math.py 76.5 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
Y
Yang Zhang 已提交
18
import numpy as np
19

20
from paddle.common_ops_import import *
21 22
from paddle.tensor import cast
import paddle
23
from ..fluid import layers
L
Li Fuchen 已提交
24 25 26
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
27
from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
28 29 30

# TODO: define math functions
# yapf: disable
31 32 33 34 35
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
36 37
from ..fluid.layers import sinh    #DEFINE_ALIAS
from ..fluid.layers import cosh    #DEFINE_ALIAS
38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
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 square    #DEFINE_ALIAS
from ..fluid.layers import stanh    #DEFINE_ALIAS
from ..fluid.layers import atan    #DEFINE_ALIAS
from ..fluid.layers import erf    #DEFINE_ALIAS
60 61
from ..fluid.layers import sqrt    #DEFINE_ALIAS
from ..fluid.layers import sin    #DEFINE_ALIAS
62

63 64 65
from ..fluid.layers import increment    #DEFINE_ALIAS
from ..fluid.layers import multiplex    #DEFINE_ALIAS
from ..fluid.layers import sums    #DEFINE_ALIAS
G
guofei 已提交
66
from ..fluid import layers
67
import paddle
68

69

70
__all__ = [
71 72 73 74 75 76
        'abs',
        'acos',
        'asin',
        'atan',
        'ceil',
        'cos',
77
        'cosh',
78 79 80 81 82 83 84 85 86
        'cumsum',
        'elementwise_add',
        'elementwise_div',
        'elementwise_floordiv',
        'elementwise_mod',
        'elementwise_pow',
        'elementwise_sub',
        'exp',
        'floor',
87
        'increment',
88
        'log',
89
        'logsumexp',
90
        'mul',
91
        'multiplex',
92
        'pow',
93
        'prod',
94 95 96 97 98 99 100 101 102 103
        'reciprocal',
        'reduce_max',
        'reduce_min',
        'reduce_prod',
        'reduce_sum',
        'round',
        'rsqrt',
        'scale',
        'sign',
        'sin',
104
        'sinh',
105 106 107 108
        'sqrt',
        'square',
        'stanh',
        'sum',
109
        'sums',
110 111 112
        'tanh',
        'elementwise_sum',
        'max',
113
        'maximum',
114
        'min',
115
        'minimum',
116
        'mm',
117 118 119 120 121
        'divide',
        'floor_divide',
        'remainder',
        'mod',
        'floor_mod',
122
        'multiply',
123 124 125
        'add',
        'atan',
        'logsumexp',
126
        'inverse',
127 128 129 130
        'log1p',
        'erf',
        'addcmul',
        'addmm',
Y
Yang Zhang 已提交
131
        'clip',
L
Li Fuchen 已提交
132
        'trace',
J
Jack Zhou 已提交
133 134 135 136
        'kron',
        'isfinite',
        'isinf',
        'isnan'
137 138 139
]
# yapf: enable.

140 141 142 143 144 145 146 147 148 149 150 151 152
_supported_int_dtype_ = [
    VarDesc.VarType.UINT8,
    VarDesc.VarType.INT8,
    VarDesc.VarType.INT16,
    VarDesc.VarType.INT32,
    VarDesc.VarType.INT64,
]

_supported_float_dtype_ = [
    VarDesc.VarType.FP32,
    VarDesc.VarType.FP64,
]

153
def pow(x, y, name=None):
154
    """
155
    Compute the power of tensor elements. The equation is:
S
swtkiwi 已提交
156

157 158
    .. math::
        out = x^{y} 
159

160 161
    **Note**:
    ``paddle.pow`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
162 163


164 165 166 167 168
    Args:
        x (Tensor): An N-D Tensor, the data type is float32, float64, int32 or int64.
        y (Tensor): An N-D Tensor with type float32, float64, int32 or int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
    
169
    Returns:
170
        N-D Tensor. A location into which the result is stored. Its dimension equals with $x$.
171 172 173

    Examples:

174
        ..  code-block:: python
175 176 177

            import paddle

178 179 180
            paddle.disable_static()
            
            # example 1: y is a float
181
            x = paddle.to_tensor([1, 2, 3])
182 183 184 185 186 187 188 189
            y = 2
            res = paddle.pow(x, y)
            print(res.numpy()) # [1 4 9]
            
            # example 2: y is a Tensor
            y = paddle.fill_constant(shape=[1], value=2, dtype='float32')
            res = paddle.pow(x, y)
            print(res.numpy()) # [1 4 9]
190 191

    """
192
    # in dynamic graph mode
W
WuHaobo 已提交
193
    if in_dygraph_mode():
194 195 196
        if isinstance(y, (int, float)):
            return core.ops.pow(x, 'factor', y)
        elif isinstance(y, (paddle.Tensor, Variable)):
197

198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230
            if x.dtype != y.dtype:
                y = cast(y, dtype='float64')
                x = cast(x, dtype='float64')
                out_dygraph = _elementwise_op_in_dygraph(
                x, y, axis=-1, act=None, op_name='elementwise_pow')
                return out_dygraph

            return _elementwise_op_in_dygraph(
                x, y, axis=-1, act=None, op_name='elementwise_pow')
        else:
            raise TypeError('y must be scalar or tensor type, but received: %s '% (y.dtype))
    # in static graph mode
    else:
        if isinstance(y, (int, float)):
            helper = LayerHelper('pow', **locals())
            inputs = {'X': x}
            attrs = {'factor': y}
            out = helper.create_variable_for_type_inference(dtype=x.dtype)
            helper.append_op(
                type='pow', inputs=inputs, outputs={'Out': out}, attrs=attrs)
            return out
        elif isinstance(y, (paddle.Tensor, Variable)):
            # TODO A potential speed improvement is supporting different types in C++ and removing the cast ops here
            helper = LayerHelper('elementwise_pow', **locals())
            if x.dtype != y.dtype:
                y = cast(y, dtype='float64')
                x = cast(x, dtype='float64')
                out = helper.create_variable_for_type_inference(dtype=x.dtype)
            else:
                out = helper.create_variable_for_type_inference(dtype=x.dtype)
            return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
        else:
            raise TypeError('y must be scalar or tensor type, but received: %s '% (type(y)))
231 232 233



234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253
@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)

254 255
    out = helper.kwargs.get('out', None)

256 257 258 259 260 261 262 263 264 265 266 267
    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)
268 269 270 271 272 273

    if out is None:
        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)
274 275 276 277 278 279 280 281 282 283 284

    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)


Y
Yang Zhang 已提交
285
def add(x, y, name=None):
286 287 288 289 290 291 292
    """
Examples:

    ..  code-block:: python

        import paddle

Y
Yang Zhang 已提交
293
        paddle.disable_static()
294 295
        x = paddle.to_tensor([2, 3, 4], 'float64')
        y = paddle.to_tensor([1, 5, 2], 'float64')
W
WuHaobo 已提交
296
        z = paddle.add(x, y)
Y
Yang Zhang 已提交
297 298
        np_z = z.numpy()
        print(np_z)  # [3., 8., 6. ]
299 300 301 302 303 304

    """
    op_type = 'elementwise_add'
    axis = -1
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
Y
Yang Zhang 已提交
305
            x, y, axis=axis, op_name=op_type)
306 307 308 309

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


310
def divide(x, y, name=None):
311
    """
312
    Divide two tensors element-wise. The equation is:
313

314 315
    .. math::
        out = x / y
316

317 318
    **Note**:
    ``paddle.divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
319

320 321 322 323
    Args:
        x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
        y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
324

325 326
    Returns:
        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
327

328
    Examples:
329

330
        ..  code-block:: python
331

332
            import paddle
333

334
            paddle.disable_static()
335

336 337
            x = paddle.to_tensor([2, 3, 4], dtype='float64')
            y = paddle.to_tensor([1, 5, 2], dtype='float64')
338 339
            z = paddle.divide(x, y)
            print(z.numpy())  # [2., 0.6, 2.]
340

341 342 343 344 345
    """
    op_type = 'elementwise_div'
    axis = -1
    act = None
    if in_dygraph_mode():
346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375
        # rule 1 : avoid numpy.ndarray
        if isinstance(x, numpy.ndarray) or isinstance(y, numpy.ndarray):
            raise TypeError("divide(): arguments must be Tensor or scalar, not numpy.ndarray.")

        # rule 2: both the inputs are not Tensor
        elif not isinstance(x, paddle.Tensor) and not isinstance(y, paddle.Tensor):
            x = paddle.full(shape=[1], dtype=paddle.get_default_dtype(), fill_value=x)
            y = paddle.full(shape=[1], dtype=paddle.get_default_dtype(), fill_value=y)

        # rule 3: both the inputs are Tensor
        elif isinstance(x, paddle.Tensor) and isinstance(y, paddle.Tensor):
            if y.dtype != x.dtype:
                raise TypeError("divide(): argument position 1 and argument position 2 must have the same dtype."
                                "But x is {}, y is {}".format(x.dtype, y.dtype))
            elif x.dtype in _supported_int_dtype_:
                x = x.astype(paddle.get_default_dtype())
                y = y.astype(paddle.get_default_dtype())

        # rule 4: x is Tensor, y is scalar
        elif isinstance(x, paddle.Tensor) and not isinstance(y, paddle.Tensor):
            if x.dtype in _supported_int_dtype_:
                x = x.astype(paddle.get_default_dtype())
            y = paddle.full(shape=[1], dtype=x.dtype, fill_value=y)

        # rule 5: x is scalar, y is Tensor
        elif not isinstance(x, paddle.Tensor) and isinstance(y, paddle.Tensor):
            if y.dtype in _supported_int_dtype_:
                y = y.astype(paddle.get_default_dtype())
            x = paddle.full(shape=[1], dtype=y.dtype, fill_value=x)

376 377
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)
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
    # rule 1 : avoid numpy.ndarray
    if isinstance(x, numpy.ndarray) or isinstance(y, numpy.ndarray):
        raise TypeError("divide(): arguments must be Tensor or scalar, not numpy.ndarray.")

    # rule 2: both the inputs are not Tensor
    elif not isinstance(x, Variable) and not isinstance(y, Variable):
        x = paddle.fill_constant(shape=[1], dtype=paddle.get_default_dtype(), value=x)
        y = paddle.fill_constant(shape=[1], dtype=paddle.get_default_dtype(), value=y)

    # rule 3: both the inputs are Tensor
    elif isinstance(x, Variable) and isinstance(y, Variable):
        if y.dtype != x.dtype:
            raise TypeError("divide(): argument position 1 and argument position 2 must have the same dtype."
                            "But x is {}, y is {}".format(x.dtype, y.dtype))
        elif x.dtype in _supported_int_dtype_:
            x = paddle.cast(x, paddle.get_default_dtype())
            y = paddle.cast(y, paddle.get_default_dtype())

    # rule 4: x is Tensor, y is scalar
    elif isinstance(x, Variable) and not isinstance(y, Variable):
        if x.dtype in _supported_int_dtype_:
            x = paddle.cast(x, paddle.get_default_dtype())
        y = paddle.fill_constant(shape=[1], dtype=x.dtype, value=y)

    # rule 5: x is scalar, y is Tensor
    elif not isinstance(x, Variable) and isinstance(y, Variable):
        if y.dtype in _supported_int_dtype_:
            y = paddle.cast(y, paddle.get_default_dtype())
        x = paddle.fill_constant(shape=[1], dtype=y.dtype, value=x)

409
    return _elementwise_op(LayerHelper(op_type, **locals()))
410 411


412 413 414
def floor_divide(x, y, name=None):
    """
    Floor divide two tensors element-wise. The equation is:
415

416 417
    .. math::
        out = x // y
418

419 420
    **Note**:
    ``paddle.floor_divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
421

422 423 424 425
    Args:
        x (Tensor): the input tensor, it's data type should be int32, int64.
        y (Tensor): the input tensor, it's data type should be int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
426

427 428
    Returns:
        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
429

430
    Examples:
431

432
        ..  code-block:: python
433

434
            import paddle
435

436
            paddle.disable_static()
437

438 439
            x = paddle.to_tensor([2, 3, 8, 7])
            y = paddle.to_tensor([1, 5, 3, 3])
440 441
            z = paddle.floor_divide(x, y)
            print(z.numpy())  # [2, 0, 2, 2]
442

443 444 445 446
    """
    op_type = 'elementwise_floordiv'
    axis = -1
    if in_dygraph_mode():
447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469
        # rule 1 : avoid numpy.ndarray
        if isinstance(x, numpy.ndarray) or isinstance(y, numpy.ndarray):
            raise TypeError("floor_divide(): arguments must be Tensor or scalar, not numpy.ndarray.")

        # rule 2: both the inputs are not Tensor
        elif not isinstance(x, paddle.Tensor) and not isinstance(y, paddle.Tensor):
            x = paddle.full(shape=[1], dtype=paddle.get_default_dtype(), fill_value=x)
            y = paddle.full(shape=[1], dtype=paddle.get_default_dtype(), fill_value=y)

        # rule 3: both the inputs are Tensor
        elif isinstance(x, paddle.Tensor) and isinstance(y, paddle.Tensor):
            if y.dtype != x.dtype:
                raise TypeError("floor_divide(): argument position 1 and argument position 2 must have the same dtype."
                                "But x is {}, y is {}".format(x.dtype, y.dtype))

        # rule 4: x is Tensor, y is scalar
        elif isinstance(x, paddle.Tensor) and not isinstance(y, paddle.Tensor):
            y = paddle.full(shape=[1], dtype=x.dtype, fill_value=y)

        # rule 5: x is scalar, y is Tensor
        elif not isinstance(x, paddle.Tensor) and isinstance(y, paddle.Tensor):
            x = paddle.full(shape=[1], dtype=y.dtype, fill_value=x)

470 471
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, op_name=op_type)
472

473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495
    # rule 1 : avoid numpy.ndarray
    if isinstance(x, numpy.ndarray) or isinstance(y, numpy.ndarray):
        raise TypeError("divide(): arguments must be Tensor or scalar, not numpy.ndarray.")

    # rule 2: both the inputs are not Tensor
    elif not isinstance(x, Variable) and not isinstance(y, Variable):
        x = paddle.fill_constant(shape=[1], dtype=paddle.get_default_dtype(), value=x)
        y = paddle.fill_constant(shape=[1], dtype=paddle.get_default_dtype(), value=y)

    # rule 3: both the inputs are Tensor
    elif isinstance(x, Variable) and isinstance(y, Variable):
        if y.dtype != x.dtype:
            raise TypeError("divide(): argument position 1 and argument position 2 must have the same dtype."
                            "But x is {}, y is {}".format(x.dtype, y.dtype))

    # rule 4: x is Tensor, y is scalar
    elif isinstance(x, Variable) and not isinstance(y, Variable):
        y = paddle.fill_constant(shape=[1], dtype=x.dtype, value=y)

    # rule 5: x is scalar, y is Tensor
    elif not isinstance(x, Variable) and isinstance(y, Variable):
        x = paddle.fill_constant(shape=[1], dtype=y.dtype, value=x)

496
    return _elementwise_op(LayerHelper(op_type, **locals()))
497 498


499
def remainder(x, y, name=None):
500
    """
501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524
    Mod two tensors element-wise. The equation is:

    .. math::
        out = x \% y

    **Note**:
    ``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .

    Args:
        x (Tensor): the input tensor, it's data type should be int32, int64.
        y (Tensor): the input tensor, it's data type should be int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.

    Examples:

        ..  code-block:: python

            import paddle

            paddle.disable_static()

525 526
            x = paddle.to_tensor([2, 3, 8, 7])
            y = paddle.to_tensor([1, 5, 3, 3])
527 528 529 530 531
            z = paddle.remainder(x, y)
            print(z.numpy())  # [0, 3, 2, 1]

    """
    op_type = 'elementwise_mod'
532 533
    axis = -1
    if in_dygraph_mode():
534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550
        # rule 1 : avoid numpy.ndarray
        if isinstance(x, numpy.ndarray) or isinstance(y, numpy.ndarray):
            raise TypeError("remainder(): arguments must be Tensor or scalar, not numpy.ndarray.")

        elif not isinstance(x, paddle.Tensor):
            raise TypeError("remainder(): arguments position 1 must be Tensor, not {}".format(type(x)))

        # rule 3: both the inputs are Tensor
        elif isinstance(y, paddle.Tensor):
            if y.dtype != x.dtype:
                raise TypeError("remainder(): argument position 1 and argument position 2 must have the same dtype."
                                "But x is {}, y is {}".format(x.dtype, y.dtype))

        # rule 4: x is Tensor, y is scalar
        elif not isinstance(y, paddle.Tensor):
            y = paddle.full(shape=[1], dtype=x.dtype, fill_value=y)

551
        return _elementwise_op_in_dygraph(
552
            x, y, axis=axis, op_name=op_type)
553

554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570
    # rule 1 : avoid numpy.ndarray
    if isinstance(x, numpy.ndarray) or isinstance(y, numpy.ndarray):
        raise TypeError("remainder(): arguments must be Tensor or scalar, not numpy.ndarray.")

    elif not isinstance(x, Variable):
        raise TypeError("remainder(): arguments position 1 must be Tensor, not {}".format(type(x)))

    # rule 3: both the inputs are Tensor
    elif isinstance(y, Variable):
        if y.dtype != x.dtype:
            raise TypeError("remainder(): argument position 1 and argument position 2 must have the same dtype."
                            "But x is {}, y is {}".format(x.dtype, y.dtype))

    # rule 4: x is Tensor, y is scalar
    elif not isinstance(y, paddle.Tensor):
        y = paddle.fill_constant(shape=[1], dtype=x.dtype, value=y)

571 572 573
    return _elementwise_op(LayerHelper(op_type, **locals()))


574 575 576 577
mod = remainder  #DEFINE_ALIAS
floor_mod = remainder  #DEFINE_ALIAS


578 579
def multiply(x, y, axis=-1, name=None):
    """
580
    multiply two tensors element-wise. The equation is:
581

582 583
    .. math::
        out = x * y
584

585 586
    **Note**:
    ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
587

588 589 590 591
    Args:
        x (Tensor): the input tensor, its data type should be float32, float64, int32, int64.
        y (Tensor): the input tensor, its data type should be float32, float64, int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
592

593 594
    Returns:
        N-D Tensor. A location into which the result is stored. Its dimension equals with $x$.
595

596 597 598 599 600 601 602
    Examples:

        ..  code-block:: python

            import paddle

            paddle.disable_static()
603 604
            x = paddle.to_tensor([[1, 2], [3, 4]])
            y = paddle.to_tensor([[5, 6], [7, 8]])
605 606 607
            res = paddle.multiply(x, y)
            print(res.numpy()) # [[5, 12], [21, 32]]

608 609
            x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
            y = paddle.to_tensor([1, 2])
610 611
            res = paddle.multiply(x, y, axis=1)
            print(res.numpy()) # [[[1, 2, 3], [2, 4, 6]]]
612 613 614 615

    """
    op_type = 'elementwise_mul'
    act = None
616 617 618 619 620
    if x.dtype != y.dtype:
        raise TypeError(
            'Input tensors must be same type, but received type of x: %s, type of y: %s '
            % (x.dtype, y.dtype))

621 622 623 624 625 626
    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()))

627 628 629 630 631 632 633 634 635 636 637
def maximum(x, y, axis=-1, name=None):
    """
Examples:

    .. code-block:: python

        import paddle
        import numpy as np

        paddle.disable_static()
  
638 639
        x = paddle.to_tensor([[1, 2], [3, 4]])
        y = paddle.to_tensor([[5, 6], [7, 8]])
640 641 642 643 644
        res = paddle.maximum(x, y)
        print(res.numpy())
        #[[5. 6.]
        # [7. 8.]]

645 646
        x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
        y = paddle.to_tensor([1, 2])
647 648 649 650 651
        res = paddle.maximum(x, y, axis=1)
        print(res.numpy())
        #[[[1. 2. 3.]
        #  [2. 2. 3.]]]

652 653
        x = paddle.to_tensor([2, 3, 5], dtype='float32')
        y = paddle.to_tensor([1, 4, np.nan], dtype='float32')
654 655 656 657
        res = paddle.maximum(x, y)
        print(res.numpy())
        #[ 2.  4. nan]

658 659
        x = paddle.to_tensor([5, 3, np.inf], dtype='float32')
        y = paddle.to_tensor([1, 4, 5], dtype='float32')
660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678
        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
679

680 681
        paddle.disable_static()
  
682 683
        x = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32')
        y = paddle.to_tensor([[5, 6], [7, 8]], dtype='float32')
684 685 686 687 688
        res = paddle.minimum(x, y)
        print(res.numpy())
        #[[1. 2.]
        # [3. 4.]]

689 690
        x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]], dtype='float32')
        y = paddle.to_tensor([1, 2], dtype='float32')
691 692 693 694 695
        res = paddle.minimum(x, y, axis=1)
        print(res.numpy())
        #[[[1. 1. 1.]
        #  [2. 2. 2.]]]

696 697
        x = paddle.to_tensor([2, 3, 5], dtype='float32')
        y = paddle.to_tensor([1, 4, np.nan], dtype='float32')
698 699 700 701
        res = paddle.minimum(x, y)
        print(res.numpy())
        #[ 1.  3. nan]

702 703
        x = paddle.to_tensor([5, 3, np.inf], dtype='float32')
        y = paddle.to_tensor([1, 4, 5], dtype='float32')
704 705 706 707 708 709 710 711 712 713
        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()))
714

715 716
for func in [
        add,
717 718 719
        maximum,
        minimum,
        multiply
720
]:
721
    proto_dict = {'add': 'elementwise_add', 'div': 'elementwise_div', 'maximum': 'elementwise_max', 'minimum': 'elementwise_min', 'multiply': 'elementwise_mul'}
722 723
    op_proto = OpProtoHolder.instance().get_op_proto(proto_dict[func.__name__])

Y
Yang Zhang 已提交
724 725 726 727 728 729 730
    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` "
    ]

    func.__doc__ = _generate_doc_string_(
731 732
        op_proto,
        additional_args_lines=additional_args_lines,
733
        skip_attrs_set={"x_data_format", "y_data_format", "axis",
734
            "use_quantizer", "mkldnn_data_type", "Scale_x", "Scale_y", "Scale_out"
735
        }) + """\n""" + str(func.__doc__)
736

Y
Yang Zhang 已提交
737

738
def sum(x, axis=None, dtype=None, keepdim=False, name=None):
739 740 741 742
    """
    Computes the sum of tensor elements over the given dimension.

    Args:
743 744 745
        x (Tensor): An N-D Tensor, the data type is float32, float64, int32 or int64.
        axis (int|list|tuple, optional): The dimensions along which the sum is performed. If
            :attr:`None`, sum all elements of :attr:`x` and return a
746
            Tensor variable with a single element, otherwise must be in the
747 748 749 750 751 752 753
            range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`,
            the dimension to reduce is :math:`rank + axis[i]`.
        dtype (str, optional): The dtype of output Tensor. The default value is None, the dtype
            of output is the same as input Tensor `x`.
        keepdim (bool, optional): Whether to reserve the reduced dimension in the
            output Tensor. The result Tensor will have one fewer dimension
            than the :attr:`x` unless :attr:`keepdim` is true, default
754
            value is False.
755
        name (str, optional): The default value is None. Normally there is no need for
756 757 758
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`

    Returns:
759 760
        Tensor: Results of summation operation on the specified axis of input Tensor `x`,
        it's data type is the same as `x`.
761 762

    Raises:
763 764
        ValueError: If the data type of `x` is float64, :attr:`dtype` can not be float32 or int32.
        ValueError: If the data type of `x` is int64, :attr:`dtype` can not be int32.
765
        TypeError: The type of :attr:`axis` must be int, list or tuple.
766

767 768 769 770
    Examples:
        .. code-block:: python

            import paddle
771 772
            paddle.disable_static()

773
            # x is a Tensor with following elements:
774 775 776
            #    [[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.
777 778
            x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
                                  [0.1, 0.2, 0.6, 0.7]])
779
            out1 = paddle.sum(x)  # [3.5]
780 781 782
            out2 = paddle.sum(x, axis=0)  # [0.3, 0.5, 1.1, 1.6]
            out3 = paddle.sum(x, axis=-1)  # [1.9, 1.6]
            out4 = paddle.sum(x, axis=1, keepdim=True)  # [[1.9], [1.6]]
783

784
            # y is a Tensor with shape [2, 2, 2] and elements as below:
785 786 787
            #      [[[1, 2], [3, 4]],
            #      [[5, 6], [7, 8]]]
            # Each example is followed by the corresponding output tensor.
788 789
            y = paddle.to_tensor([[[1, 2], [3, 4]], 
                                  [[5, 6], [7, 8]]])
790 791
            out5 = paddle.sum(y, axis=[1, 2]) # [10, 26]
            out6 = paddle.sum(y, axis=[0, 1]) # [16, 20]
792
    """
793 794 795 796 797 798 799 800 801 802 803
    if axis is not None and not isinstance(axis, (list, tuple)):
        axis = [axis]

    if not axis:
        reduce_all_flag = True
    else:
        if len(axis) == len(x.shape):
            reduce_all_flag = True
        else:
            reduce_all_flag = False

804
    attrs = {
805 806 807
        'dim': axis if axis != None and axis != [] and axis != () else [0],
        'keep_dim': keepdim,
        'reduce_all': reduce_all_flag
808 809 810 811
    }
    dtype_flag = False
    if dtype is not None:
        if dtype in ['float64', 'int64']:
812 813
            if (convert_dtype(x.dtype) == "float32" and dtype == "float64") or \
               (convert_dtype(x.dtype) == "int32" and dtype == "int64"):
814
                attrs.update({
815
                    'in_dtype': x.dtype,
816 817 818 819 820
                    'out_dtype': convert_np_dtype_to_dtype_(dtype)
                })
                dtype_flag = True

    if in_dygraph_mode():
821
        axis = axis if axis != None and axis != [] else [0]
822
        if dtype_flag:
823 824 825
            return core.ops.reduce_sum(x, 'dim', axis, 'keep_dim', keepdim,
                                       'reduce_all', reduce_all_flag, 'in_dtype',
                                       x.dtype, 'out_dtype',
826 827
                                       convert_np_dtype_to_dtype_(dtype))
        else:
828 829
            return core.ops.reduce_sum(x, 'dim', axis, 'keep_dim', keepdim,
                                       'reduce_all', reduce_all_flag)
830
    check_variable_and_dtype(
831
        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'sum')
832 833 834 835 836 837 838 839 840 841 842

    if dtype is not None:
        check_dtype(dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'], 'sum')
        x_dtype = convert_dtype(x.dtype)

        if (x_dtype == "float64" and dtype in ["float32", "int32"]) or \
                (x_dtype == "int64" and dtype == "int32"):
            raise ValueError("The input(x)'s dtype is {} but the attr(dtype) of sum is {}, "
                             "which may cause data type overflows. Please reset attr(dtype) of sum."
                             .format(x_dtype, dtype))

843 844
    check_type(axis, 'axis', (int, list, tuple, type(None)), 'sum')

845 846 847 848 849
    helper = LayerHelper('sum', **locals())
    if dtype_flag:
        out = helper.create_variable_for_type_inference(
            dtype=convert_np_dtype_to_dtype_(dtype))
    else:
850
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
851 852
    helper.append_op(
        type='reduce_sum',
853
        inputs={'X': x},
854 855 856
        outputs={'Out': out},
        attrs=attrs)
    return out
857

858

859 860 861
@templatedoc(op_type="sum")
def elementwise_sum(inputs, name=None):
    """
862 863
	:alias_main: paddle.elementwise_sum
	:alias: paddle.elementwise_sum,paddle.tensor.elementwise_sum,paddle.tensor.math.elementwise_sum
S
swtkiwi 已提交
864

865
    ${comment}
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
    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:
898 899
        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.
900 901 902 903
        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:
904
        Variable: the sum of input :math:`inputs` . its shape and data types are consistent with :math:`inputs` .
905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929

    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.
930 931
            # 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,
932 933 934 935
            #       and '__int64' on Windows. They both represent 64-bit integer variables.
    """

    helper = LayerHelper('elementwise_sum', **locals())
936 937 938 939 940 941 942 943 944 945 946
    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')


947 948 949 950 951 952 953 954 955 956 957
    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
958
def mm(input, mat2, name=None):
959
    """
960 961
	:alias_main: paddle.mm
	:alias: paddle.mm,paddle.tensor.mm,paddle.tensor.math.mm
S
swtkiwi 已提交
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
    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
1011
        out = _varbase_creator(dtype=input.dtype)
1012 1013
        core.ops.matmul(input, mat2, out)
        return out
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

    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
1051
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
1052 1053 1054 1055
    helper.append_op(
        type='matmul', inputs={'X': input,
                               'Y': mat2}, outputs={'Out': out})
    return out
1056

1057

Y
yaoxuefeng 已提交
1058
def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
1059
    """
1060 1061
	:alias_main: paddle.addmm
	:alias: paddle.addmm,paddle.tensor.addmm,paddle.tensor.math.addmm
S
swtkiwi 已提交
1062

1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078
    **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
1079
        alpha (float): Coefficient of $x*y$.
1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094
        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)

1095
            paddle.disable_static()
Y
yaoxuefeng 已提交
1096

1097 1098 1099
            x = paddle.to_tensor(data_x)
            y = paddle.to_tensor(data_y)
            input = paddle.to_tensor(data_input)
Y
yaoxuefeng 已提交
1100 1101 1102 1103

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

            print( out.numpy() )
1104 1105 1106
            # [[10.5 10.5]
            # [10.5 10.5]]
    """
Y
yaoxuefeng 已提交
1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126
    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))



1127 1128 1129 1130
    if in_dygraph_mode():
        out = core.ops.addmm(input, x, y, "Alpha", alpha, "Beta", beta)
        return out

1131 1132 1133 1134
    inputs = {'Input': input, "X": x, "Y": y}
    attrs = {'Alpha': alpha, 'Beta': beta}

    helper = LayerHelper("addmm", **locals())
Y
yaoxuefeng 已提交
1135
    check_variable_and_dtype(input, 'Input', ['float32', 'float64'], 'addmm')
1136 1137 1138 1139 1140 1141 1142
    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
1143 1144


1145
def logsumexp(x, axis=None, keepdim=False, name=None):
1146
    """
1147
    This OP calculates the log of the sum of exponentials of ``x`` along ``axis`` .
1148

1149 1150
    .. math::
       logsumexp(x) = \log\sum exp(x)
1151

1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169
    Args:
        x (Tensor): The input Tensor with data type float32, float64.
        axis (int|list|tuple, optional): The axis along which to perform
            logsumexp calculations. ``axis`` should be int, list(int) or
            tuple(int). If ``axis`` is a list/tuple of dimension(s), logsumexp
            is calculated along all element(s) of ``axis`` . ``axis`` or
            element(s) of ``axis`` should be in range [-D, D), where D is the
            dimensions of ``x`` . If ``axis`` or element(s) of ``axis`` is
            less than 0, it works the same way as :math:`axis + D` . If
            ``axis`` is None, logsumexp is calculated along all elements of
            ``x``. Default is None.
        keepdim (bool, optional): Whether to reserve the reduced dimension(s)
            in the output Tensor. If ``keep_dim`` is True, the dimensions of
            the output Tensor is the same as ``x`` except in the reduced
            dimensions(it is of size 1 in this case). Otherwise, the shape of
            the output Tensor is squeezed in ``axis`` . Default is False.
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.
1170

1171
    Returns:
1172 1173
        Tensor, results of logsumexp along ``axis`` of ``x``, with the same data
        type as ``x``.
1174

1175
    Examples:
1176

1177
    .. code-block:: python
1178

1179 1180
        import paddle

1181
        paddle.disable_static()
1182

1183
        x = paddle.to_tensor([[-1.5, 0., 2.], [3., 1.2, -2.4]])
1184 1185
        out1 = paddle.logsumexp(x) # [3.4691226]
        out2 = paddle.logsumexp(x, 1) # [2.15317821, 3.15684602]
1186 1187

    """
1188 1189 1190 1191 1192 1193 1194
    if isinstance(axis, int):
        axis = [axis]
    reduce_all = True if axis is None \
        or len(axis)==0 \
        or len(axis) == len(x.shape) else False
    if axis is None or len(axis) == 0:
        axis = [0]
1195

1196 1197 1198
    if in_dygraph_mode():
        return core.ops.logsumexp(x, 'dim', axis, 'keep_dim', keepdim,
                                    'reduce_all', reduce_all)
1199

1200 1201 1202
    check_variable_and_dtype(x, 'x',
                             ['float32', 'float64'],
                             'logsumexp')
1203

1204 1205 1206 1207 1208 1209
    helper = LayerHelper('logsumexp', **locals())
    attrs = {'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all}
    out = helper.create_variable_for_type_inference(x.dtype)
    helper.append_op(
        type='logsumexp', inputs={'X': x}, outputs={'Out': out}, attrs=attrs)
    return out
1210

S
swtkiwi 已提交
1211

1212 1213
def inverse(x, name=None):
    """
1214 1215 1216 1217 1218
    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:
1219
        x (Variable): The input tensor. The last two
1220 1221 1222 1223 1224 1225 1226 1227
            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:
1228 1229
        Variable: A Tensor holds the inverse of x. The shape and data type
                        is the same as x.
1230 1231 1232 1233 1234

    Examples:
        .. code-block:: python

            import paddle
1235
            paddle.disable_static()
1236 1237

            mat = paddle.to_tensor([[2, 0], [0, 2]], dtype='float32')
1238 1239
            inv = paddle.inverse(mat)
            print(inv) # [[0.5, 0], [0, 0.5]]
1240 1241 1242

    """
    if in_dygraph_mode():
1243
        return core.ops.inverse(x)
1244

1245 1246
    def _check_input(x):
        check_variable_and_dtype(x, 'x',
1247
                                 ['float32', 'float64'], 'inverse')
1248
        if len(x.shape) < 2:
1249 1250 1251
            raise ValueError(
                "The input of inverse is expected to be a Tensor whose number "
                "of dimensions is no less than 2. But reviced: %d, "
1252 1253
                "x's shape: %s." % (len(x.shape), x.shape))
    _check_input(x)
1254
    helper = LayerHelper('inverse', **locals())
1255
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
1256
    helper.append_op(
1257
        type='inverse', inputs={'Input': [x] }, outputs={'Output': [out]})
1258 1259 1260
    return out


1261
def max(x, axis=None, keepdim=False, name=None):
1262
    """
S
swtkiwi 已提交
1263

1264
    Computes the maximum of tensor elements over the given axis.
1265 1266

    Args:
1267
        x(Tensor): A tensor, the data type is float32,
1268
            float64, int32, int64.
1269
        axis(list|int, optional): The axis along which the maximum is computed.
1270
            If :attr:`None`, compute the maximum over all elements of
1271
             `x` and return a Tensor variable with a single element,
1272 1273 1274
            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
1275
            output Tensor. The result tensor will have one fewer dimension
1276
            than the `x` unless :attr:`keepdim` is true, default
1277
            value is False.
1278
        name(str, optional): The default value is None.  Normally there is no need for
1279 1280 1281
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`

    Returns:
1282
        Tensor, results of maximum on the specified axis of input tensor,
1283
        it's data type is the same as `x`.
1284 1285 1286

    Examples:
        .. code-block:: python
1287

1288
            import paddle
1289

1290 1291 1292 1293
            paddle.disable_static()

            # data_x is a variable with shape [2, 4]
            # the axis is a int element
1294 1295 1296

            x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
                                  [0.1, 0.2, 0.6, 0.7]])
1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
            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 
1313 1314 1315

            y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]],
                                  [[5.0, 6.0], [7.0, 8.0]]])
1316 1317 1318 1319 1320 1321
            result5 = paddle.max(y, axis=[1, 2])
            print(result5.numpy())
            #[4. 8.]
            result6 = paddle.max(y, axis=[0, 1])
            print(result6.numpy())
            #[7. 8.]
1322 1323
    """

1324
    if axis is not None and not isinstance(axis, list):
1325 1326 1327 1328 1329 1330 1331 1332
        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)))

1333 1334 1335 1336 1337
    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)
1338

1339
    helper = LayerHelper('max', **locals())
1340
    check_variable_and_dtype(
1341
        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'max')
1342

1343 1344
    out = helper.create_variable_for_type_inference(
            dtype=helper.input_dtype())
1345 1346
    helper.append_op(
        type='reduce_max',
1347
        inputs={'X': x},
1348 1349
        outputs={'Out': out},
        attrs={
1350 1351
            'dim': axis,
            'keep_dim': keepdim,
1352 1353 1354 1355
            'reduce_all': reduce_all
        })
    return out

1356
def min(x, axis=None, keepdim=False, name=None):
1357
    """
S
swtkiwi 已提交
1358

1359
    Computes the minimum of tensor elements over the given axis
1360

1361
    Args:
1362 1363
        x(Tensor): A tensor, the data type is float32, float64, int32, int64.
        axis(list|int, optional): The axis along which the minimum is computed.
1364
            If :attr:`None`, compute the minimum over all elements of
1365
            `x` and return a Tensor variable with a single element,
1366 1367 1368
            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
1369
            output Tensor. The result tensor will have one fewer dimension
1370
            than the `x` unless :attr:`keepdim` is true, default
1371
            value is False.
W
WuHaobo 已提交
1372
        name(str, optional): The default value is None.  Normally there is no need for 
1373
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
1374

1375
    Returns:
1376
        Tensor, results of minimum on the specified axis of input tensor,
1377
        it's data type is the same as input's Tensor.
1378

1379 1380 1381
    Examples:
        .. code-block:: python

1382
            import paddle
1383

1384
            paddle.disable_static()
1385

1386
            # x is a tensor with shape [2, 4]
1387
            # the axis is a int element
1388 1389
            x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
                                  [0.1, 0.2, 0.6, 0.7]])
1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403
            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]]

1404
            # y is a variable with shape [2, 2, 2]
1405
            # the axis is list 
1406 1407
            y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]],
                                  [[5.0, 6.0], [7.0, 8.0]]])
1408 1409 1410 1411 1412 1413 1414
            result5 = paddle.min(y, axis=[1, 2])
            print(result5.numpy()) 
            #[1. 5.]
            result6 = paddle.min(y, axis=[0, 1])
            print(result6.numpy())
            #[1. 2.]
    """
1415

1416
    if axis is not None and not isinstance(axis, list):
1417 1418 1419 1420 1421 1422 1423
        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)))
1424 1425
    reduce_all = True if axis == None or axis == [] else False
    axis = axis if axis != None and axis != [] else [0]
1426
    if in_dygraph_mode():
1427
        return core.ops.reduce_min(x, 'dim', axis, 'keep_dim', keepdim,
1428
                                   'reduce_all', reduce_all)
1429 1430 1431 1432 1433 1434 1435

    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())
1436 1437
    helper.append_op(
        type='reduce_min',
1438
        inputs={'X': x},
1439 1440
        outputs={'Out': out},
        attrs={
1441 1442
            'dim': axis,
            'keep_dim': keepdim,
1443 1444 1445 1446 1447
            'reduce_all': reduce_all
        })
    return out


W
WuHaobo 已提交
1448
def log1p(x, name=None):
1449
    """
1450 1451
	:alias_main: paddle.log1p
	:alias: paddle.log1p,paddle.tensor.log1p,paddle.tensor.math.log1p
S
swtkiwi 已提交
1452

1453 1454 1455 1456 1457 1458 1459 1460 1461
    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.
1462

1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485
    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
1486
    out = helper.create_variable_for_type_inference(dtype)
1487 1488
    helper.append_op(type="log1p", inputs={"X": x}, outputs={"Out": out})
    return out
B
Bai Yifan 已提交
1489

W
WuHaobo 已提交
1490

W
WuHaobo 已提交
1491
def addcmul(input, tensor1, tensor2, value=1.0, name=None):
B
Bai Yifan 已提交
1492
    """
1493 1494
	:alias_main: paddle.addcmul
	:alias: paddle.addcmul,paddle.tensor.addcmul,paddle.tensor.math.addcmul
S
swtkiwi 已提交
1495

B
Bai Yifan 已提交
1496 1497 1498 1499 1500 1501 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
    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
1529
    out = layers.elementwise_add(input, layers.elementwise_mul(tensor1, tensor2) * value)
B
Bai Yifan 已提交
1530
    return out
1531 1532


Y
Yang Zhang 已提交
1533
def clip(x, min=None, max=None, name=None):
1534
    """
Y
Yang Zhang 已提交
1535 1536
        :alias_main: paddle.clip
        :alias: paddle.clip,paddle.tensor.clip,paddle.tensor.math.clip
S
swtkiwi 已提交
1537

Y
Yang Zhang 已提交
1538
    **clip layer**
1539

Y
Yang Zhang 已提交
1540
    This operator clip all elements in input into the range [ min, max ] and return
1541 1542 1543 1544
    a resulting tensor as the following equation:

    .. math::

1545
        Out = MIN(MAX(x, min), max)
1546 1547

    Args:
Y
Yang Zhang 已提交
1548 1549
        x (Tensor): An N-D Tensor with data type float32 or float64.
        min (float32|Tensor): The lower bound with type ``float32`` or a ``Tensor``
1550
            with shape [1] and type ``int32``, ``float32``, ``float64``.
Y
Yang Zhang 已提交
1551
        max (float32|Tensor): The upper bound with type ``float32`` or a ``Tensor``
1552 1553 1554 1555 1556 1557
            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:
Y
Yang Zhang 已提交
1558
        Tensor: A Tensor with the same data type and data shape as input.
1559 1560 1561 1562 1563 1564

    Examples:
        .. code-block:: python

            import paddle

Y
Yang Zhang 已提交
1565
            paddle.disable_static()
1566
            x1 = paddle.to_tensor([[1.2, 3.5], [4.5, 6.4]], 'float32')
Y
Yang Zhang 已提交
1567 1568 1569 1570 1571 1572 1573 1574
            out1 = paddle.clip(x1, min=3.5, max=5.0)
            out2 = paddle.clip(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]
1575 1576
    """

Y
Yang Zhang 已提交
1577 1578
    fmin = float(np.finfo(np.float32).min)
    fmax = float(np.finfo(np.float32).max)
1579

W
WuHaobo 已提交
1580
    if in_dygraph_mode():
1581 1582 1583 1584
        if isinstance(min, Variable):
            min = min.numpy().item(0)
        if isinstance(max, Variable):
            max = max.numpy().item(0)
Y
Yang Zhang 已提交
1585 1586
        min = fmin if min is None else min
        max = fmax if max is None else max
Y
Yang Zhang 已提交
1587
        return core.ops.clip(x, "min", min, "max", max)
W
WuHaobo 已提交
1588

1589
    if min is not None:
Y
Yang Zhang 已提交
1590
        check_type(min, 'min', (float, int, Variable), 'clip')
1591 1592
        if isinstance(min, Variable):
            check_dtype(min.dtype, 'min', ['float32', 'float64', 'int32'],
Y
Yang Zhang 已提交
1593
                        'clip', '(When the type of min in clip is Variable.)')
1594
    if max is not None:
Y
Yang Zhang 已提交
1595
        check_type(max, 'max', (float, int, Variable), 'clip')
1596 1597
        if isinstance(max, Variable):
            check_dtype(max.dtype, 'max', ['float32', 'float64', 'int32'],
Y
Yang Zhang 已提交
1598
                        'clip', '(When the type of max in clip is Variable.)')
1599

Y
Yang Zhang 已提交
1600
    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'clip')
Y
Yang Zhang 已提交
1601 1602

    inputs = {'X': x}
Y
Yang Zhang 已提交
1603
    attrs = {'min': fmin, 'max': fmax}
1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616

    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

Y
Yang Zhang 已提交
1617
    helper = LayerHelper('clip', **locals())
W
WuHaobo 已提交
1618
    output = helper.create_variable_for_type_inference(
Y
Yang Zhang 已提交
1619
        dtype=helper.input_dtype('x'))
1620 1621 1622 1623
    helper.append_op(
        type='clip', inputs=inputs, outputs={'Out': [output]}, attrs=attrs)

    return output
F
Feiyu Chan 已提交
1624

W
WuHaobo 已提交
1625

1626
def trace(x, offset=0, axis1=0, axis2=1, name=None):
L
Li Fuchen 已提交
1627
    """
1628 1629
	:alias_main: paddle.trace
	:alias: paddle.trace,paddle.tensor.trace,paddle.tensor.math.trace
S
swtkiwi 已提交
1630

1631
    This OP computes the sum along diagonals of the input tensor x.
1632 1633

    If ``x`` is 2D, returns the sum of diagonal.
L
Li Fuchen 已提交
1634

1635
    If ``x`` has larger dimensions, then returns an tensor of diagonals sum, diagonals be taken from
1636
    the 2D planes specified by axis1 and axis2. By default, the 2D planes formed by the first and second axes
1637
    of the input tensor x.
L
Li Fuchen 已提交
1638

1639
    The argument ``offset`` determines where diagonals are taken from input tensor x:
L
Li Fuchen 已提交
1640 1641 1642 1643

    - 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.
1644

L
Li Fuchen 已提交
1645
    Args:
1646 1647 1648 1649
        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
1650 1651 1652 1653 1654 1655 1656 1657 1658 1659
        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
1660

L
Li Fuchen 已提交
1661 1662 1663
            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')
1664

1665
            paddle.disable_static()
1666

1667 1668 1669
            case1 = paddle.to_tensor(case1)
            case2 = paddle.to_tensor(case2)
            case3 = paddle.to_tensor(case3)
1670 1671 1672
            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
1673
    """
1674 1675
    inputs = {'Input': [x]}
    attrs = {'offset': offset, 'axis1': axis1, 'axis2': axis2}
L
Li Fuchen 已提交
1676 1677

    def __check_input(input, offset, dim1, dim2):
1678
        check_dtype(x.dtype, 'Input',
L
Li Fuchen 已提交
1679 1680 1681
                    ['int32', 'int64', 'float16', 'float32', 'float64'],
                    'trace')

1682
        input_shape = list(x.shape)
L
Li Fuchen 已提交
1683
        assert len(input_shape) >= 2,                     \
1684 1685
                "The x must be at least 2-dimensional, "   \
                "But received Input x's dimensional: %s.\n" %  \
L
Li Fuchen 已提交
1686 1687
                len(input_shape)

1688 1689
        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
1690

1691 1692 1693
        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
1694

1695 1696 1697
        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
1698 1699


1700 1701 1702
        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
1703 1704

    if not in_dygraph_mode():
1705
        __check_input(input, offset, axis1, axis2)
L
Li Fuchen 已提交
1706 1707
    helper = LayerHelper('trace', **locals())

1708
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
L
Li Fuchen 已提交
1709 1710 1711

    helper.append_op(
        type='trace',
1712
        inputs={'Input': [x]},
L
Li Fuchen 已提交
1713
        attrs={'offset': offset,
1714 1715
               'axis1': axis1,
               'axis2': axis2},
L
Li Fuchen 已提交
1716 1717 1718
        outputs={'Out': [out]})
    return out

F
Feiyu Chan 已提交
1719
@templatedoc(op_type="kron")
W
WuHaobo 已提交
1720
def kron(x, y, name=None):
S
swtkiwi 已提交
1721
    """
1722 1723
	:alias_main: paddle.kron
	:alias: paddle.kron,paddle.tensor.kron,paddle.tensor.math.kron
S
swtkiwi 已提交
1724 1725

${comment}
F
Feiyu Chan 已提交
1726 1727

    Args:
1728
        x (Variable): the fist operand of kron op, data type: float16, float32,
F
Feiyu Chan 已提交
1729
            float64, int32 or int64.
1730 1731
        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
1732
            with x.
1733 1734
        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
1735 1736 1737 1738 1739 1740 1741
            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
1742

F
Feiyu Chan 已提交
1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772
          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
1773
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
F
Feiyu Chan 已提交
1774 1775
    helper.append_op(type="kron", inputs={"X": x, "Y": y}, outputs={"Out": out})
    return out
1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794


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
1795
            from paddle import to_variable
1796 1797
            import numpy as np

1798
            paddle.disable_static()
1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842
            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)
G
guofei 已提交
1843

J
Jack Zhou 已提交
1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860
def isfinite(x, name=None):
    """

    Return whether every element of input tensor is finite number or not.

    Args:
        x (Tensor): The input tensor, it's data type should be float16, float32, float64, int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        `Tensor`, the bool result which shows every element of `x` whether it is finite number or not.

    Examples:
        .. code-block:: python

            import paddle
            paddle.disable_static()
1861
            x = paddle.to_tensor([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
J
Jack Zhou 已提交
1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889
            out = paddle.tensor.isfinite(x)
            print(out.numpy())  # [False  True  True False  True False False]
    """
    if in_dygraph_mode():
        return core.ops.isfinite_v2(x)
    helper = LayerHelper("isfinite_v2", **locals())
    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isfinite')
    out = helper.create_variable_for_type_inference('bool')
    helper.append_op(type="isfinite_v2", inputs={"X": x}, outputs={"Out": out})
    return out

def isinf(x, name=None):
    """

    Return whether every element of input tensor is `+/-INF` or not.

    Args:
        x (Tensor): The input tensor, it's data type should be float16, float32, float64, int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        `Tensor`, the bool result which shows every element of `x` whether it is `+/-INF` or not.

    Examples:
        .. code-block:: python

            import paddle
            paddle.disable_static()
1890
            x = paddle.to_tensor([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
J
Jack Zhou 已提交
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
            out = paddle.tensor.isinf(x)
            print(out.numpy())  # [ True False False  True False False False]
    """
    if in_dygraph_mode():
        return core.ops.isinf_v2(x)
    helper = LayerHelper("isinf_v2", **locals())
    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isinf')
    out = helper.create_variable_for_type_inference(dtype='bool')
    helper.append_op(type="isinf_v2", inputs={"X": x}, outputs={"Out": out})
    return out

def isnan(x, name=None):
    """

    Return whether every element of input tensor is `NaN` or not.

    Args:
        x (Tensor): The input tensor, it's data type should be float16, float32, float64, int32, int64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        `Tensor`, the bool result which shows every element of `x` whether it is `NaN` or not.

    Examples:
        .. code-block:: python

            import paddle
            paddle.disable_static()
1919
            x = paddle.to_tensor([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
J
Jack Zhou 已提交
1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931
            out = paddle.tensor.isnan(x)
            print(out.numpy())  # [False False False False False  True  True]
    """
    if in_dygraph_mode():
        return core.ops.isnan_v2(x)
    helper = LayerHelper("isnan_v2", **locals())
    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'isnan')
    out = helper.create_variable_for_type_inference(dtype='bool')
    helper.append_op(type="isnan_v2", inputs={"X": x}, outputs={"Out": out})
    return out


G
guofei 已提交
1932 1933 1934 1935 1936
def prod(x, axis=None, keepdim=False, dtype=None, name=None):
    """
    Compute the product of tensor elements over the given axis.

    Args:
1937
        x(Tensor): The input tensor, its data type should be float32, float64, int32, int64.
G
guofei 已提交
1938 1939 1940 1941 1942 1943 1944 1945 1946
        axis(int|list|tuple, optional): The axis along which the product is computed. If :attr:`None`, 
            multiply all elements of `x` and return a Tensor with a single element, 
            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]`. Default is None.
        dtype(str|np.dtype, optional): The desired date type of returned tensor, can be float32, float64, 
            int32, int64. If specified, the input tensor is casted to dtype before operator performed. 
            This is very useful for avoiding data type overflows. The default value is None, the dtype 
            of output is the same as input Tensor `x`.
        keepdim(bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result 
1947
            tensor will have one fewer dimension than the input unless `keepdim` is true. Default is False.
G
guofei 已提交
1948 1949 1950 1951 1952 1953 1954 1955 1956
        name(string, 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:
        Tensor, result of product on the specified dim of input tensor.

    Raises:
        ValueError: The :attr:`dtype` must be float32, float64, int32 or int64.
        TypeError: The type of :attr:`axis` must be int, list or tuple.
J
Jack Zhou 已提交
1957
    
G
guofei 已提交
1958 1959 1960 1961 1962 1963 1964 1965
    Examples:
        .. code-block:: python

            import paddle

            paddle.disable_static()

            # the axis is a int element
1966 1967
            x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
                                  [0.1, 0.2, 0.6, 0.7]])
G
guofei 已提交
1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992
            out1 = paddle.prod(x)
            print(out1.numpy())
            # [0.0002268]

            out2 = paddle.prod(x, -1)
            print(out2.numpy())
            # [0.027  0.0084]

            out3 = paddle.prod(x, 0)
            print(out3.numpy())
            # [0.02 0.06 0.3  0.63]
            print(out3.numpy().dtype)
            # float32

            out4 = paddle.prod(x, 0, keepdim=True)
            print(out4.numpy())
            # [[0.02 0.06 0.3  0.63]]

            out5 = paddle.prod(x, 0, dtype='int64')
            print(out5.numpy())
            # [0 0 0 0]
            print(out5.numpy().dtype)
            # int64

            # the axis is list
1993 1994
            y = paddle.to_tensor([[[1.0, 2.0], [3.0, 4.0]],
                                  [[5.0, 6.0], [7.0, 8.0]]])
G
guofei 已提交
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
            out6 = paddle.prod(y, [0, 1])
            print(out6.numpy())
            # [105. 384.]

            out7 = paddle.prod(y, (1, 2))
            print(out7.numpy())
            # [  24. 1680.]

    """
    if dtype is not None:
        check_dtype(dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'], 'prod')
        if x.dtype != convert_np_dtype_to_dtype_(dtype):
            x = layers.cast(x, dtype)

    return layers.reduce_prod(input=x, dim=axis, keep_dim=keepdim, name=name)
W
WangXi 已提交
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029


def sign(x, name=None):
    """
    This OP returns sign of every element in `x`: 1 for positive, -1 for negative and 0 for zero.

    Args:
        x(Tensor): The input tensor. The data type can be float16, float32 or 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:
        Tensor: The output sign tensor with identical shape and data type to the input :attr:`x`.

    Examples:
        .. code-block:: python

          import paddle

          paddle.disable_static()
2030
          x = paddle.to_tensor([3.0, 0.0, -2.0, 1.7], dtype='float32')
W
WangXi 已提交
2031 2032 2033 2034 2035 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
          out = paddle.sign(x=x)
          print(out)  # [1.0, 0.0, -1.0, 1.0]
    """
    if in_dygraph_mode():
        return core.ops.sign(x)

    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'sign')
    helper = LayerHelper("sign", **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)

    helper.append_op(type='sign', inputs={'X': [x]}, outputs={'Out': [out]})

    return out


def tanh(x, name=None):
    """
    Tanh Activation Operator.

    .. math::
        out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}

    Args:
        x (Tensor): Input of Tanh operator, an N-D Tensor, with data type float32, float64 or float16.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Output of Tanh operator, a Tensor with same data type and shape as input.

    Examples:

        .. code-block:: python

            import paddle

            paddle.disable_static()
2067
            x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
W
WangXi 已提交
2068 2069 2070 2071 2072 2073 2074 2075
            out = paddle.tanh(x)
            print(out.numpy())
            # [-0.37994896 -0.19737532  0.09966799  0.29131261]
    """
    if in_dygraph_mode():
        return core.ops.tanh(x)

    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'tanh')
S
ShenLiang 已提交
2076
    check_type(x, 'x', (Variable), 'tanh')
W
WangXi 已提交
2077 2078 2079 2080
    helper = LayerHelper('tanh', **locals())
    out = helper.create_variable_for_type_inference(x.dtype)
    helper.append_op(type='tanh', inputs={'X': x}, outputs={'Out': out})
    return out