-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
data_feeder.py
504 lines (436 loc) · 18 KB
/
data_feeder.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
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
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
# Copyright (c) 2018 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.
import struct
import numpy as np
from paddle import pir
from ..pir import Value
from ..pir.core import _PADDLE_PIR_DTYPE_2_NUMPY_DTYPE, ParameterMeta
from . import core
from .framework import (
Variable,
_cpu_num,
_cuda_ids,
default_main_program,
in_dygraph_mode,
in_pir_mode,
)
__all__ = []
_PADDLE_DTYPE_2_NUMPY_DTYPE = {
core.VarDesc.VarType.BOOL: 'bool',
core.VarDesc.VarType.FP16: 'float16',
core.VarDesc.VarType.BF16: 'uint16',
core.VarDesc.VarType.FP32: 'float32',
core.VarDesc.VarType.FP64: 'float64',
core.VarDesc.VarType.INT8: 'int8',
core.VarDesc.VarType.INT16: 'int16',
core.VarDesc.VarType.INT32: 'int32',
core.VarDesc.VarType.INT64: 'int64',
core.VarDesc.VarType.UINT8: 'uint8',
core.VarDesc.VarType.COMPLEX64: 'complex64',
core.VarDesc.VarType.COMPLEX128: 'complex128',
}
_NUMPY_DTYPE_2_PADDLE_DTYPE = {
'bool': core.VarDesc.VarType.BOOL,
'float16': core.VarDesc.VarType.FP16,
'uint16': core.VarDesc.VarType.BF16,
'float32': core.VarDesc.VarType.FP32,
'float64': core.VarDesc.VarType.FP64,
'int8': core.VarDesc.VarType.INT8,
'int16': core.VarDesc.VarType.INT16,
'int32': core.VarDesc.VarType.INT32,
'int64': core.VarDesc.VarType.INT64,
'uint8': core.VarDesc.VarType.UINT8,
'complex64': core.VarDesc.VarType.COMPLEX64,
'complex128': core.VarDesc.VarType.COMPLEX128,
}
def convert_float_to_uint16(data, data_format="NCHW"):
if data.size == 0:
return data.view(np.uint16)
if data_format == "NHWC":
data = np.transpose(data, [0, 3, 1, 2])
new_data = np.vectorize(
lambda x: struct.unpack('<I', struct.pack('<f', x))[0] >> 16,
otypes=[np.uint16],
)(data.flat)
new_data = np.reshape(new_data, data.shape)
if data_format == "NHWC":
new_data = np.transpose(new_data, [0, 2, 3, 1])
return new_data
def convert_uint16_to_float(data):
new_data = np.vectorize(
lambda x: struct.unpack('<f', struct.pack('<I', x << 16))[0],
otypes=[np.float32],
)(data.flat)
return np.reshape(new_data, data.shape)
def convert_dtype(dtype):
if isinstance(dtype, core.VarDesc.VarType):
if dtype in _PADDLE_DTYPE_2_NUMPY_DTYPE:
return _PADDLE_DTYPE_2_NUMPY_DTYPE[dtype]
if isinstance(dtype, core.DataType):
if dtype in _PADDLE_PIR_DTYPE_2_NUMPY_DTYPE:
return _PADDLE_PIR_DTYPE_2_NUMPY_DTYPE[dtype]
elif isinstance(dtype, type):
# This branch is for NumPy scalar types
if dtype in [
bool,
np.float16,
np.uint16,
np.float32,
np.float64,
np.int8,
np.int16,
np.int32,
np.int64,
np.uint8,
np.complex64,
np.complex128,
]:
return dtype.__name__
else:
# This branch is for np.dtype and str
if dtype in [
'bool',
'float16',
'uint16',
'float32',
'float64',
'int8',
'int16',
'int32',
'int64',
'uint8',
'complex64',
'complex128',
]:
# NOTE(SigureMo): Since the np.dtype object is not an instance of
# type, so it will not be handled by the previous branch. We need
# to convert it to str here.
return str(dtype)
# NOTE(zhangbo): Now numpy does not support bfloat, so use numpy.uint16 to represent paddle.bfloat16, there binaries are consistent.
# If cast ndarray to uint16 and trans to tensor, should not ndarray.astype('uint16') directly
# should use function 'convert_float_to_uint16' above, otherwise bits is wrong
if dtype in ['bfloat16']:
return 'uint16'
raise TypeError(
"dtype must be any of [bool, float16, uint16, float32, float64, int8, int16, "
"int32, int64, uint8, complex64, complex128, bfloat16], but received %s"
% dtype
)
def check_variable_and_dtype(
input, input_name, expected_dtype, op_name, extra_message=''
):
if in_pir_mode():
check_type(
input, input_name, (Value, ParameterMeta), op_name, extra_message
)
else:
check_type(input, input_name, Variable, op_name, extra_message)
check_dtype(input.dtype, input_name, expected_dtype, op_name, extra_message)
def check_type(input, input_name, expected_type, op_name, extra_message=''):
# NOTE [ Why skip dynamic graph check ]:
# 1. If the input type / dtype of a layer is wrong, it will be reported
# directly on that line. User can easily print the relevant information
# on which line. It is easier to debug, so there is no need to check
# in dynamic graph mode.
# 2. Performance considerations. Because these checks are executed at
# each step in dynamic graph mode, it will bring a heavy performance burden.
if in_dygraph_mode():
return
# NOTE: `in_to_static_mode` is used to determined whether this op is called under
# @to_static in transformation from dygraph to static layer. We add Tensor in
# expected_type to skip checking because Tensor may be created and used in unusual way.
from .dygraph.base import in_to_static_mode
# Need a better design to be fix this.
if in_to_static_mode():
if not isinstance(expected_type, tuple):
expected_type = (expected_type,)
expected_type += (core.eager.Tensor,)
elif isinstance(input, core.eager.Tensor):
raise TypeError(
"Please use `with base.dygraph.guard()` as context or `base.enable_dygraph()` to switch to imperative mode firstly. "
f"Because received '{input_name}' in {op_name} is a imperative Variable."
)
if not isinstance(input, expected_type):
raise TypeError(
f"The type of '{input_name}' in {op_name} must be {expected_type}, but received {type(input)}. {extra_message}"
)
def check_dtype(
input_dtype, input_name, expected_dtype, op_name, extra_message=''
):
# See NOTE [ Why skip dynamic graph check ]
if in_dygraph_mode():
return
if convert_dtype(input_dtype) not in expected_dtype:
raise TypeError(
f"The data type of '{input_name}' in {op_name} must be {expected_dtype}, but received {convert_dtype(input_dtype)}. {extra_message}"
)
def check_shape(
shape,
op_name,
expected_shape_type=(list, tuple, Variable, Value),
expected_element_type=(int, Variable, Value),
expected_tensor_dtype=('int32', 'int64'),
):
# See NOTE [ Why skip dynamic graph check ]
if in_dygraph_mode():
return
check_type(shape, 'shape', expected_shape_type, op_name)
if expected_element_type is not None and not isinstance(
shape, (Variable, Value)
):
for item in shape:
check_type(item, 'element of shape', expected_element_type, op_name)
if expected_tensor_dtype is not None and isinstance(
item, (Variable, Value)
):
check_dtype(
item.dtype,
'element of shape',
expected_tensor_dtype,
op_name,
'If element of shape is Tensor, its data type should be {}'.format(
', '.join(expected_tensor_dtype)
),
)
if expected_tensor_dtype is not None and isinstance(
shape, (Variable, Value)
):
check_dtype(shape.dtype, 'shape', expected_tensor_dtype, op_name)
class DataToLoDTensorConverter:
def __init__(self, place, lod_level, shape, dtype):
self.place = place
self.lod_level = lod_level
self.shape = shape
negative_count = 0
for s in self.shape:
if s < 0:
negative_count += 1
if negative_count > 1:
self.shape = None
break
self.dtype = convert_dtype(dtype)
self._reset()
def _reset(self):
self.data = []
self.lod = [[] for _ in range(self.lod_level)]
def feed(self, data):
self._feed_impl_(data, self.lod, self.lod_level)
def _feed_impl_(self, data, lod, lod_level):
if lod_level == 0:
self.data.append(data)
else:
lod[0].append(len(data))
for each_data in data:
self._feed_impl_(each_data, lod[1:], lod_level - 1)
def _check_shape(self, shape):
for s1, s2 in zip(self.shape, shape):
if s1 != s2 and s1 >= 0 and s2 >= 0:
raise ValueError(
f"Shape not match. What is defined in data layer is {self.shape}, but receive {shape}"
)
def done(self):
arr = np.array(self.data, dtype=self.dtype)
if self.shape:
if len(arr.shape) != len(self.shape):
try:
arr = arr.reshape(self.shape)
except ValueError:
raise ValueError(
f"Reshape error. What is defined in data layer is {self.shape}, but receive {arr.shape}"
)
t = core.LoDTensor()
t.set(arr, self.place)
if self.lod_level > 0:
t.set_recursive_sequence_lengths(self.lod)
self._reset()
return t
class BatchedTensorProvider:
def __init__(self, feed_list, place, batch_size, generator, drop_last):
self.place = place
self.batch_size = batch_size
self.generator = generator
self.converters = []
self.drop_last = drop_last
for var in feed_list:
assert var.lod_level == 0, "lod_level must be 0"
self.converters.append(
DataToLoDTensorConverter(
place=self.place,
lod_level=0,
shape=var.shape,
dtype=var.dtype,
)
)
def _done(self):
return [c.done() for c in self.converters]
def __call__(self):
idx = 0
for each_sample in self.generator():
for each_slot, each_converter in zip(each_sample, self.converters):
each_converter.data.append(each_slot)
idx += 1
if idx == self.batch_size:
idx = 0
yield self._done()
if not self.drop_last and idx > 0:
yield self._done()
else:
[c._reset() for c in self.converters]
class DataFeeder:
"""
:api_attr: Static Graph
DataFeeder converts the data that returned by a reader into a data
structure that can feed into Executor. The reader is usually a
python generator that returns a list of mini-batch data entries.
Parameters:
feed_list (list): Variables or names of Variables that need
to feed.
place (:ref:`api_paddle_CPUPlace` | :ref:`api_paddle_CUDAPlace` ):
place indicates the device (CPU | GPU) the data will be fed into, if
you want to feed data into GPU, please using :code:`base.CUDAPlace(i)`
(:code:`i` represents the GPU id), or if you want to feed data into CPU,
please using :code:`base.CPUPlace()`.
program (:ref:`api_paddle_static_Program` , optional): The Program that will
feed data into, if program is None, it will use default_main_program().
Default None.
Raises:
:code:`ValueError` - If some Variables are not in this Program.
Example:
.. code-block:: python
>>> import numpy as np
>>> import paddle
>>> from paddle import base
>>> paddle.enable_static()
>>> place = paddle.CPUPlace()
>>> def reader():
... for _ in range(4):
... yield np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32'),
...
>>> main_program = paddle.static.Program()
>>> startup_program = paddle.static.Program()
>>> with paddle.static.program_guard(main_program, startup_program):
... data_1 = paddle.static.data(name='data_1', shape=[None, 2, 2], dtype='float32')
... data_2 = paddle.static.data(name='data_2', shape=[None, 1, 3], dtype='float32')
... out = paddle.static.nn.fc(x=[data_1, data_2], size=2)
... # ...
>>> feeder = base.DataFeeder([data_1, data_2], place)
>>> exe = paddle.static.Executor(place)
>>> exe.run(startup_program)
>>> feed_data = feeder.feed(reader())
>>> # print feed_data to view feed results
>>> # print(feed_data['data_1'])
>>> # print(feed_data['data_2'])
>>> outs = exe.run(
... program=main_program,
... feed=feed_data,
... fetch_list=[out]
... )
>>> print(outs)
"""
def __init__(self, feed_list, place, program=None):
self.feed_dtypes = []
self.feed_names = []
self.feed_shapes = []
self.feed_lod_level = []
self.place = place
if in_pir_mode():
if program is None:
program = pir.core.default_main_program()
for each_var in feed_list:
if isinstance(each_var, str):
raise ValueError(
"In PIR Mode, Not supported string input yet"
)
if not isinstance(each_var, Value):
raise TypeError("Feed list should contain a list of Value")
self.feed_dtypes.append(each_var.dtype)
self.feed_names.append(each_var.name)
self.feed_lod_level.append(0)
self.feed_shapes.append(each_var.shape)
else:
if program is None:
program = default_main_program()
for each_var in feed_list:
if isinstance(each_var, str):
each_var = program.block(0).var(each_var)
if not isinstance(each_var, Variable):
raise TypeError(
"Feed list should contain a list of variable"
)
self.feed_dtypes.append(each_var.dtype)
self.feed_names.append(each_var.name)
self.feed_lod_level.append(each_var.lod_level)
self.feed_shapes.append(each_var.shape)
def feed(self, iterable):
"""
According to :code:`feed_list` of :code:`DataFeeder` and :code:`iterable` , converts
the input into a data structure that can feed into Executor.
Parameters:
iterable (generator): user defined python generator to read the raw input data
Returns:
:code:`dict`: a :code:`dict` that contains (variable name - converted tensor) pairs
Example:
.. code-block:: python
>>> # In this example, reader - generator will return a list of ndarray of 3 elements
>>> # feed API will convert each ndarray input into a tensor
>>> # the return result is a dict with keys: data_1, data_2, data_3
>>> # result['data_1'] a LoD-Tensor with shape of [5, 2, 1, 3]. 5 is batch size, and [2, 1, 3] is the real shape of data_1.
>>> # result['data_2'], result['data_3'] are similar.
>>> import numpy as np
>>> import paddle
>>> from paddle import base
>>> paddle.enable_static()
>>> def reader(limit=5):
... for i in range(1, limit + 1):
... yield np.ones([6]).astype('float32') * i , np.ones([1]).astype('int64') * i, np.random.random([9]).astype('float32')
...
>>> data_1 = paddle.static.data(name='data_1', shape=[None, 2, 1, 3])
>>> data_2 = paddle.static.data(name='data_2', shape=[None, 1], dtype='int64')
>>> data_3 = paddle.static.data(name='data_3', shape=[None, 3, 3], dtype='float32')
>>> feeder = base.DataFeeder(['data_1','data_2', 'data_3'], paddle.CPUPlace())
>>> result = feeder.feed(reader())
>>> print(result['data_1'])
>>> print(result['data_2'])
>>> print(result['data_3'])
"""
converter = []
for lod_level, shape, dtype in zip(
self.feed_lod_level, self.feed_shapes, self.feed_dtypes
):
converter.append(
DataToLoDTensorConverter(
place=self.place,
lod_level=lod_level,
shape=shape,
dtype=dtype,
)
)
for each_sample in iterable:
assert len(each_sample) == len(converter), (
"The number of fields in data (%d) does not match "
+ "len(feed_list) (%d)"
) % (len(each_sample), len(converter))
for each_converter, each_slot in zip(converter, each_sample):
each_converter.feed(each_slot)
ret_dict = {}
for each_name, each_converter in zip(self.feed_names, converter):
ret_dict[each_name] = each_converter.done()
return ret_dict
def _get_number_of_places_(self, num_places):
if num_places is not None:
return int(num_places)
elif isinstance(self.place, core.CUDAPlace):
return len(_cuda_ids())
else:
return _cpu_num()