1# Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15 16"""Operations for automatic batching and unbatching.""" 17from __future__ import absolute_import 18from __future__ import division 19from __future__ import print_function 20 21from tensorflow.python.eager import function 22from tensorflow.python.framework import ops 23from tensorflow.python.framework import tensor_spec 24from tensorflow.python.ops import gen_batch_ops 25# pylint: disable=wildcard-import 26from tensorflow.python.ops.gen_batch_ops import * 27# pylint: enable=wildcard-import 28from tensorflow.python.util.tf_export import tf_export 29 30 31@tf_export("nondifferentiable_batch_function") 32def batch_function(num_batch_threads, 33 max_batch_size, 34 batch_timeout_micros, 35 allowed_batch_sizes=None, 36 max_enqueued_batches=10, 37 autograph=True, 38 enable_large_batch_splitting=True): 39 """Batches the computation done by the decorated function. 40 41 So, for example, in the following code 42 43 ```python 44 @batch_function(1, 2, 3) 45 def layer(a): 46 return tf.matmul(a, a) 47 48 b = layer(w) 49 ``` 50 51 if more than one session.run call is simultaneously trying to compute `b` 52 the values of `w` will be gathered, non-deterministically concatenated 53 along the first axis, and only one thread will run the computation. See the 54 documentation of the `Batch` op for more details. 55 56 Assumes that all arguments of the decorated function are Tensors which will 57 be batched along their first dimension. 58 59 SparseTensor is not supported. The return value of the decorated function 60 must be a Tensor or a list/tuple of Tensors. 61 62 Args: 63 num_batch_threads: Number of scheduling threads for processing batches 64 of work. Determines the number of batches processed in parallel. 65 max_batch_size: Batch sizes will never be bigger than this. 66 batch_timeout_micros: Maximum number of microseconds to wait before 67 outputting an incomplete batch. 68 allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, 69 does nothing. Otherwise, supplies a list of batch sizes, causing the op 70 to pad batches up to one of those sizes. The entries must increase 71 monotonically, and the final entry must equal max_batch_size. 72 max_enqueued_batches: The maximum depth of the batch queue. Defaults to 10. 73 autograph: Whether to use autograph to compile python and eager style code 74 for efficient graph-mode execution. 75 enable_large_batch_splitting: The value of this option doesn't affect 76 processing output given the same input; it affects implementation details 77 as stated below: 1. Improve batching efficiency by eliminating unnecessary 78 adding. 2.`max_batch_size` specifies the limit of input and 79 `allowed_batch_sizes` specifies the limit of a task to be processed. API 80 user can give an input of size 128 when 'max_execution_batch_size' 81 is 32 -> implementation can split input of 128 into 4 x 32, schedule 82 concurrent processing, and then return concatenated results corresponding 83 to 128. 84 85 Returns: 86 The decorated function will return the unbatched computation output Tensors. 87 """ 88 89 def decorator(fn): # pylint: disable=missing-docstring 90 91 def decorated(*args): # pylint: disable=missing-docstring 92 93 @function.defun(autograph=autograph) 94 def computation(*computation_args): 95 return fn(*computation_args) 96 97 computation = computation.get_concrete_function( 98 *[tensor_spec.TensorSpec(dtype=x.dtype, shape=x.shape, name=str(i)) 99 for i, x in enumerate(args)]) 100 101 with ops.name_scope("batch") as name: 102 for a in args: 103 if not isinstance(a, ops.Tensor): 104 raise ValueError("All arguments to functions decorated with " 105 "`batch_function` are supposed to be Tensors; " 106 "found %s" % repr(a)) 107 return gen_batch_ops.batch_function( 108 num_batch_threads=num_batch_threads, 109 max_batch_size=max_batch_size, 110 batch_timeout_micros=batch_timeout_micros, 111 allowed_batch_sizes=allowed_batch_sizes, 112 max_enqueued_batches=max_enqueued_batches, 113 shared_name=name, 114 enable_large_batch_splitting=enable_large_batch_splitting, 115 f=computation, 116 in_tensors=list(args), 117 captured_tensors=computation.captured_inputs, 118 Tout=[o.dtype for o in computation.outputs]) 119 120 return decorated 121 122 return decorator 123