import numbers
from tensorflow.python.ops import array_ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
# from tensorflow.contrib.offline_train.ops import gen_npu_ops
from tensorflow.contrib.util import loader
from tensorflow.python.eager import context
from tensorflow.python.framework import device
from tensorflow.python.framework import ops
from tensorflow.python.platform import resource_loader
from npu_bridge.estimator.npu.npu_common import NPUBasics
from npu_bridge.helper import helper
gen_npu_ops = helper.get_gen_ops()
DEFAULT_GRAPH_SEED = 87654321
_MAXINT32 = 2**31 - 1
def npu_test():
"""A placeholder op for values fed into the TPU simultaneously as a tuple.
"""
return gen_npu_ops.npu_test()
def NPUInit(name=None):
if context.executing_eagerly():
raise RuntimeError("tf.NPUInit() is not compatible with "
"eager execution.")
#
# 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.
# ==============================================================================
from tensorflow.contrib.util import loader
from tensorflow.python.framework import load_library
from tensorflow.python.platform import resource_loader
from npu_bridge.helper import helper
gen_npu_image_ops = helper.get_gen_ops();
def decode_and_resize_jpeg(image, size):
"""Decode and resize JPEG-encoded image.
Args:
image: The JPEG-encoded image.
size: A 1-D int32 Tensor of 2 elements: new_height, new_width.
The new size for the images.
Returns:
Resized image, a 3-D uint8 tensor: [new_height, new_width, channel=3] .
"""
return gen_npu_image_ops.decode_and_resize_jpeg(image, size)
def decode_and_crop_and_resize_jpeg(image, crop_size, size):
from __future__ import division
from __future__ import print_function
import numbers
from tensorflow.contrib.util import loader
from tensorflow.python.platform import resource_loader
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import dtypes
from tensorflow.python.eager import context
from tensorflow.python.framework import device
from npu_bridge.estimator.npu.npu_common import NPUBasics
from npu_bridge.helper import helper
npu_aicore_ops = helper.get_gen_ops();
@ops.RegisterGradient("FastGelu")
def _fast_gelu_grad(op, grad):
"""The gradient for `fast_gelu`.
Args:
op: The `fast_gelu` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
grad: Gradient with respect to the output of the `fast_gelu` op.
Returns:
Gradients with respect to the input of `fast_gelu`.
"""
return [npu_aicore_ops.fast_gelu_grad(grad, op.inputs[0])] # List of one Tensor, since we have one input
# 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.
# ==============================================================================
## @file hccl_ops.py
# HCCL 算子API
from tensorflow.python.framework import load_library
from tensorflow.python.framework import ops
from tensorflow.python.platform import resource_loader
from npu_bridge.helper import helper
gen_hccl_ops = helper.get_gen_ops()
## 提供group内的集合通信allreduce功能
# @param tensor tensorflow的tensor类型，allreduce操作的输入；
# @param reduction string类型，reduce的操作类型，可以为”max”,”min”,”prod”和”sum”;
# @param fusion int类型，算子融合标识。0: 不融合；1: 按照梯度切分设置融合，默认融； 2: 按照相同fusion_id融合。
# @param fusion_id int类型，算子融合索引标识，相同fusion_id的算子将会融合。
# @param group string类型，group名称，可以为用户自定义group或者"hccl_world_group";
# @return 对输入tensor执行完allreduce操作之后的结果tensor
def allreduce(tensor,
reduction,
fusion=1,
fusion_id=-1,
group="hccl_world_group"):
result = gen_hccl_ops.hcom_all_reduce(input=tensor,
# 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.
# ==============================================================================
"""All bert ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.util import loader
from tensorflow.python.platform import resource_loader
from tensorflow.python.framework import ops
from npu_bridge.helper import helper
npu_unary_ops = helper.get_gen_ops()
@ops.RegisterGradient("Gelu")
def _gelu_grad(op, grad):
"""The gradient for `gelu`.
Args:
op: The `gelu` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
grad: Gradient with respect to the output of the `gelu` op.
Returns:
Gradients with respect to the input of `gelu`.
"""
return [npu_unary_ops.gelu_grad(grad, op.inputs[0], op.outputs[0])
