tensor.py 13.3 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13 14
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

Y
Yu Yang 已提交
15
from ..layer_helper import LayerHelper
16
from ..param_attr import ParamAttr
X
xuwei06 已提交
17 18
from ..framework import convert_np_dtype_to_dtype_
from ..framework import Variable
19
from ..initializer import Constant, force_init_on_cpu
20
from ..core import VarDesc
X
xuwei06 已提交
21
import numpy
Y
Yu Yang 已提交
22 23

__all__ = [
24 25
    'create_tensor',
    'create_parameter',
Q
Qiao Longfei 已提交
26
    'create_global_var',
27 28 29 30 31 32 33 34
    'cast',
    'concat',
    'sums',
    'assign',
    'fill_constant_batch_size_like',
    'fill_constant',
    'ones',
    'zeros',
Y
Yu Yang 已提交
35 36 37
]


X
xuwei06 已提交
38
def create_tensor(dtype, name=None, persistable=False):
Y
Yu Yang 已提交
39
    helper = LayerHelper("create_tensor", **locals())
X
xuwei06 已提交
40 41
    return helper.create_variable(
        name=helper.name, dtype=dtype, persistable=persistable)
Y
Yu Yang 已提交
42 43


44 45
def create_parameter(shape,
                     dtype,
X
xuwei06 已提交
46
                     name=None,
47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
                     attr=None,
                     is_bias=False,
                     default_initializer=None):
    """
    Create a parameter
    Args:
        shape(list[int]): shape of the parameter
        dtype(string): element type of the parameter
        attr(ParamAttr): attributes of the parameter
        is_bias(bool): This can affect which default initializer is chosen
                       when default_initializer is None. If is_bias,
                       initializer.Constant(0.0) will be used. Otherwise,
                       Xavier() will be used.
        default_initializer(Initializer): initializer for the parameter

    Returns:
        Parameter: the created parameter
    """
Q
Qiao Longfei 已提交
65
    helper = LayerHelper("create_parameter", **locals())
66
    if attr is None:
X
xuwei06 已提交
67
        attr = ParamAttr(name=name)
68 69 70 71
    return helper.create_parameter(attr, shape, dtype, is_bias,
                                   default_initializer)


72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
def create_global_var(shape,
                      value,
                      dtype,
                      persistable=False,
                      force_cpu=False,
                      name=None):
    """
    Create a global variable. such as global_step
    Args:
        shape(list[int]): shape of the variable
        value(float): the value of the variable
        dtype(string): element type of the parameter
        persistable(bool): if this variable is persistable
        force_cpu(bool): force this variable to be on CPU

    Returns:
        Variable: the created Variable
    """
Q
Qiao Longfei 已提交
90 91 92 93
    helper = LayerHelper("global_var", **locals())
    var = helper.create_global_variable(
        dtype=dtype, shape=shape, persistable=persistable, name=name)
    helper.set_variable_initializer(
94 95
        var, initializer=Constant(
            value=float(value), force_cpu=force_cpu))
Q
Qiao Longfei 已提交
96 97 98
    return var


99
def cast(x, dtype):
Y
Yu Yang 已提交
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114
    """
    This function takes in the input with input_dtype
    and casts it to the output_dtype as the output.
    """
    helper = LayerHelper('cast', **locals())
    out = helper.create_tmp_variable(dtype=dtype)
    helper.append_op(
        type='cast',
        inputs={'X': [x]},
        outputs={'Out': [out]},
        attrs={'in_dtype': x.dtype,
               'out_dtype': out.dtype})
    return out


115
def concat(input, axis=0):
Y
Yu Yang 已提交
116
    """
117 118 119
    **Concat**

    This function concatenates the input along the axis mentioned
Y
Yu Yang 已提交
120
    and returns that as the output.
121 122 123 124 125 126 127 128 129 130 131

    Args:
        input(list): List of tensors to be concatenated
        axis(int): Integer axis along which the tensors will be concatenated

    Returns:
        Variable: Output variable of the concatenation

    Examples:
        .. code-block:: python
          out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
Y
Yu Yang 已提交
132 133 134 135 136 137 138 139 140 141 142
    """
    helper = LayerHelper('concat', **locals())
    out = helper.create_tmp_variable(dtype=helper.input_dtype())
    helper.append_op(
        type='concat',
        inputs={'X': input},
        outputs={'Out': [out]},
        attrs={'axis': axis})
    return out


143
def sums(input, out=None):
K
kavyasrinet 已提交
144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
    """This function performs the sum operation on the input and returns the
    result as the output.

    Args:
        input (Variable|list): The input tensor that has the elements
                               that need to be summed up.

    Returns:
        Variable: The tensor type variable that has the sum of input
                  written to it.

    Examples:
        .. code-block::python

          tmp = fluid.layers.zeros(shape=[10], dtype='int32')
          i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
          a0 = layers.array_read(array=tmp, i=i)
          i = layers.increment(x=i)
          a1 = layers.array_read(array=tmp, i=i)
Y
Yu Yang 已提交
163 164
          mean_a0 = layers.mean(a0)
          mean_a1 = layers.mean(a1)
K
kavyasrinet 已提交
165
          a_sum = layers.sums(input=[mean_a0, mean_a1])
Y
Yu Yang 已提交
166 167 168 169 170 171 172 173
    """
    helper = LayerHelper('sum', **locals())
    if out is None:
        out = helper.create_tmp_variable(dtype=helper.input_dtype())
    helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
    return out


174
def assign(input, output):
175 176 177 178 179 180
    """
    **Assign**

    This function copies the *input* Variable to the *output* Variable.

    Args:
X
xuwei06 已提交
181
        input(Variable|numpy.ndarray): The source variable
182 183 184 185 186 187 188 189 190 191 192
        output(Variable): The destination variable

    Returns:
        Variable: The destination variable that was supplied as the *output*.

    Examples:
        .. code-block:: python
          out = fluid.layers.create_tensor(dtype='float32')
          hidden = fluid.layers.fc(input=data, size=10)
          fluid.layers.assign(hidden, out)
    """
Y
Yu Yang 已提交
193
    helper = LayerHelper('assign', **locals())
X
xuwei06 已提交
194 195 196 197 198 199 200 201
    if isinstance(input, Variable):
        helper.append_op(
            type='scale',
            inputs={'X': [input]},
            outputs={'Out': [output]},
            attrs={'scale': 1.0})
    elif isinstance(input, numpy.ndarray):
        dtype = convert_np_dtype_to_dtype_(input.dtype)
202
        if dtype == VarDesc.VarType.FP32:
X
xuwei06 已提交
203
            value_name = "fp32_values"
204
            values = [float(v) for v in input.flat]
205
        elif dtype == VarDesc.VarType.INT32:
X
xuwei06 已提交
206
            value_name = "int32_values"
207
            values = [int(v) for v in input.flat]
X
xuwei06 已提交
208 209
        else:
            raise ValueError("Unsupported dtype %s", input.dtype)
210 211 212
        if input.size > 1024 * 1024:
            raise ValueError("The size of input is too big. Please consider "
                             "saving it to file and 'load_op' to load it")
X
xuwei06 已提交
213 214 215 216 217 218 219

        helper.append_op(
            type='assign_value',
            outputs={'Out': [output]},
            attrs={
                'dtype': dtype,
                'shape': list(input.shape),
220
                value_name: values
X
xuwei06 已提交
221 222 223 224
            })
    else:
        raise ValueError("Wrong type for assign input: %s" % type(input))

Y
Yu Yang 已提交
225 226 227
    return output


Q
QI JUN 已提交
228
def fill_constant(shape, dtype, value, force_cpu=False, out=None):
Y
Yu Yang 已提交
229
    """
230 231
    **fill_constant**

232 233
    This function creates a tensor with specified `shape` and `dtype`, and
    initializes it with a constant specifed by `value`.
K
kavyasrinet 已提交
234

235
    The attribute `stop_gradient` of the created tensor is set to True.
236 237

    Args:
238
        shape(tuple|list|None): Shape of the output tensor.
239
        dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor.
240 241
        value(float): The constant value used to initialize the output tensor.
        out(Variable): The output tensor.
242
        force_cpu(True|False): data should be on CPU if set true.
243 244

    Returns:
245
        Variable: The tensor variable storing the output.
246 247 248 249 250

    Examples:
        .. code-block:: python

          data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
Y
Yu Yang 已提交
251
    """
252

Y
Yu Yang 已提交
253 254 255 256 257 258 259
    helper = LayerHelper("fill_constant", **locals())
    if out is None:
        out = helper.create_tmp_variable(dtype=dtype)
    helper.append_op(
        type='fill_constant',
        inputs={},
        outputs={'Out': [out]},
Q
QI JUN 已提交
260 261 262 263
        attrs={
            'shape': shape,
            'dtype': out.dtype,
            'value': float(value),
264
            'force_cpu': force_cpu or force_init_on_cpu()
Q
QI JUN 已提交
265
        })
Y
Yu Yang 已提交
266 267 268 269 270 271 272 273 274
    out.stop_gradient = True
    return out


def fill_constant_batch_size_like(input,
                                  shape,
                                  dtype,
                                  value,
                                  input_dim_idx=0,
275
                                  output_dim_idx=0):
276 277 278
    """
    **fill_constant_batch_size_like**

K
kavyasrinet 已提交
279 280 281
    This function creates a tensor of specified *shape*, *dtype* and batch size,
    and initializes this with a constant supplied in *value*. The batch size is
    obtained from the `input` tensor.
282 283 284 285 286 287

    It also sets *stop_gradient* to True.

    Args:
        input(Variable): Tensor whose dimensions will be used to get batch size
        shape(tuple|list|None): Shape of output tensor
288
        dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
289 290 291 292 293 294 295 296 297 298
        value(float): Constant value to initialize the output tensor
        input_dim_idx(int): Index of input's batch size dimension
        output_dim_idx(int): Index of output's batch size dimension

    Returns:
        Variable: The tensor variable storing the output

    Examples:
        .. code-block:: python

299 300
          data = fluid.layers.fill_constant_batch_size_like(
              input=like, shape=[1], value=0, dtype='int64')
301
    """
Y
Yu Yang 已提交
302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318
    helper = LayerHelper("fill_constant_batch_size_like", **locals())
    out = helper.create_tmp_variable(dtype=dtype)
    helper.append_op(
        type='fill_constant_batch_size_like',
        inputs={'Input': input},
        outputs={'Out': [out]},
        attrs={
            'shape': shape,
            'dtype': out.dtype,
            'value': float(value),
            'input_dim_idx': input_dim_idx,
            'output_dim_idx': output_dim_idx
        })
    out.stop_gradient = True
    return out


Y
Yang Yu 已提交
319
def ones(shape, dtype, force_cpu=False):
Y
Yu Yang 已提交
320
    """
321 322 323 324 325 326 327 328 329
    **ones**

    This function creates a tensor of specified *shape* and
    *dtype*, and initializes this with 1.

    It also sets *stop_gradient* to True.

    Args:
        shape(tuple|list|None): Shape of output tensor
330
        dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
331 332 333 334 335 336 337 338

    Returns:
        Variable: The tensor variable storing the output

    Examples:
        .. code-block:: python

          data = fluid.layers.ones(shape=[1], dtype='int64')
Y
Yu Yang 已提交
339 340 341 342
    """
    return fill_constant(value=1.0, **locals())


Y
Yang Yu 已提交
343
def zeros(shape, dtype, force_cpu=False):
Y
Yu Yang 已提交
344
    """
345 346 347 348 349 350 351 352 353
    **zeros**

    This function creates a tensor of specified *shape* and
    *dtype*, and initializes this with 0.

    It also sets *stop_gradient* to True.

    Args:
        shape(tuple|list|None): Shape of output tensor
354
        dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
355 356 357 358 359 360 361 362

    Returns:
        Variable: The tensor variable storing the output

    Examples:
        .. code-block:: python

          data = fluid.layers.zeros(shape=[1], dtype='int64')
Y
Yu Yang 已提交
363 364
    """
    return fill_constant(value=0.0, **locals())
365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436


def save(x, file_path, overwrite=True):
    """
    Saves a variable as a file.

    Args:
        x(variable): The Tensor/LoDTensor to be saved.
        file_path(str): The file path where the variable will be saved.
        overwrite(bool): Whether or not cover the given file when it has already 
            existed. If it's set 'False' and the file is existed, a runtime 
            error will be thrown. 
    """
    helper = LayerHelper("save", **locals())
    helper.append_op(
        type="save",
        inputs={"input": x},
        outputs={},
        args={"file_path": file_path,
              "overwrite": overwrite})


def save_combine(x, file_path, overwrite=True):
    """
    Saves a list of variables into a single file.

    Args:
        x(list): A list of Tensor/LoDTensor to be saved together in a single file.
        file_path(str): The file path where variables will be saved.
        overwrite(bool): Whether or not cover the given file when it has already 
            existed. If it's set 'False' and the file is existed, a runtime 
            error will be thrown. 
    """
    helper = LayerHelper("save_combine", **locals())
    helper.append_op(
        type="save_combine",
        inputs={"input": x},
        outputs={},
        args={"file_path": file_path,
              "overwrite": overwrite})


def load(out, file_path):
    """
    Loads a variable from a given file.

    Args:
        out(variable): The variable to be read from the disk file.
        file_path(str): The path of the disk file.
    """
    helper = LayerHelper("load", **locals())
    helper.append_op(
        type="load",
        inputs={},
        output={"Out": out},
        args={"file_path": file_path})


def load_combine(out, file_path):
    """
    Loads a list of vairables from a single file.

    Args:
        out(list): The list of variables to be read from the disk file.
        file_path(str): The path of the disk file.
    """
    helper = LayerHelper("load_combine", **locals())
    helper.append_op(
        type="load_combine",
        inputs={},
        output={"Out": out},
        args={"file_path": file_path})