        inputs['Max'] = max
    elif max is not None:
        attrs['max'] = max

    helper = LayerHelper('clamp', **locals())
W
WuHaobo 已提交
1529
    output = helper.create_variable_for_type_inference(
1530 1531 1532 1533 1534
            dtype=helper.input_dtype())
    helper.append_op(
        type='clip', inputs=inputs, outputs={'Out': [output]}, attrs=attrs)

    return output
F
Feiyu Chan 已提交
1535

W
WuHaobo 已提交
1536

1537
def trace(x, offset=0, axis1=0, axis2=1, name=None):
L
Li Fuchen 已提交
1538
    """
1539 1540
	:alias_main: paddle.trace
	:alias: paddle.trace,paddle.tensor.trace,paddle.tensor.math.trace
S
swtkiwi 已提交
1541

1542
    This OP computes the sum along diagonals of the input tensor x.
1543 1544

    If ``x`` is 2D, returns the sum of diagonal.
L
Li Fuchen 已提交
1545

1546
    If ``x`` has larger dimensions, then returns an tensor of diagonals sum, diagonals be taken from
1547
    the 2D planes specified by axis1 and axis2. By default, the 2D planes formed by the first and second axes
1548
    of the input tensor x.
L
Li Fuchen 已提交
1549

1550
    The argument ``offset`` determines where diagonals are taken from input tensor x:
L
Li Fuchen 已提交
1551 1552 1553 1554

    - If offset = 0, it is the main diagonal.
    - If offset > 0, it is above the main diagonal.
    - If offset < 0, it is below the main diagonal.
1555

L
Li Fuchen 已提交
1556
    Args:
1557 1558 1559 1560
        x(Variable): The input tensor x. Must be at least 2-dimensional. The input data type should be float32, float64, int32, int64.
        offset(int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals).
        axis1(int, optional): The first axis with respect to take diagonal. Default: 0.
        axis2(int, optional): The second axis with respect to take diagonal. Default: 1.
L
Li Fuchen 已提交
1561 1562 1563 1564 1565 1566 1567 1568 1569 1570
        name (str, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None.

    Returns:
        Variable: the output data type is the same as input data type.

    Examples:
        .. code-block:: python

            import paddle
            import numpy as np
1571

L
Li Fuchen 已提交
1572 1573 1574
            case1 = np.random.randn(2, 3).astype('float32')
            case2 = np.random.randn(3, 10, 10).astype('float32')
            case3 = np.random.randn(3, 10, 5, 10).astype('float32')
1575

1576
            paddle.disable_static()
1577

1578 1579 1580
            case1 = paddle.to_variable(case1)
            case2 = paddle.to_variable(case2)
            case3 = paddle.to_variable(case3)
1581 1582 1583
            data1 = paddle.trace(case1) # data1.shape = [1]
            data2 = paddle.trace(case2, offset=1, axis1=1, axis2=2) # data2.shape = [3]
            data3 = paddle.trace(case3, offset=-3, axis1=1, axis2=-1) # data2.shape = [3, 5]
L
Li Fuchen 已提交
1584
    """
1585 1586
    inputs = {'Input': [x]}
    attrs = {'offset': offset, 'axis1': axis1, 'axis2': axis2}
L
Li Fuchen 已提交
1587 1588

    def __check_input(input, offset, dim1, dim2):
1589
        check_dtype(x.dtype, 'Input',
L
Li Fuchen 已提交
1590 1591 1592
                    ['int32', 'int64', 'float16', 'float32', 'float64'],
                    'trace')

1593
        input_shape = list(x.shape)
L
Li Fuchen 已提交
1594
        assert len(input_shape) >= 2,                     \
1595 1596
                "The x must be at least 2-dimensional, "   \
                "But received Input x's dimensional: %s.\n" %  \
L
Li Fuchen 已提交
1597 1598
                len(input_shape)

1599 1600
        axis1_ = axis1 if axis1 >= 0 else len(input_shape) + axis1
        axis2_ = axis2 if axis2 >= 0 else len(input_shape) + axis2
L
Li Fuchen 已提交
1601

1602 1603 1604
        assert axis1_ < len(input_shape),     \
            "The argument axis1 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
            % (-(len(input_shape)), len(input_shape) - 1, axis1)
L
Li Fuchen 已提交
1605

1606 1607 1608
        assert axis2_ < len(input_shape),   \
            "The argument axis2 is out of range (expected to be in range of [%d, %d], but got %d).\n"   \
            % (-(len(input_shape)), len(input_shape) - 1, axis2)
L
Li Fuchen 已提交
1609 1610


1611 1612 1613
        assert  axis1_ != axis2_,   \
               "axis1 and axis2 cannot be the same axis." \
                "But received axis1 = %d, axis2 = %d\n"%(axis1, axis2)
L
Li Fuchen 已提交
1614 1615

    if not in_dygraph_mode():
1616
        __check_input(input, offset, axis1, axis2)
L
Li Fuchen 已提交
1617 1618
    helper = LayerHelper('trace', **locals())

1619
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
L
Li Fuchen 已提交
1620 1621 1622

    helper.append_op(
        type='trace',
1623
        inputs={'Input': [x]},
L
Li Fuchen 已提交
1624
        attrs={'offset': offset,
1625 1626
               'axis1': axis1,
               'axis2': axis2},
L
Li Fuchen 已提交
1627 1628 1629
        outputs={'Out': [out]})
    return out

F
Feiyu Chan 已提交
1630
@templatedoc(op_type="kron")
W
WuHaobo 已提交
1631
def kron(x, y, name=None):
S
swtkiwi 已提交
1632
    """
1633 1634
	:alias_main: paddle.kron
	:alias: paddle.kron,paddle.tensor.kron,paddle.tensor.math.kron
S
swtkiwi 已提交
1635 1636

${comment}
F
Feiyu Chan 已提交
1637 1638

    Args:
1639
        x (Variable): the fist operand of kron op, data type: float16, float32,
F
Feiyu Chan 已提交
1640
            float64, int32 or int64.
1641 1642
        y (Variable): the second operand of kron op, data type: float16,
            float32, float64, int32 or int64. Its data type should be the same
F
Feiyu Chan 已提交
1643
            with x.
1644 1645
        name(str, optional): The default value is None.  Normally there is no
            need for user to set this property.  For more information, please
F
Feiyu Chan 已提交
1646 1647 1648 1649 1650 1651 1652
            refer to :ref:`api_guide_Name`.

    Returns:
        Variable: The output of kron op, data type: float16, float32, float64, int32 or int64. Its data is the same with x.

    Examples:
        .. code-block:: python
1653

F
Feiyu Chan 已提交
1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683
          import paddle
          from paddle import fluid
          import paddle.fluid.dygraph as dg
          import numpy as np

          a = np.arange(1, 5).reshape(2, 2).astype(np.float32)
          b = np.arange(1, 10).reshape(3, 3).astype(np.float32)

          place = fluid.CPUPlace()
          with dg.guard(place):
              a_var = dg.to_variable(a)
              b_var = dg.to_variable(b)
              c_var = paddle.kron(a_var, b_var)
              c_np = c_var.numpy()
          print(c_np)

          #[[ 1.  2.  3.  2.  4.  6.]
          # [ 4.  5.  6.  8. 10. 12.]
          # [ 7.  8.  9. 14. 16. 18.]
          # [ 3.  6.  9.  4.  8. 12.]
          # [12. 15. 18. 16. 20. 24.]
          # [21. 24. 27. 28. 32. 36.]]
    """
    if in_dygraph_mode():
        return core.ops.kron(x, y)

    helper = LayerHelper('kron', **locals())
    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'kron')
    check_variable_and_dtype(y, 'y', ['float16', 'float32', 'float64', 'int32', 'int64'], 'kron')

W
WuHaobo 已提交
1684
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
F
Feiyu Chan 已提交
1685 1686
    helper.append_op(type="kron", inputs={"X": x, "Y": y}, outputs={"Out": out})
    return out
1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705


def cumsum(x, axis=None, dtype=None, name=None):
    """
    The cumulative sum of the elements along a given axis. The first element of the result is the same of the first element of the input. 

    Args:
        x (Tensor): Input of cumsum operator, the Tensor needed to be cumsumed. 
        axis (int, optional): The dimension to accumulate along. -1 means the last dimension. The default (None) is to compute the cumsum over the flattened array.
        dtype (str, optional): The data type of the output tensor, can be float32, float64, int32, int64. If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. The default value is None. 
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, the result of cumsum operator, output of cumsum operator. 

    Examples:
        .. code-block:: python
            
            import paddle
1706
            from paddle import to_variable
1707 1708
            import numpy as np

1709
            paddle.disable_static()
1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753
            data_np = np.arange(12).reshape(3, 4)
            data = to_variable(data_np)

            y = paddle.cumsum(data)
            print(y.numpy())
            # [ 0  1  3  6 10 15 21 28 36 45 55 66]

            y = paddle.cumsum(data, axis=0)
            print(y.numpy())
            # [[ 0  1  2  3]
            #  [ 4  6  8 10]
            #  [12 15 18 21]]
            
            y = paddle.cumsum(data, axis=-1)
            print(y.numpy())
            # [[ 0  1  3  6]
            #  [ 4  9 15 22]
            #  [ 8 17 27 38]]

            y = paddle.cumsum(data, dtype='float64')
            print(y.dtype)
            # VarType.FP64
    """
    if axis is None:
        flatten = True
    else:
        flatten = False
    if dtype is not None and x.dtype != convert_np_dtype_to_dtype_(dtype):
        x = layers.cast(x, dtype)

    if in_dygraph_mode():
        if axis is None:
            return core.ops.cumsum(x, 'flatten', flatten)
        else:
            return core.ops.cumsum(x, 'axis', axis, 'flatten', flatten)

    check_type(x, 'x', (Variable), 'cumsum')
    locals_var = locals().copy()
    kwargs = dict()
    for name, val in locals_var.items():
        if val is not None:
            kwargs[name] = val
    _cum_sum_ = generate_layer_fn('cumsum')
    return _cum_sum_(**kwargs)