math.py 78.2 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 21
from paddle.tensor import cast
import paddle
22
from ..fluid import layers
L
Li Fuchen 已提交
23 24 25
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
26
from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
27
import sys
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

            import paddle
177
            import numpy as np
178

179 180 181 182 183 184 185 186 187 188 189 190 191
            paddle.disable_static()
            
            # example 1: y is a float
            x_data = np.array([1, 2, 3])
            y = 2
            x = paddle.to_tensor(x_data)
            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]
192 193

    """
194
    # in dynamic graph mode
W
WuHaobo 已提交
195
    if in_dygraph_mode():
196 197 198
        if isinstance(y, (int, float)):
            return core.ops.pow(x, 'factor', y)
        elif isinstance(y, (paddle.Tensor, Variable)):
199

200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232
            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)))
233 234 235



236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255
@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)

256 257
    out = helper.kwargs.get('out', None)

258 259 260 261 262 263 264 265 266 267 268 269
    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)
270 271 272 273 274 275

    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)
276 277 278 279 280 281 282 283 284 285 286

    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
287
def add(x, y, name=None):
288 289 290 291 292 293 294 295
    """
Examples:

    ..  code-block:: python

        import paddle
        import numpy as np

Y
Yang Zhang 已提交
296 297 298 299 300
        paddle.disable_static()
        np_x = np.array([2, 3, 4]).astype('float64')
        np_y = np.array([1, 5, 2]).astype('float64')
        x = paddle.to_variable(np_x)
        y = paddle.to_variable(np_y)
W
WuHaobo 已提交
301
        z = paddle.add(x, y)
Y
Yang Zhang 已提交
302 303
        np_z = z.numpy()
        print(np_z)  # [3., 8., 6. ]
304 305 306 307 308 309

    """
    op_type = 'elementwise_add'
    axis = -1
    if in_dygraph_mode():
        return _elementwise_op_in_dygraph(
Y
Yang Zhang 已提交
310
            x, y, axis=axis, op_name=op_type)
311 312 313 314

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


315
def divide(x, y, name=None):
316
    """
317
    Divide two tensors element-wise. The equation is:
318

319 320
    .. math::
        out = x / y
321

322 323
    **Note**:
    ``paddle.divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
324

325 326 327 328
    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`.
329

330 331
    Returns:
        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
332

333
    Examples:
334

335
        ..  code-block:: python
336

337 338
            import paddle
            import numpy as np
339

340
            paddle.disable_static()
341

342 343 344 345 346 347
            np_x = np.array([2, 3, 4]).astype('float64')
            np_y = np.array([1, 5, 2]).astype('float64')
            x = paddle.to_tensor(np_x)
            y = paddle.to_tensor(np_y)
            z = paddle.divide(x, y)
            print(z.numpy())  # [2., 0.6, 2.]
348

349 350 351 352 353
    """
    op_type = 'elementwise_div'
    axis = -1
    act = None
    if in_dygraph_mode():
354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383
        # 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)

384 385
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, act=act, op_name=op_type)
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
    # 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)

417
    return _elementwise_op(LayerHelper(op_type, **locals()))
418 419


420 421 422
def floor_divide(x, y, name=None):
    """
    Floor divide two tensors element-wise. The equation is:
423

424 425
    .. math::
        out = x // y
426

427 428
    **Note**:
    ``paddle.floor_divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
429

430 431 432 433
    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`.
434

435 436
    Returns:
        N-D Tensor. A location into which the result is stored. It's dimension equals with $x$.
437

438
    Examples:
439

440
        ..  code-block:: python
441

442 443
            import paddle
            import numpy as np
444

445
            paddle.disable_static()
446

447 448 449 450 451 452
            np_x = np.array([2, 3, 8, 7])
            np_y = np.array([1, 5, 3, 3])
            x = paddle.to_tensor(np_x)
            y = paddle.to_tensor(np_y)
            z = paddle.floor_divide(x, y)
            print(z.numpy())  # [2, 0, 2, 2]
453

454 455 456 457
    """
    op_type = 'elementwise_floordiv'
    axis = -1
    if in_dygraph_mode():
458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
        # 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)

481 482
        return _elementwise_op_in_dygraph(
            x, y, axis=axis, op_name=op_type)
483

484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506
    # 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)

507
    return _elementwise_op(LayerHelper(op_type, **locals()))
508 509


510
def remainder(x, y, name=None):
511
    """
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
    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
            import numpy as np

            paddle.disable_static()

            np_x = np.array([2, 3, 8, 7])
            np_y = np.array([1, 5, 3, 3])
            x = paddle.to_tensor(np_x)
            y = paddle.to_tensor(np_y)
            z = paddle.remainder(x, y)
            print(z.numpy())  # [0, 3, 2, 1]

    """
    op_type = 'elementwise_mod'
546 547
    axis = -1
    if in_dygraph_mode():
548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564
        # 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)

565
        return _elementwise_op_in_dygraph(
566
            x, y, axis=axis, op_name=op_type)
567

568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584
    # 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)

585 586 587
    return _elementwise_op(LayerHelper(op_type, **locals()))


588 589 590 591
mod = remainder  #DEFINE_ALIAS
floor_mod = remainder  #DEFINE_ALIAS


592 593
def multiply(x, y, axis=-1, name=None):
    """
594
    multiply two tensors element-wise. The equation is:
595

596 597
    .. math::
        out = x * y
598

599 600
    **Note**:
    ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
601

602 603 604 605
    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`.
606

607 608
    Returns:
        N-D Tensor. A location into which the result is stored. Its dimension equals with $x$.
609

610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630
    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_tensor(x_data)
            y = paddle.to_tensor(y_data)
            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)
            x = paddle.to_tensor(x_data)
            y = paddle.to_tensor(y_data)
            res = paddle.multiply(x, y, axis=1)
            print(res.numpy()) # [[[1, 2, 3], [2, 4, 6]]]
631 632 633 634

    """
    op_type = 'elementwise_mul'
    act = None
635 636 637 638 639
    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))

640 641 642 643 644 645
    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()))

646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747
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()))
748

749 750
for func in [
        add,
751 752 753
        maximum,
        minimum,
        multiply
754
]:
755
    proto_dict = {'add': 'elementwise_add', 'div': 'elementwise_div', 'maximum': 'elementwise_max', 'minimum': 'elementwise_min', 'multiply': 'elementwise_mul'}
756 757
    op_proto = OpProtoHolder.instance().get_op_proto(proto_dict[func.__name__])

Y
Yang Zhang 已提交
758 759 760 761 762 763 764
    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_(
765 766
        op_proto,
        additional_args_lines=additional_args_lines,
767
        skip_attrs_set={"x_data_format", "y_data_format", "axis",
768
            "use_quantizer", "mkldnn_data_type", "Scale_x", "Scale_y", "Scale_out"
769
        }) + """\n""" + str(func.__doc__)
770

Y
Yang Zhang 已提交
771

772
def sum(x, axis=None, dtype=None, keepdim=False, name=None):
773 774 775 776
    """
    Computes the sum of tensor elements over the given dimension.

    Args:
777 778 779
        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
780
            Tensor variable with a single element, otherwise must be in the
781 782 783 784 785 786 787
            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
788
            value is False.
789
        name (str, optional): The default value is None. Normally there is no need for
790 791 792
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`

    Returns:
793 794
        Tensor: Results of summation operation on the specified axis of input Tensor `x`,
        it's data type is the same as `x`.
795 796

    Raises:
797 798
        ValueError: The :attr:`dtype` must be float64 or int64.
        TypeError: The type of :attr:`axis` must be int, list or tuple.
799

800 801 802
    Examples:
        .. code-block:: python

803
            import numpy as np
804
            import paddle
805 806
            paddle.disable_static()

807 808 809 810
            # 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.
811 812
            x_data = np.array([[0.2, 0.3, 0.5, 0.9],[0.1, 0.2, 0.6, 0.7]]).astype('float32')
            x = paddle.to_variable(x_data)
813
            out1 = paddle.sum(x)  # [3.5]
814 815 816
            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]]
817 818 819 820 821

            # 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.
822 823 824 825
            y_data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]).astype('float32')
            y = paddle.to_variable(y_data)
            out5 = paddle.sum(y, axis=[1, 2]) # [10, 26]
            out6 = paddle.sum(y, axis=[0, 1]) # [16, 20]
826
    """
827 828 829 830 831 832 833 834 835 836 837
    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

838
    attrs = {
839 840 841
        'dim': axis if axis != None and axis != [] and axis != () else [0],
        'keep_dim': keepdim,
        'reduce_all': reduce_all_flag
842 843 844 845
    }
    dtype_flag = False
    if dtype is not None:
        if dtype in ['float64', 'int64']:
846 847
            if (convert_dtype(x.dtype) == "float32" and dtype == "float64") or \
               (convert_dtype(x.dtype) == "int32" and dtype == "int64"):
848
                attrs.update({
849
                    'in_dtype': x.dtype,
850 851 852 853 854 855 856 857 858
                    '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():
859
        axis = axis if axis != None and axis != [] else [0]
860
        if dtype_flag:
861 862 863
            return core.ops.reduce_sum(x, 'dim', axis, 'keep_dim', keepdim,
                                       'reduce_all', reduce_all_flag, 'in_dtype',
                                       x.dtype, 'out_dtype',
864 865
                                       convert_np_dtype_to_dtype_(dtype))
        else:
866 867
            return core.ops.reduce_sum(x, 'dim', axis, 'keep_dim', keepdim,
                                       'reduce_all', reduce_all_flag)
868
    check_variable_and_dtype(
869 870 871
        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'sum')
    check_type(axis, 'axis', (int, list, tuple, type(None)), 'sum')

872 873 874 875 876
    helper = LayerHelper('sum', **locals())
    if dtype_flag:
        out = helper.create_variable_for_type_inference(
            dtype=convert_np_dtype_to_dtype_(dtype))
    else:
877
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
878 879
    helper.append_op(
        type='reduce_sum',
880
        inputs={'X': x},
881 882 883
        outputs={'Out': out},
        attrs=attrs)
    return out
884

885

886 887 888
@templatedoc(op_type="sum")
def elementwise_sum(inputs, name=None):
    """
889 890
	:alias_main: paddle.elementwise_sum
	:alias: paddle.elementwise_sum,paddle.tensor.elementwise_sum,paddle.tensor.math.elementwise_sum
S
swtkiwi 已提交
891

892
    ${comment}
893

894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924
    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:
925 926
        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.
927 928 929 930
        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:
931
        Variable: the sum of input :math:`inputs` . its shape and data types are consistent with :math:`inputs` .
932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956

    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.
957 958
            # 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,
959 960 961 962
            #       and '__int64' on Windows. They both represent 64-bit integer variables.
    """

    helper = LayerHelper('elementwise_sum', **locals())
963 964 965 966 967 968 969 970 971 972 973
    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')


974 975 976 977 978 979 980 981 982 983 984
    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
985
def mm(input, mat2, name=None):
986
    """
987 988
	:alias_main: paddle.mm
	:alias: paddle.mm,paddle.tensor.mm,paddle.tensor.math.mm
S
swtkiwi 已提交
989

990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037
    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
1038
        out = _varbase_creator(dtype=input.dtype)
1039 1040
        core.ops.matmul(input, mat2, out)
        return out
1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077

    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
1078
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
1079 1080 1081 1082
    helper.append_op(
        type='matmul', inputs={'X': input,
                               'Y': mat2}, outputs={'Out': out})
    return out
1083

1084

Y
yaoxuefeng 已提交
1085
def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
1086
    """
1087 1088
	:alias_main: paddle.addmm
	:alias: paddle.addmm,paddle.tensor.addmm,paddle.tensor.math.addmm
S
swtkiwi 已提交
1089

1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105
    **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
1106
        alpha (float): Coefficient of $x*y$.
1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121
        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)

1122
            paddle.disable_static()
Y
yaoxuefeng 已提交
1123

1124 1125 1126
            x = paddle.to_variable(data_x)
            y = paddle.to_variable(data_y)
            input = paddle.to_variable(data_input)
Y
yaoxuefeng 已提交
1127 1128 1129 1130

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

            print( out.numpy() )
1131 1132 1133
            # [[10.5 10.5]
            # [10.5 10.5]]
    """
Y
yaoxuefeng 已提交
1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153
    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))



1154 1155 1156 1157
    if in_dygraph_mode():
        out = core.ops.addmm(input, x, y, "Alpha", alpha, "Beta", beta)
        return out

1158 1159 1160 1161
    inputs = {'Input': input, "X": x, "Y": y}
    attrs = {'Alpha': alpha, 'Beta': beta}

    helper = LayerHelper("addmm", **locals())
Y
yaoxuefeng 已提交
1162
    check_variable_and_dtype(input, 'Input', ['float32', 'float64'], 'addmm')
1163 1164 1165 1166 1167 1168 1169
    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
1170 1171


1172
def logsumexp(x, axis=None, keepdim=False, name=None):
1173
    """
1174
    This OP calculates the log of the sum of exponentials of ``x`` along ``axis`` .
1175

1176 1177
    .. math::
       logsumexp(x) = \log\sum exp(x)
1178

1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196
    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`.
1197

1198
    Returns:
1199 1200
        Tensor, results of logsumexp along ``axis`` of ``x``, with the same data
        type as ``x``.
1201

1202
    Examples:
1203

1204
    .. code-block:: python
1205

1206 1207 1208
        import paddle
        import numpy as np

1209
        paddle.disable_static()
1210

1211 1212 1213 1214
        x = np.array([[-1.5, 0., 2.], [3., 1.2, -2.4]])
        x = paddle.to_tensor(x)
        out1 = paddle.logsumexp(x) # [3.4691226]
        out2 = paddle.logsumexp(x, 1) # [2.15317821, 3.15684602]
1215 1216

    """
1217 1218 1219 1220 1221 1222 1223
    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]
1224

1225 1226 1227
    if in_dygraph_mode():
        return core.ops.logsumexp(x, 'dim', axis, 'keep_dim', keepdim,
                                    'reduce_all', reduce_all)
1228

1229 1230 1231
    check_variable_and_dtype(x, 'x',
                             ['float32', 'float64'],
                             'logsumexp')
1232

1233 1234 1235 1236 1237 1238
    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
1239

S
swtkiwi 已提交
1240

1241 1242
def inverse(x, name=None):
    """
1243 1244 1245 1246 1247
    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:
1248
        x (Variable): The input tensor. The last two
1249 1250 1251 1252 1253 1254 1255 1256
            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:
1257 1258
        Variable: A Tensor holds the inverse of x. The shape and data type
                        is the same as x.
1259 1260 1261 1262 1263 1264 1265 1266

    Examples:
        .. code-block:: python

            import numpy as np
            import paddle

            mat_np = np.array([[2, 0], [0, 2]]).astype("float32")
1267 1268 1269 1270
            paddle.disable_static()
            mat = paddle.to_variable(mat_np)
            inv = paddle.inverse(mat)
            print(inv) # [[0.5, 0], [0, 0.5]]
1271 1272 1273

    """
    if in_dygraph_mode():
1274
        return core.ops.inverse(x)
1275

1276 1277
    def _check_input(x):
        check_variable_and_dtype(x, 'x',
1278
                                 ['float32', 'float64'], 'inverse')
1279
        if len(x.shape) < 2:
1280 1281 1282
            raise ValueError(
                "The input of inverse is expected to be a Tensor whose number "
                "of dimensions is no less than 2. But reviced: %d, "
1283 1284
                "x's shape: %s." % (len(x.shape), x.shape))
    _check_input(x)
1285
    helper = LayerHelper('inverse', **locals())
1286
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
1287
    helper.append_op(
1288
        type='inverse', inputs={'Input': [x] }, outputs={'Output': [out]})
1289 1290 1291
    return out


1292
def max(x, axis=None, keepdim=False, name=None):
1293
    """
S
swtkiwi 已提交
1294

1295
    Computes the maximum of tensor elements over the given axis.
1296 1297

    Args:
1298
        x(Tensor): A tensor, the data type is float32,
1299
            float64, int32, int64.
1300
        axis(list|int, optional): The axis along which the maximum is computed.
1301
            If :attr:`None`, compute the maximum over all elements of
1302
             `x` and return a Tensor variable with a single element,
1303 1304 1305
            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
1306
            output Tensor. The result tensor will have one fewer dimension
1307
            than the `x` unless :attr:`keepdim` is true, default
1308
            value is False.
1309
        name(str, optional): The default value is None.  Normally there is no need for
1310 1311 1312
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`

    Returns:
1313
        Tensor, results of maximum on the specified axis of input tensor,
1314
        it's data type is the same as `x`.
1315 1316 1317

    Examples:
        .. code-block:: python
1318 1319

            import numpy as np
1320
            import paddle
1321

1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353
            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.]
1354 1355
    """

1356
    if axis is not None and not isinstance(axis, list):
1357 1358 1359 1360 1361 1362 1363 1364
        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)))

1365 1366 1367 1368 1369
    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)
1370

1371
    helper = LayerHelper('max', **locals())
1372
    check_variable_and_dtype(
1373
        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'max')
1374

1375 1376
    out = helper.create_variable_for_type_inference(
            dtype=helper.input_dtype())
1377 1378
    helper.append_op(
        type='reduce_max',
1379
        inputs={'X': x},
1380 1381
        outputs={'Out': out},
        attrs={
1382 1383
            'dim': axis,
            'keep_dim': keepdim,
1384 1385 1386 1387
            'reduce_all': reduce_all
        })
    return out

1388
def min(x, axis=None, keepdim=False, name=None):
1389
    """
S
swtkiwi 已提交
1390

1391
    Computes the minimum of tensor elements over the given axis
1392

1393
    Args:
1394 1395
        x(Tensor): A tensor, the data type is float32, float64, int32, int64.
        axis(list|int, optional): The axis along which the minimum is computed.
1396
            If :attr:`None`, compute the minimum over all elements of
1397
            `x` and return a Tensor variable with a single element,
1398 1399 1400
            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
1401
            output Tensor. The result tensor will have one fewer dimension
1402
            than the `x` unless :attr:`keepdim` is true, default
1403
            value is False.
W
WuHaobo 已提交
1404
        name(str, optional): The default value is None.  Normally there is no need for 
1405
            user to set this property.  For more information, please refer to :ref:`api_guide_Name`
1406

1407
    Returns:
1408
        Tensor, results of minimum on the specified axis of input tensor,
1409
        it's data type is the same as input's Tensor.
1410

1411 1412 1413
    Examples:
        .. code-block:: python

1414 1415
            import numpy as np
            import paddle
1416

1417
            paddle.disable_static()
1418

1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449
            # 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.]
    """
1450

1451
    if axis is not None and not isinstance(axis, list):
1452 1453 1454 1455 1456 1457 1458
        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)))
1459 1460
    reduce_all = True if axis == None or axis == [] else False
    axis = axis if axis != None and axis != [] else [0]
1461
    if in_dygraph_mode():
1462
        return core.ops.reduce_min(x, 'dim', axis, 'keep_dim', keepdim,
1463
                                   'reduce_all', reduce_all)
1464 1465 1466 1467 1468 1469 1470

    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())
1471 1472
    helper.append_op(
        type='reduce_min',
1473
        inputs={'X': x},
1474 1475
        outputs={'Out': out},
        attrs={
1476 1477
            'dim': axis,
            'keep_dim': keepdim,
1478 1479 1480 1481 1482
            'reduce_all': reduce_all
        })
    return out


W
WuHaobo 已提交
1483
def log1p(x, name=None):
1484
    """
1485 1486
	:alias_main: paddle.log1p
	:alias: paddle.log1p,paddle.tensor.log1p,paddle.tensor.math.log1p
S
swtkiwi 已提交
1487

1488 1489 1490 1491 1492 1493 1494 1495 1496
    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.
1497

1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
    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
1521
    out = helper.create_variable_for_type_inference(dtype)
1522 1523
    helper.append_op(type="log1p", inputs={"X": x}, outputs={"Out": out})
    return out
B
Bai Yifan 已提交
1524

W
WuHaobo 已提交
1525

W
WuHaobo 已提交
1526
def addcmul(input, tensor1, tensor2, value=1.0, name=None):
B
Bai Yifan 已提交
1527
    """
1528 1529
	:alias_main: paddle.addcmul
	:alias: paddle.addcmul,paddle.tensor.addcmul,paddle.tensor.math.addcmul
S
swtkiwi 已提交
1530

B
Bai Yifan 已提交
1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563
    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
1564
    out = layers.elementwise_add(input, layers.elementwise_mul(tensor1, tensor2) * value)
B
Bai Yifan 已提交
1565
    return out
1566 1567


Y
Yang Zhang 已提交
1568
def clip(x, min=None, max=None, name=None):
1569
    """
Y
Yang Zhang 已提交
1570 1571
        :alias_main: paddle.clip
        :alias: paddle.clip,paddle.tensor.clip,paddle.tensor.math.clip
S
swtkiwi 已提交
1572

Y
Yang Zhang 已提交
1573
    **clip layer**
1574

Y
Yang Zhang 已提交
1575
    This operator clip all elements in input into the range [ min, max ] and return
1576 1577 1578 1579
    a resulting tensor as the following equation:

    .. math::

1580
        Out = MIN(MAX(x, min), max)
1581 1582

    Args:
Y
Yang Zhang 已提交
1583 1584
        x (Tensor): An N-D Tensor with data type float32 or float64.
        min (float32|Tensor): The lower bound with type ``float32`` or a ``Tensor``
1585
            with shape [1] and type ``int32``, ``float32``, ``float64``.
Y
Yang Zhang 已提交
1586
        max (float32|Tensor): The upper bound with type ``float32`` or a ``Tensor``
1587 1588 1589 1590 1591 1592
            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
1593
        Tensor: A Tensor with the same data type and data shape as input.
1594 1595 1596 1597 1598 1599 1600

    Examples:
        .. code-block:: python

            import paddle
            import numpy as np

Y
Yang Zhang 已提交
1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611
            paddle.disable_static()
            x = np.array([[1.2,3.5], [4.5,6.4]]).astype('float32')
            x1 = paddle.to_variable(x)
            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]
1612 1613 1614 1615
    """

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

W
WuHaobo 已提交
1616 1617 1618
    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
Y
Yang Zhang 已提交
1619
        return core.ops.clip(x, "min", min, "max", max)
W
WuHaobo 已提交
1620

1621
    if min is not None:
Y
Yang Zhang 已提交
1622
        check_type(min, 'min', (float, int, Variable), 'clip')
1623 1624
        if isinstance(min, Variable):
            check_dtype(min.dtype, 'min', ['float32', 'float64', 'int32'],
Y
Yang Zhang 已提交
1625
                        'clip', '(When the type of min in clip is Variable.)')
1626
    if max is not None:
Y
Yang Zhang 已提交
1627
        check_type(max, 'max', (float, int, Variable), 'clip')
1628 1629
        if isinstance(max, Variable):
            check_dtype(max.dtype, 'max', ['float32', 'float64', 'int32'],
Y
Yang Zhang 已提交
1630
                        'clip', '(When the type of max in clip is Variable.)')
1631

Y
Yang Zhang 已提交
1632 1633 1634
    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'clip')

    inputs = {'X': x}
1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
    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

Y
Yang Zhang 已提交
1649
    helper = LayerHelper('clip', **locals())
W
WuHaobo 已提交
1650
    output = helper.create_variable_for_type_inference(
Y
Yang Zhang 已提交
1651
        dtype=helper.input_dtype())
1652 1653 1654 1655
    helper.append_op(
        type='clip', inputs=inputs, outputs={'Out': [output]}, attrs=attrs)

    return output
F
Feiyu Chan 已提交
1656

W
WuHaobo 已提交
1657

1658
def trace(x, offset=0, axis1=0, axis2=1, name=None):
L
Li Fuchen 已提交
1659
    """
1660 1661
	:alias_main: paddle.trace
	:alias: paddle.trace,paddle.tensor.trace,paddle.tensor.math.trace
S
swtkiwi 已提交
1662

1663
    This OP computes the sum along diagonals of the input tensor x.
1664 1665

    If ``x`` is 2D, returns the sum of diagonal.
L
Li Fuchen 已提交
1666

1667
    If ``x`` has larger dimensions, then returns an tensor of diagonals sum, diagonals be taken from
1668
    the 2D planes specified by axis1 and axis2. By default, the 2D planes formed by the first and second axes
1669
    of the input tensor x.
L
Li Fuchen 已提交
1670

1671
    The argument ``offset`` determines where diagonals are taken from input tensor x:
L
Li Fuchen 已提交
1672 1673 1674 1675

    - 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.
1676

L
Li Fuchen 已提交
1677
    Args:
1678 1679 1680 1681
        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
1682 1683 1684 1685 1686 1687 1688 1689 1690 1691
        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
1692

L
Li Fuchen 已提交
1693 1694 1695
            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')
1696

1697
            paddle.disable_static()
1698

1699 1700 1701
            case1 = paddle.to_variable(case1)
            case2 = paddle.to_variable(case2)
            case3 = paddle.to_variable(case3)
1702 1703 1704
            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
1705
    """
1706 1707
    inputs = {'Input': [x]}
    attrs = {'offset': offset, 'axis1': axis1, 'axis2': axis2}
L
Li Fuchen 已提交
1708 1709

    def __check_input(input, offset, dim1, dim2):
1710
        check_dtype(x.dtype, 'Input',
L
Li Fuchen 已提交
1711 1712 1713
                    ['int32', 'int64', 'float16', 'float32', 'float64'],
                    'trace')

1714
        input_shape = list(x.shape)
L
Li Fuchen 已提交
1715
        assert len(input_shape) >= 2,                     \
1716 1717
                "The x must be at least 2-dimensional, "   \
                "But received Input x's dimensional: %s.\n" %  \
L
Li Fuchen 已提交
1718 1719
                len(input_shape)

1720 1721
        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
1722

1723 1724 1725
        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
1726

1727 1728 1729
        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
1730 1731


1732 1733 1734
        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
1735 1736

    if not in_dygraph_mode():
1737
        __check_input(input, offset, axis1, axis2)
L
Li Fuchen 已提交
1738 1739
    helper = LayerHelper('trace', **locals())

1740
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
L
Li Fuchen 已提交
1741 1742 1743

    helper.append_op(
        type='trace',
1744
        inputs={'Input': [x]},
L
Li Fuchen 已提交
1745
        attrs={'offset': offset,
1746 1747
               'axis1': axis1,
               'axis2': axis2},
L
Li Fuchen 已提交
1748 1749 1750
        outputs={'Out': [out]})
    return out

F
Feiyu Chan 已提交
1751
@templatedoc(op_type="kron")
W
WuHaobo 已提交
1752
def kron(x, y, name=None):
S
swtkiwi 已提交
1753
    """
1754 1755
	:alias_main: paddle.kron
	:alias: paddle.kron,paddle.tensor.kron,paddle.tensor.math.kron
S
swtkiwi 已提交
1756 1757

${comment}
F
Feiyu Chan 已提交
1758 1759

    Args:
1760
        x (Variable): the fist operand of kron op, data type: float16, float32,
F
Feiyu Chan 已提交
1761
            float64, int32 or int64.
1762 1763
        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
1764
            with x.
1765 1766
        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
1767 1768 1769 1770 1771 1772 1773
            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
1774

F
Feiyu Chan 已提交
1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804
          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
1805
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
F
Feiyu Chan 已提交
1806 1807
    helper.append_op(type="kron", inputs={"X": x, "Y": y}, outputs={"Out": out})
    return out
1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826


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
1827
            from paddle import to_variable
1828 1829
            import numpy as np

1830
            paddle.disable_static()
1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874
            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
1875

J
Jack Zhou 已提交
1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969
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
            import numpy as np
            paddle.disable_static()
            x_np = np.array([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
            x = paddle.to_tensor(x_np)
            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
            import numpy as np
            paddle.disable_static()
            x_np = np.array([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
            x = paddle.to_tensor(x_np)
            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
            import numpy as np
            paddle.disable_static()
            x_np = np.array([float('-inf'), -2, 3.6, float('inf'), 0, float('-nan'), float('nan')])
            x = paddle.to_tensor(x_np)
            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
1970 1971 1972 1973 1974
def prod(x, axis=None, keepdim=False, dtype=None, name=None):
    """
    Compute the product of tensor elements over the given axis.

    Args:
1975
        x(Tensor): The input tensor, its data type should be float32, float64, int32, int64.
G
guofei 已提交
1976 1977 1978 1979 1980 1981 1982 1983 1984
        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 
1985
            tensor will have one fewer dimension than the input unless `keepdim` is true. Default is False.
G
guofei 已提交
1986 1987 1988 1989 1990 1991 1992 1993 1994
        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
1995
    
G
guofei 已提交
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050
    Examples:
        .. code-block:: python

            import paddle
            import numpy as np

            paddle.disable_static()

            # 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]]).astype(np.float32)
            x = paddle.to_tensor(data_x)
            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
            data_y = np.array([[[1.0, 2.0], [3.0, 4.0]],
                               [[5.0, 6.0], [7.0, 8.0]]])
            y = paddle.to_tensor(data_y)
            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
2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121


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 numpy as np
          import paddle

          data = np.array([3.0, 0.0, -2.0, 1.7], dtype='float32')
          paddle.disable_static()
          x = paddle.to_tensor(data)
          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
            import numpy as np

            paddle.disable_static()

            x_data = np.array([-0.4, -0.2, 0.1, 0.3])
            x = paddle.to_tensor(x_data)
            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
2122
    check_type(x, 'x', (Variable), 'tanh')
W
WangXi 已提交
2123 2124 2125 2126
    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