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# Copyright 2017 The TensorFlow 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.
# =============================================================================
"""Operations for TPUs."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import platform
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
if platform.system() != "Windows":
# pylint: disable=wildcard-import,unused-import,g-import-not-at-top
from tensorflow.contrib.tpu.ops import gen_tpu_ops
from tensorflow.contrib.tpu.ops.gen_tpu_ops import *
from tensorflow.contrib.util import loader
from tensorflow.python.platform import resource_loader
# pylint: enable=wildcard-import,unused-import,g-import-not-at-top
_tpu_ops = loader.load_op_library(
resource_loader.get_path_to_datafile("_tpu_ops.so"))
@ops.RegisterGradient("CrossReplicaSum")
def _cross_replica_sum_grad(op, grad):
# The gradient of a cross replica sum is also a cross-replica sum.
return gen_tpu_ops.cross_replica_sum(grad, op.get_attr("group_assignment"))
# This extra type checking exists to give a more helpful error message in
# the common case that uint8 and int64 values are infed. Remove when both
# types are supported.
_SUPPORTED_INFEED_DTYPES = set([
dtypes.bool, dtypes.int32, dtypes.int64, dtypes.bfloat16, dtypes.float32,
dtypes.complex64
])
def infeed_dequeue(dtype, shape, name=None):
"""A placeholder op for a value that will be fed into the computation.
Args:
dtype: A `tf.DType`. The type of elements in the tensor.
shape: A `tf.TensorShape` or list of `ints`. The shape of the tensor.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `dtype`.
A tensor that will be provided using the infeed mechanism.
Raises:
TypeError: If 'dtype` is not a supported infeed type.
"""
if dtype not in _SUPPORTED_INFEED_DTYPES:
raise TypeError(
"{} is not a supported TPU infeed type. Supported types are: "
"{}".format(dtype, list(_SUPPORTED_INFEED_DTYPES)))
return gen_tpu_ops.infeed_dequeue(dtype, shape, name=name)
# pylint: disable=redefined-outer-name
def infeed_dequeue_tuple(dtypes, shapes, name=None):
"""A placeholder op for values fed into the TPU simultaneously as a tuple.
Args:
dtypes: A list of `tf.DType`s that has length `>= 1`.
The element types of each element in `outputs`.
shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
The shapes of each tensor in `outputs`.
name: A name for the operation (optional).
Returns:
A list of `Tensor` objects of type `dtypes`.
A list of tensors that will be provided using the infeed mechanism.
Raises:
TypeError: If a type in 'dtypes` is not a supported infeed type.
"""
for dtype in dtypes:
if dtype not in _SUPPORTED_INFEED_DTYPES:
raise TypeError(
"{} is not a supported TPU infeed type. Supported types are: "
"{}".format(dtype, list(_SUPPORTED_INFEED_DTYPES)))
return gen_tpu_ops.infeed_dequeue_tuple(dtypes, shapes, name=name)
# pylint: enable=redefined-outer-name
else:
# We have already built the appropriate libraries into the binary via CMake
# if we have built contrib, so we don't need this
pass
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