random.py 40.0 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# TODO: define random functions  
S
silingtong123 已提交
16

C
cc 已提交
17
from ..fluid import core
J
JYChen 已提交
18
from ..fluid.framework import in_dygraph_mode, Variable, convert_np_dtype_to_dtype_, dygraph_only
C
cc 已提交
19
from ..fluid.layer_helper import LayerHelper
20
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, check_shape
21 22
from ..fluid.layers import utils
import paddle
W
wanghuancoder 已提交
23
from paddle import _C_ops
S
silingtong123 已提交
24

25 26
__all__ = []

S
silingtong123 已提交
27

L
Leo Chen 已提交
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
def bernoulli(x, name=None):
    """

    This OP returns a Tensor filled with random binary(0 or 1) number from a Bernoulli distribution.
    The input ``x`` is a tensor with probabilities for generating the random binary number.
    Each element in ``x`` should be in [0, 1], and the out is generated by:
    
    .. math::

        out_i ~ Bernoulli (x_i)

    Args:
        x(Tensor):  A tensor with probabilities for generating the random binary number. The data type 
            should be 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: 
        Tensor: A Tensor filled with random binary number with the same shape and dtype as ``x``.

    Examples:
        .. code-block:: python

51
            import paddle
L
Leo Chen 已提交
52

L
Leo Chen 已提交
53 54 55
            paddle.set_device('cpu')  # on CPU device
            paddle.seed(100) 

56
            x = paddle.rand([2,3])
L
Leo Chen 已提交
57 58 59
            print(x)
            # [[0.55355281, 0.20714243, 0.01162981],
            #  [0.51577556, 0.36369765, 0.26091650]]
L
Leo Chen 已提交
60

61
            out = paddle.bernoulli(x)
L
Leo Chen 已提交
62 63 64
            print(out)
            # [[1., 0., 1.],
            #  [0., 1., 0.]]
L
Leo Chen 已提交
65 66 67 68

    """

    if in_dygraph_mode():
W
wanghuancoder 已提交
69
        return _C_ops.bernoulli(x)
L
Leo Chen 已提交
70 71 72 73 74 75 76 77

    check_variable_and_dtype(x, "x", ["float32", "float64"], "bernoulli")

    helper = LayerHelper("randint", **locals())
    out = helper.create_variable_for_type_inference(
        dtype=x.dtype)  # maybe set out to int32 ? 
    helper.append_op(
        type='bernoulli', inputs={"X": x}, outputs={'Out': out}, attrs={})
78
    out.stop_gradient = True
L
Leo Chen 已提交
79 80 81
    return out


82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
def poisson(x, name=None):
    """
    This OP returns a tensor filled with random number from a Poisson Distribution.

    .. math::

        out_i ~ Poisson (x_i)

    Args:
        x(Tensor):  A tensor with rate parameter of poisson Distribution. The data type 
            should be 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: 
        Tensor: A Tensor filled with random number with the same shape and dtype as ``x``.

    Examples:
        .. code-block:: python

            import paddle
103
            paddle.set_device('cpu')
104 105 106 107
            paddle.seed(2021)

            x = paddle.uniform([2,3], min=1.0, max=5.0)
            out = paddle.poisson(x)
108 109
            # [[2., 1., 4.],
            #  [4., 5., 1.]]
110 111 112 113 114 115 116 117 118 119 120 121 122 123 124

    """

    if in_dygraph_mode():
        return _C_ops.poisson(x)

    check_variable_and_dtype(x, "x", ["float32", "float64"], "poisson")

    helper = LayerHelper("poisson", **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='poisson', inputs={'X': x}, outputs={'Out': out}, attrs={})
    return out


P
pangyoki 已提交
125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146
def multinomial(x, num_samples=1, replacement=False, name=None):
    """
    This OP returns a Tensor filled with random values sampled from a Multinomical
    distribution. The input ``x`` is a tensor with probabilities for generating the
    random number. Each element in ``x`` should be larger or equal to 0, but not all
    0. ``replacement`` indicates whether it is a replaceable sample. If ``replacement``
    is True, a category can be sampled more than once.

    Args:
        x(Tensor):  A tensor with probabilities for generating the random number. The data type
            should be float32, float64.
        num_samples(int, optional): Number of samples, default is 1.
        replacement(bool, optional): Whether it is a replaceable sample, default is False.
        name(str, optional): The default value is None. Normally there is no
            need for user to set this property. For more information, please
            refer to :ref:`api_guide_Name`.
    Returns:
        Tensor: A Tensor filled with sampled category index after ``num_samples`` times samples.

    Examples:
        .. code-block:: python

147 148
            import paddle

C
cnn 已提交
149
            paddle.seed(100) # on CPU device
150
            x = paddle.rand([2,4])
151
            print(x)
152 153 154
            # [[0.5535528  0.20714243 0.01162981 0.51577556]
            # [0.36369765 0.2609165  0.18905126 0.5621971 ]]

C
cnn 已提交
155
            paddle.seed(200) # on CPU device
156
            out1 = paddle.multinomial(x, num_samples=5, replacement=True)
157
            print(out1)
158 159 160 161 162 163 164
            # [[3 3 0 0 0]
            # [3 3 3 1 0]]

            # out2 = paddle.multinomial(x, num_samples=5)
            # InvalidArgumentError: When replacement is False, number of samples
            #  should be less than non-zero categories

C
cnn 已提交
165
            paddle.seed(300) # on CPU device
166
            out3 = paddle.multinomial(x, num_samples=3)
167
            print(out3)
168 169
            # [[3 0 1]
            # [3 1 0]]
P
pangyoki 已提交
170 171 172

    """

173 174 175
    assert core.is_compiled_with_rocm() == False, (
        "multinomial op is not supported on ROCM yet.")

P
pangyoki 已提交
176
    if in_dygraph_mode():
W
wanghuancoder 已提交
177 178
        return _C_ops.multinomial(x, 'num_samples', num_samples, 'replacement',
                                  replacement)
P
pangyoki 已提交
179 180 181 182 183 184 185 186 187 188 189 190

    check_variable_and_dtype(x, "x", ["float32", "float64"], "multinomial")

    helper = LayerHelper("multinomial", **locals())
    out = helper.create_variable_for_type_inference(
        dtype=convert_np_dtype_to_dtype_('int64'))
    helper.append_op(
        type='multinomial',
        inputs={"X": x},
        outputs={'Out': out},
        attrs={'num_samples': num_samples,
               'replacement': replacement})
191
    out.stop_gradient = True
P
pangyoki 已提交
192 193 194
    return out


195
def gaussian(shape, mean=0.0, std=1.0, dtype=None, name=None):
196 197 198 199 200
    """
    This OP returns a Tensor filled with random values sampled from a Gaussian
    distribution, with ``shape`` and ``dtype``.

    Args:
201
        shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape``
202 203 204 205
            is a list or tuple, the elements of it should be integers or Tensors
            (with the shape [1], and the data type int32 or int64). If ``shape``
            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
            int64).
206 207
        mean (float|int, optional): Mean of the output tensor, default is 0.0.
        std (float|int, optional): Standard deviation of the output tensor, default
208
            is 1.0.
209 210
        seed (int, optional): Random seed of generator.
        dtype (str|np.dtype, optional): The data type of the output Tensor.
211 212 213
            Supported data types: float32, float64.
            Default is None, use global default dtype (see ``get_default_dtype``
            for details).
214
        name (str, optional): The default value is None. Normally there is no
215 216 217 218 219 220 221
            need for user to set this property. For more information, please
            refer to :ref:`api_guide_Name`.

    Returns:
        Tensor: A Tensor filled with random values sampled from a Gaussian
        distribution, with ``shape`` and ``dtype``. 
    """
222 223 224
    op_type_for_check = 'gaussian/standard_normal/randn/normal'
    seed = 0

225 226 227 228
    if dtype is None:
        dtype = paddle.framework.get_default_dtype()
        if dtype not in ['float32', 'float64']:
            raise TypeError(
229 230
                "{} only supports [float32, float64], but the default dtype is {}"
                .format(op_type_for_check, dtype))
231 232 233 234
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)

    if in_dygraph_mode():
235
        shape = utils.convert_shape_to_list(shape)
W
wanghuancoder 已提交
236 237 238
        return _C_ops.gaussian_random('shape', shape, 'mean',
                                      float(mean), 'std',
                                      float(std), 'seed', seed, 'dtype', dtype)
239

240
    check_shape(shape, op_type_for_check)
241 242 243 244 245 246 247 248 249 250
    check_dtype(dtype, 'dtype', ['float32', 'float64'], op_type_for_check)

    inputs = {}
    attrs = {
        'mean': mean,
        'std': std,
        'seed': seed,
        'dtype': dtype,
        'use_mkldnn': False
    }
251
    utils.get_shape_tensor_inputs(
252 253
        inputs=inputs, attrs=attrs, shape=shape, op_type=op_type_for_check)

254
    helper = LayerHelper('gaussian', **locals())
255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type='gaussian_random',
        inputs=inputs,
        outputs={'Out': out},
        attrs=attrs)
    out.stop_gradient = True
    return out


def standard_normal(shape, dtype=None, name=None):
    """
    This OP returns a Tensor filled with random values sampled from a standard
    normal distribution with mean 0 and standard deviation 1, with ``shape``
    and ``dtype``.

    Args:
272
        shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape``
273 274 275 276
            is a list or tuple, the elements of it should be integers or Tensors
            (with the shape [1], and the data type int32 or int64). If ``shape``
            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
            int64).
277
        dtype (str|np.dtype, optional): The data type of the output Tensor.
278 279 280
            Supported data types: float32, float64.
            Default is None, use global default dtype (see ``get_default_dtype``
            for details).
281 282 283 284 285 286 287 288 289 290 291 292 293 294
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor: A Tensor filled with random values sampled from a standard
        normal distribution with mean 0 and standard deviation 1, with
        ``shape`` and ``dtype``.

    Examples:
        .. code-block:: python

            import paddle

            # example 1: attr shape is a list which doesn't contain Tensor.
295
            out1 = paddle.standard_normal(shape=[2, 3])
296 297 298 299
            # [[-2.923464  ,  0.11934398, -0.51249987],  # random
            #  [ 0.39632758,  0.08177969,  0.2692008 ]]  # random

            # example 2: attr shape is a list which contains Tensor.
Z
zhupengyang 已提交
300 301
            dim1 = paddle.to_tensor([2], 'int64')
            dim2 = paddle.to_tensor([3], 'int32')
302
            out2 = paddle.standard_normal(shape=[dim1, dim2, 2])
303 304 305 306 307 308 309 310
            # [[[-2.8852394 , -0.25898588],  # random
            #   [-0.47420555,  0.17683524],  # random
            #   [-0.7989969 ,  0.00754541]],  # random
            #  [[ 0.85201347,  0.32320443],  # random
            #   [ 1.1399018 ,  0.48336947],  # random
            #   [ 0.8086993 ,  0.6868893 ]]]  # random

            # example 3: attr shape is a Tensor, the data type must be int64 or int32.
311
            shape_tensor = paddle.to_tensor([2, 3])
Z
zhupengyang 已提交
312
            out3 = paddle.standard_normal(shape_tensor)
313 314 315 316
            # [[-2.878077 ,  0.17099959,  0.05111201]  # random
            #  [-0.3761474, -1.044801  ,  1.1870178 ]]  # random

    """
317
    return gaussian(shape=shape, mean=0.0, std=1.0, dtype=dtype, name=name)
318 319


Z
zhupengyang 已提交
320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371
def randn(shape, dtype=None, name=None):
    """
    This OP returns a Tensor filled with random values sampled from a standard
    normal distribution with mean 0 and standard deviation 1, with ``shape``
    and ``dtype``.

    Args:
        shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape``
            is a list or tuple, the elements of it should be integers or Tensors
            (with the shape [1], and the data type int32 or int64). If ``shape``
            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
            int64).
        dtype (str|np.dtype, optional): The data type of the output Tensor.
            Supported data types: float32, float64.
            Default is None, use global default dtype (see ``get_default_dtype``
            for details).
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor: A Tensor filled with random values sampled from a standard
        normal distribution with mean 0 and standard deviation 1, with
        ``shape`` and ``dtype``.

    Examples:
        .. code-block:: python

            import paddle

            # example 1: attr shape is a list which doesn't contain Tensor.
            out1 = paddle.randn(shape=[2, 3])
            # [[-2.923464  ,  0.11934398, -0.51249987],  # random
            #  [ 0.39632758,  0.08177969,  0.2692008 ]]  # random

            # example 2: attr shape is a list which contains Tensor.
            dim1 = paddle.to_tensor([2], 'int64')
            dim2 = paddle.to_tensor([3], 'int32')
            out2 = paddle.randn(shape=[dim1, dim2, 2])
            # [[[-2.8852394 , -0.25898588],  # random
            #   [-0.47420555,  0.17683524],  # random
            #   [-0.7989969 ,  0.00754541]],  # random
            #  [[ 0.85201347,  0.32320443],  # random
            #   [ 1.1399018 ,  0.48336947],  # random
            #   [ 0.8086993 ,  0.6868893 ]]]  # random

            # example 3: attr shape is a Tensor, the data type must be int64 or int32.
            shape_tensor = paddle.to_tensor([2, 3])
            out3 = paddle.randn(shape_tensor)
            # [[-2.878077 ,  0.17099959,  0.05111201]  # random
            #  [-0.3761474, -1.044801  ,  1.1870178 ]]  # random
    """
    return standard_normal(shape, dtype, name)
372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415


def normal(mean=0.0, std=1.0, shape=None, name=None):
    """
    This OP returns a Tensor filled with random values sampled from a normal
    distribution with ``mean`` and ``std`` (standard deviation) .

    If ``mean`` is a Tensor, the output Tensor has the same shape and data type as ``mean``.
    If ``mean`` is not a Tensor and ``std`` is a Tensor, the output Tensor has the same shape and data type as ``std``.
    If ``mean`` and ``std`` are not a Tensor, the output Tensor has the same shape as ``shape``, with data type float32.

    If ``mean`` and ``std`` are Tensor, the num of elements of ``mean`` and ``std`` should be the same.

    Args:
        mean (float|Tensor, optional): The mean of the output Tensor's normal distribution.
            If ``mean`` is float, all elements of the output Tensor shared the same mean.
            If ``mean`` is a Tensor(data type supports float32, float64), it has per-element means.
            Default is 0.0
        std (float|Tensor, optional): The  standard deviation of the output Tensor's normal distribution.
            If ``std`` is float, all elements of the output Tensor shared the same standard deviation.
            If ``std`` is a Tensor(data type supports float32, float64), it has per-element standard deviations.
            Defaule is 1.0
        shape (list|tuple|Tensor, optional): The shape of the output Tensor. If ``shape``
            is a list or tuple, the elements of it should be integers or Tensors
            (with the shape [1], and the data type int32 or int64). If ``shape``
            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
            int64). If ``mean`` or ``std`` is a Tensor, the shape of the output
            Tensor is the same as ``mean`` or ``std`` , attr ``shape`` is ignored.
            Default is None
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        A Tensor filled with random values sampled from a normal distribution with ``mean`` and ``std`` .

    Examples:
        .. code-block:: python

            import paddle

            out1 = paddle.normal(shape=[2, 3])
            # [[ 0.17501129  0.32364586  1.561118  ]  # random
            #  [-1.7232178   1.1545963  -0.76156676]]  # random

416
            mean_tensor = paddle.to_tensor([1.0, 2.0, 3.0])
417 418 419
            out2 = paddle.normal(mean=mean_tensor)
            # [ 0.18644847 -1.19434458  3.93694787]  # random

420
            std_tensor = paddle.to_tensor([1.0, 2.0, 3.0])
421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438
            out3 = paddle.normal(mean=mean_tensor, std=std_tensor)
            # [1.00780561 3.78457445 5.81058198]  # random

    """
    if not in_dygraph_mode():
        check_type(mean, 'mean', (int, float, Variable), 'normal')
        check_type(std, 'std', (int, float, Variable), 'normal')
        if isinstance(mean, Variable):
            check_dtype(
                mean.dtype, 'mean', ['float32', 'float64'], 'normal',
                "If mean is Tensor, it's data type only support float32, float64."
            )
        if isinstance(std, Variable):
            check_dtype(
                std.dtype, 'std', ['float32', 'float64'], 'normal',
                "If std is Tensor, it's data type only support float32, float64."
            )
        if shape is not None:
439
            check_shape(shape, 'normal')
440 441 442 443 444 445 446 447 448 449 450 451 452 453

    if isinstance(mean, Variable):
        if isinstance(std, Variable):
            if std.dtype != mean.dtype:
                std = paddle.cast(std, mean.dtype)
            mean_shape = paddle.shape(mean)
            std = paddle.reshape(std, mean_shape)
        else:
            std = float(std)
        out = standard_normal(paddle.shape(mean), mean.dtype, name)
    elif isinstance(std, Variable):
        mean = float(mean)
        out = standard_normal(paddle.shape(std), std.dtype, name)
    else:
454
        return gaussian(shape=shape, mean=mean, std=std, name=name)
455 456 457 458 459 460 461

    out = out * std + mean
    if not in_dygraph_mode():
        out.stop_grediant = True
    return out


462
def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
P
pangyoki 已提交
463 464 465 466 467
    """
    This OP returns a Tensor filled with random values sampled from a uniform
    distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``.

    Examples:
李灿 已提交
468

Z
zhupengyang 已提交
469
    .. code-block:: text
李灿 已提交
470

P
pangyoki 已提交
471 472 473 474 475 476 477 478 479 480 481
        Input:
          shape = [1, 2]
        Output:
          result=[[0.8505902, 0.8397286]]

    Args:
        shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
            is a list or tuple, the elements of it should be integers or Tensors
            (with the shape [1], and the data type int32 or int64). If ``shape``
            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
            int64).
482 483 484 485
        dtype(str|np.dtype, optional): The data type of the output Tensor.
            Supported data types: float32, float64.
            Default is None, use global default dtype (see ``get_default_dtype``
            for details).
P
pangyoki 已提交
486 487 488 489
        min(float|int, optional): The lower bound on the range of random values
            to generate, ``min`` is included in the range. Default is -1.0.
        max(float|int, optional): The upper bound on the range of random values
            to generate, ``max`` is excluded in the range. Default is 1.0.
J
JYChen 已提交
490 491 492
        seed(int, optional): Random seed used for generating samples. If seed is 0,
            it will use the seed of the global default generator (which can be set by paddle.seed). 
            Note that if seed is not 0, this operator will always generate the same random numbers every
P
pangyoki 已提交
493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
            time. Default is 0.
        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: A Tensor filled with random values sampled from a uniform
        distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``.

    Raises:
        TypeError: If ``shape`` is not list, tuple, Tensor.
        TypeError: If ``dtype`` is not float32, float64.

    Examples:
        .. code-block:: python
            
            import paddle

            # example 1:
            # attr shape is a list which doesn't contain Tensor.
Z
zhupengyang 已提交
513 514 515 516
            out1 = paddle.uniform(shape=[3, 4])
            # [[ 0.84524226,  0.6921872,   0.56528175,  0.71690357], # random
            #  [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random
            #  [ 0.433519,    0.39483607, -0.8660099,   0.83664286]] # random
P
pangyoki 已提交
517 518 519

            # example 2:
            # attr shape is a list which contains Tensor.
Z
zhupengyang 已提交
520 521 522 523 524
            dim1 = paddle.to_tensor([2], 'int64')
            dim2 = paddle.to_tensor([3], 'int32')
            out2 = paddle.uniform(shape=[dim1, dim2])
            # [[-0.9951253,   0.30757582, 0.9899647 ], # random
            #  [ 0.5864527,   0.6607096,  -0.8886161]] # random
P
pangyoki 已提交
525 526 527

            # example 3:
            # attr shape is a Tensor, the data type must be int64 or int32.
528
            shape_tensor = paddle.to_tensor([2, 3])
Z
zhupengyang 已提交
529 530 531
            out3 = paddle.uniform(shape_tensor)
            # [[-0.8517412,  -0.4006908,   0.2551912 ], # random
            #  [ 0.3364414,   0.36278176, -0.16085452]] # random
P
pangyoki 已提交
532
    """
533 534 535 536
    if dtype is None:
        dtype = paddle.framework.get_default_dtype()
        if dtype not in ['float32', 'float64']:
            raise TypeError(
537 538
                "uniform/rand only supports [float32, float64], but the default dtype is {}".
                format(dtype))
539

P
pangyoki 已提交
540 541 542 543
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)

    if in_dygraph_mode():
544
        shape = utils.convert_shape_to_list(shape)
W
wanghuancoder 已提交
545 546 547
        return _C_ops.uniform_random('shape', shape, 'min',
                                     float(min), 'max',
                                     float(max), 'seed', seed, 'dtype', dtype)
P
pangyoki 已提交
548

549 550
    check_type(shape, 'shape', (list, tuple, Variable), 'uniform/rand')
    check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform/rand')
P
pangyoki 已提交
551 552 553

    inputs = dict()
    attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
554
    utils.get_shape_tensor_inputs(
555
        inputs=inputs, attrs=attrs, shape=shape, op_type='uniform/rand')
P
pangyoki 已提交
556

557
    helper = LayerHelper("uniform", **locals())
P
pangyoki 已提交
558 559 560 561
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="uniform_random", inputs=inputs, attrs=attrs,
        outputs={"Out": out})
562
    out.stop_gradient = True
P
pangyoki 已提交
563 564 565
    return out


J
JYChen 已提交
566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600
@dygraph_only
def uniform_(x, min=-1.0, max=1.0, seed=0, name=None):
    """
    This is the inplace version of OP ``uniform``, which returns a Tensor filled 
    with random values sampled from a uniform distribution. The output Tensor will
    be inplaced with input ``x``. Please refer to :ref:`api_tensor_uniform`.
    
    Args:
        x(Tensor): The input tensor to be filled with random values.
        min(float|int, optional): The lower bound on the range of random values
            to generate, ``min`` is included in the range. Default is -1.0.
        max(float|int, optional): The upper bound on the range of random values
            to generate, ``max`` is excluded in the range. Default is 1.0.
        seed(int, optional): Random seed used for generating samples. If seed is 0, 
            it will use the seed of the global default generator (which can be set by paddle.seed). 
            Note that if seed is not 0, this operator will always generate the same random numbers every
            time. Default is 0.
        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 input tensor x filled with random values sampled from a uniform
        distribution in the range [``min``, ``max``).
    Examples:
        .. code-block:: python
            
            import paddle
            # example:
            x = paddle.ones(shape=[3, 4])
            x.uniform_()
            print(x)
            # [[ 0.84524226,  0.6921872,   0.56528175,  0.71690357], # random
            #  [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random
            #  [ 0.433519,    0.39483607, -0.8660099,   0.83664286]] # random
    """
601 602
    return _C_ops.uniform_random_inplace_(x, 'min', min, 'max', max, 'seed',
                                          seed)
J
JYChen 已提交
603 604


605
def randint(low=0, high=None, shape=[1], dtype=None, name=None):
S
silingtong123 已提交
606
    """
607 608 609
    This OP returns a Tensor filled with random integers from a discrete uniform
    distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.
    If ``high`` is None (the default), the range is [0, ``low``).
S
silingtong123 已提交
610 611

    Args:
612
        low (int): The lower bound on the range of random values to generate.
613 614
            The ``low`` is included in the range. If ``high`` is None, the
            range is [0, ``low``). Default is 0.
615
        high (int, optional): The upper bound on the range of random values to
616 617
            generate, the ``high`` is excluded in the range. Default is None
            (see above for behavior if high = None). Default is None.
618
        shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape``
619 620 621 622
            is a list or tuple, the elements of it should be integers or Tensors
            (with the shape [1], and the data type int32 or int64). If ``shape``
            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
            int64). Default is [1].
623
        dtype (str|np.dtype, optional): The data type of the
624 625
            output tensor. Supported data types: int32, int64. If ``dytpe``
            is None, the data type is int64. Default is None.
626
        name (str, optional): The default value is None.  Normally there is no
627 628
            need for user to set this property.  For more information, please
            refer to :ref:`api_guide_Name`.
S
silingtong123 已提交
629 630

    Returns: 
631 632
        Tensor: A Tensor filled with random integers from a discrete uniform
        distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.
S
silingtong123 已提交
633 634 635

    Examples:
        .. code-block:: python
636

637
            import paddle
638

639 640
            # example 1:
            # attr shape is a list which doesn't contain Tensor.
641
            out1 = paddle.randint(low=-5, high=5, shape=[3])
642 643 644 645
            # [0, -3, 2]  # random

            # example 2:
            # attr shape is a list which contains Tensor.
Z
zhupengyang 已提交
646 647 648
            dim1 = paddle.to_tensor([2], 'int64')
            dim2 = paddle.to_tensor([3], 'int32')
            out2 = paddle.randint(low=-5, high=5, shape=[dim1, dim2])
649 650 651 652 653
            # [[0, -1, -3],  # random
            #  [4, -2,  0]]  # random

            # example 3:
            # attr shape is a Tensor
654
            shape_tensor = paddle.to_tensor(3)
Z
zhupengyang 已提交
655
            out3 = paddle.randint(low=-5, high=5, shape=shape_tensor)
656 657 658 659
            # [-2, 2, 3]  # random

            # example 4:
            # data type is int32
660
            out4 = paddle.randint(low=-5, high=5, shape=[3], dtype='int32')
661 662 663 664 665
            # [-5, 4, -4]  # random

            # example 5:
            # Input only one parameter
            # low=0, high=10, shape=[1], dtype='int64'
666
            out5 = paddle.randint(10)
667
            # [7]  # random
S
silingtong123 已提交
668

669 670
    """
    if high is None:
671 672 673 674
        if low <= 0:
            raise ValueError(
                "If high is None, low must be greater than 0, but received low = {0}.".
                format(low))
675 676
        high = low
        low = 0
S
silingtong123 已提交
677 678
    if dtype is None:
        dtype = 'int64'
679 680
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)
S
silingtong123 已提交
681 682

    if in_dygraph_mode():
683
        shape = utils.convert_shape_to_list(shape)
W
wanghuancoder 已提交
684 685
        return _C_ops.randint('shape', shape, 'low', low, 'high', high, 'seed',
                              0, 'dtype', dtype)
S
silingtong123 已提交
686

687
    check_shape(shape, 'randint')
688 689
    check_dtype(dtype, 'dtype', ['int32', 'int64'], 'randint')
    if low >= high:
S
silingtong123 已提交
690 691 692 693
        raise ValueError(
            "randint's low must less then high, but received low = {0}, "
            "high = {1}".format(low, high))

694 695
    inputs = dict()
    attrs = {'low': low, 'high': high, 'seed': 0, 'dtype': dtype}
696
    utils.get_shape_tensor_inputs(
697 698 699 700 701 702
        inputs=inputs, attrs=attrs, shape=shape, op_type='randint')

    helper = LayerHelper("randint", **locals())
    out = helper.create_variable_for_type_inference(dtype=dtype)
    helper.append_op(
        type='randint', inputs=inputs, outputs={'Out': out}, attrs=attrs)
703
    out.stop_gradient = True
S
silingtong123 已提交
704
    return out
C
cc 已提交
705 706


707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
def randint_like(x, low=0, high=None, dtype=None, name=None):
    """
    This OP returns a Tensor filled with random integers from a discrete uniform
    distribution in the range [``low``, ``high``), with the same shape as ``x``.
    (use ``dtype`` if ``dtype`` is not None) 
    If ``high`` is None (the default), the range is [0, ``low``).

    Args:
        x (Tensor): The input tensor which specifies shape. The dtype of ``x`` 
            can be bool, int32, int64, float16, float32, float64.
        low (int): The lower bound on the range of random values to generate.
            The ``low`` is included in the range. If ``high`` is None, the
            range is [0, ``low``). Default is 0.
        high (int, optional): The upper bound on the range of random values to
            generate, the ``high`` is excluded in the range. Default is None
            (see above for behavior if high = None). Default is None.
        dtype (str|np.dtype, optional): The data type of the
            output tensor. Supported data types: bool, int32, int64, float16, 
            float32, float64. If ``dytpe`` is None, the data type is the
            same as x's data type. Default is None.
        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: A Tensor filled with random integers from a discrete uniform
        distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.

    Examples:
        .. code-block:: python

            import paddle

            # example 1:
            # dtype is None and the dtype of x is float16
            x = paddle.zeros((1,2)).astype("float16")
            out1 = paddle.randint_like(x, low=-5, high=5)
            print(out1)
            print(out1.dtype)
            # [[0, -3]]  # random
            # paddle.float16

            # example 2:
            # dtype is None and the dtype of x is float32
            x = paddle.zeros((1,2)).astype("float32")
            out2 = paddle.randint_like(x, low=-5, high=5)
            print(out2)
            print(out2.dtype)
            # [[0, -3]]  # random
            # paddle.float32

            # example 3:
            # dtype is None and the dtype of x is float64
            x = paddle.zeros((1,2)).astype("float64")
            out3 = paddle.randint_like(x, low=-5, high=5)
            print(out3)
            print(out3.dtype)
            # [[0, -3]]  # random
            # paddle.float64

            # example 4:
            # dtype is None and the dtype of x is int32
            x = paddle.zeros((1,2)).astype("int32")
            out4 = paddle.randint_like(x, low=-5, high=5)
            print(out4)
            print(out4.dtype)
            # [[0, -3]]  # random
            # paddle.int32

            # example 5:
            # dtype is None and the dtype of x is int64
            x = paddle.zeros((1,2)).astype("int64")
            out5 = paddle.randint_like(x, low=-5, high=5)
            print(out5)
            print(out5.dtype)
            # [[0, -3]]  # random
            # paddle.int64

            # example 6:
            # dtype is float64 and the dtype of x is float32
            x = paddle.zeros((1,2)).astype("float32")
            out6 = paddle.randint_like(x, low=-5, high=5, dtype="float64")
            print(out6)
            print(out6.dtype)
            # [[0, -1]]  # random
            # paddle.float64

            # example 7:
            # dtype is bool and the dtype of x is float32
            x = paddle.zeros((1,2)).astype("float32")
            out7 = paddle.randint_like(x, low=-5, high=5, dtype="bool")
            print(out7)
            print(out7.dtype)
            # [[0, -1]]  # random
            # paddle.bool

            # example 8:
            # dtype is int32 and the dtype of x is float32
            x = paddle.zeros((1,2)).astype("float32")
            out8 = paddle.randint_like(x, low=-5, high=5, dtype="int32")
            print(out8)
            print(out8.dtype)
            # [[0, -1]]  # random
            # paddle.int32

            # example 9:
            # dtype is int64 and the dtype of x is float32
            x = paddle.zeros((1,2)).astype("float32")
            out9 = paddle.randint_like(x, low=-5, high=5, dtype="int64")
            print(out9)
            print(out9.dtype)
            # [[0, -1]]  # random
            # paddle.int64

            # example 10:
            # dtype is int64 and the dtype of x is bool
            x = paddle.zeros((1,2)).astype("bool")
            out10 = paddle.randint_like(x, low=-5, high=5, dtype="int64")
            print(out10)
            print(out10.dtype)
            # [[0, -1]]  # random
            # paddle.int64

    """
    if high is None:
        if low <= 0:
            raise ValueError(
                "If high is None, low must be greater than 0, but received low = {0}.".
                format(low))
        high = low
        low = 0
    if dtype is None:
        dtype = x.dtype
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)
    shape = x.shape

    if low >= high:
        raise ValueError(
            "randint_like's low must less then high, but received low = {0}, "
            "high = {1}".format(low, high))

    if in_dygraph_mode():
        shape = utils.convert_shape_to_list(shape)
        out = _C_ops.randint('shape', shape, 'low', low, 'high', high, 'seed',
                             0, 'dtype', core.VarDesc.VarType.INT64)
        out = paddle.cast(out, dtype)
        return out

    check_shape(shape, 'randint_like')
    check_dtype(dtype, 'dtype',
                ['bool', 'float16', 'float32', 'float64', 'int32',
                 'int64'], 'randint_like')

    inputs = dict()
    attrs = {
        'low': low,
        'high': high,
        'seed': 0,
        'dtype': core.VarDesc.VarType.INT64
    }
    utils.get_shape_tensor_inputs(
        inputs=inputs, attrs=attrs, shape=shape, op_type='randint_like')

    helper = LayerHelper("randint", **locals())
    out = helper.create_variable_for_type_inference(
        dtype=core.VarDesc.VarType.INT64)
    helper.append_op(
        type='randint', inputs=inputs, outputs={'Out': out}, attrs=attrs)
    out.stop_gradient = True
    out = paddle.cast(out, dtype)
    return out


881
def randperm(n, dtype="int64", name=None):
C
cc 已提交
882
    """
883 884
    This OP returns a 1-D Tensor filled with random permutation values from 0
    to n-1, with ``dtype``.
C
cc 已提交
885 886

    Args:
887 888
        n (int): The upper bound (exclusive), and it should be greater than 0.
        dtype (str|np.dtype, optional): The data type of
889 890
            the output Tensor. Supported data types: int32, int64, float32,
            float64. Default is int64.
891
        name (str, optional): The default value is None. Normally there is no
892 893
            need for user to set this property. For more information, please
            refer to :ref:`api_guide_Name`.
C
cc 已提交
894 895

    Returns:
896 897
        Tensor: A 1-D Tensor filled with random permutation values from 0
        to n-1, with ``dtype``.
C
cc 已提交
898 899 900 901

    Examples:
        .. code-block:: python

902
            import paddle
C
cc 已提交
903

904
            out1 = paddle.randperm(5)
905
            # [4, 1, 2, 3, 0]  # random
C
cc 已提交
906

907
            out2 = paddle.randperm(7, 'int32')
908
            # [1, 6, 2, 0, 4, 3, 5]  # random
C
cc 已提交
909 910
 
    """
911 912 913 914
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)

    if in_dygraph_mode():
W
wanghuancoder 已提交
915
        return _C_ops.randperm('n', n, 'seed', 0, 'dtype', dtype)
C
cc 已提交
916 917 918

    if n < 1:
        raise ValueError("The input n should be greater than 0 in randperm op.")
919 920
    check_dtype(dtype, 'dtype', ['int64', 'int32', 'float32', 'float64'],
                'randperm')
C
cc 已提交
921 922

    helper = LayerHelper("randperm", **locals())
923 924 925 926
    out = helper.create_variable_for_type_inference(dtype)
    attrs = {'n': n, 'dtype': dtype, 'seed': 0}
    helper.append_op(
        type='randperm', inputs={}, outputs={'Out': out}, attrs=attrs)
927
    out.stop_gradient = True
C
cc 已提交
928
    return out
X
Xing Wu 已提交
929 930


931
def rand(shape, dtype=None, name=None):
X
Xing Wu 已提交
932
    """
933 934
    This OP returns a Tensor filled with random values sampled from a uniform
    distribution in the range [0, 1), with ``shape`` and ``dtype``.
X
Xing Wu 已提交
935 936

    Args:
937
        shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape``
938 939 940 941
            is a list or tuple, the elements of it should be integers or Tensors
            (with the shape [1], and the data type int32 or int64). If ``shape``
            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
            int64).
942
        dtype (str|np.dtype, optional): The data type of the output Tensor.
943 944 945
            Supported data types: float32, float64.
            Default is None, use global default dtype (see ``get_default_dtype``
            for details).
946
        name (str, optional): The default value is None. Normally there is no
947 948
            need for user to set this property. For more information, please
            refer to :ref:`api_guide_Name`.
949

X
Xing Wu 已提交
950
    Returns:
951 952
        Tensor: A Tensor filled with random values sampled from a uniform
        distribution in the range [0, 1), with ``shape`` and ``dtype``.
X
Xing Wu 已提交
953 954 955 956

    Examples:
        .. code-block:: python

957
            import paddle
958

959
            # example 1: attr shape is a list which doesn't contain Tensor.
960
            out1 = paddle.rand(shape=[2, 3])
961 962 963 964
            # [[0.451152  , 0.55825245, 0.403311  ],  # random
            #  [0.22550228, 0.22106001, 0.7877319 ]]  # random

            # example 2: attr shape is a list which contains Tensor.
Z
zhupengyang 已提交
965 966
            dim1 = paddle.to_tensor([2], 'int64')
            dim2 = paddle.to_tensor([3], 'int32')
967
            out2 = paddle.rand(shape=[dim1, dim2, 2])
968 969 970 971 972 973 974 975
            # [[[0.8879919 , 0.25788337],  # random
            #   [0.28826773, 0.9712097 ],  # random
            #   [0.26438272, 0.01796806]],  # random
            #  [[0.33633623, 0.28654453],  # random
            #   [0.79109055, 0.7305809 ],  # random
            #   [0.870881  , 0.2984597 ]]]  # random

            # example 3: attr shape is a Tensor, the data type must be int64 or int32.
976
            shape_tensor = paddle.to_tensor([2, 3])
Z
zhupengyang 已提交
977
            out3 = paddle.rand(shape_tensor)
978 979
            # [[0.22920267, 0.841956  , 0.05981819],  # random
            #  [0.4836288 , 0.24573246, 0.7516129 ]]  # random
X
Xing Wu 已提交
980 981

    """
982
    return uniform(shape, dtype, min=0.0, max=1.0, name=name)
983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028


def exponential_(x, lam=1.0, name=None):
    """
    This inplace OP fill input Tensor ``x`` with random number from a Exponential Distribution.

    ``lam`` is :math:`\lambda` parameter of Exponential Distribution. 
    
    .. math::

        f(x) = \lambda e^{-\lambda x}

    Args:
        x(Tensor):  Input tensor. The data type should be float32, float64.
        lam(float): :math:`\lambda` parameter of Exponential Distribution.
        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: Input Tensor ``x``.

    Examples:
        .. code-block:: python

            import paddle
            paddle.set_device('cpu')
            paddle.seed(100)

            x = paddle.empty([2,3])
            x.exponential_()
            # [[0.80643415, 0.23211166, 0.01169797],
            #  [0.72520673, 0.45208144, 0.30234432]]

    """
    if in_dygraph_mode():
        return _C_ops.exponential_(x, "lambda", lam)

    check_variable_and_dtype(x, "x", ["float32", "float64"], "exponential")

    helper = LayerHelper("exponential", **locals())
    helper.append_op(
        type='exponential',
        inputs={"X": x},
        outputs={'Out': x},
        attrs={"lambda": lam})
    return x