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ipu_utils.py
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ipu_utils.py
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# Copyright (c) 2019 Graphcore Ltd. 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 os
import subprocess
import re
from tensorflow.python.ipu.config import (
IPUConfig,
SchedulingAlgorithm,
DeviceConnectionType
)
from tensorflow.python.ipu import utils
def get_ipu_arch():
try:
cmd = ['gc-info', '-d', '0', '--ipu-arch']
ret = subprocess.check_output(cmd).decode('utf-8')
pattern = re.compile('ipu(\d)')
match = pattern.match(ret)
arch = int(match.group(1))
except:
arch = 2
return arch
def get_config(prng=False,
ipu_id=-1,
shards=1,
number_of_replicas=1,
max_cross_replica_buffer_size=50*1024*1024,
merge_infeed_io_copies=True,
fp_exceptions=True,
half_partials=False,
conv_dithering=False,
conv_output=False,
enable_recomputation=False,
seed=None,
availableMemoryProportion=None,
stable_norm=False,
internalExchangeOptimisationTarget=None,
num_io_tiles=0,
number_of_distributed_batch_norm_replicas=1,
min_remote_tensor_size=128,
compile_only=False,
nanoo=True,
scheduling_algorithm=SchedulingAlgorithm.CHOOSE_BEST,
max_reduce_many_buffer_size=0
):
"""Builds ipu_options"""
config = IPUConfig()
config.optimizations.merge_infeed_io_copies = merge_infeed_io_copies
if scheduling_algorithm == SchedulingAlgorithm.CHOOSE_BEST:
if get_ipu_arch() == 2:
scheduling_algorithm = SchedulingAlgorithm.SHORTEST_PATH
else:
# work around to avoid OOM on MK1
scheduling_algorithm = SchedulingAlgorithm.CHOOSE_BEST
config.scheduling.algorithm = scheduling_algorithm
config.experimental.always_rearrange_copies_on_the_host = False
config.optimizations.minimum_remote_tensor_size = min_remote_tensor_size
config.optimizations.maximum_cross_replica_sum_buffer_size = (
max_cross_replica_buffer_size)
config.optimizations.maximum_reduce_many_buffer_size = (
max_reduce_many_buffer_size)
if ipu_id == -1:
config.auto_select_ipus = number_of_replicas * shards
else:
config.select_ipus = [ipu_id]
config.compilation_poplar_options = {
'target.deterministicWorkers': 'false' if seed is None else 'portable'}
if internalExchangeOptimisationTarget is not None:
config.compilation_poplar_options['opt.internalExchangeOptimisationTarget'] = internalExchangeOptimisationTarget
if num_io_tiles != 0:
config.io_tiles.place_ops_on_io_tiles = True
config.io_tiles.num_io_tiles = num_io_tiles
config.convolutions.poplar_options = {}
if availableMemoryProportion is not None:
config.convolutions.poplar_options['availableMemoryProportion'] = str(availableMemoryProportion)
if half_partials:
config.convolutions.poplar_options['partialsType'] = 'half'
config.matmuls.poplar_options['partialsType'] = 'half'
if conv_dithering:
config.convolutions.poplar_options['enableConvDithering'] = 'true'
if conv_output:
config.convolutions.poplar_options['gatherConvOutput'] = 'true'
if stable_norm:
config.norms.use_stable_statistics = True
if enable_recomputation:
config.allow_recompute = True
if compile_only:
config.device_connection.version = 'ipu2'
config.device_connection.enable_remote_buffers = True
# PRE_COMPILE allows for runing execuatables on graph without being online
config.device_connection.type = DeviceConnectionType.PRE_COMPILE
# Enforce using a exe cache path, defaulting if it doesnt exist
tf_poplar_flags = os.environ.get("TF_POPLAR_FLAGS") or ''
if '--executable_cache_path' not in tf_poplar_flags:
print("Warning: --executable_cache_path not set. " +
"Defaulting to '/tmp/tf_cache'.")
tf_poplar_flags = f"{tf_poplar_flags} --executable_cache_path=/tmp/tf_cache"
os.environ["TF_POPLAR_FLAGS"] = tf_poplar_flags
config.floating_point_behaviour.inv = fp_exceptions
config.floating_point_behaviour.div0 = fp_exceptions
config.floating_point_behaviour.oflo = fp_exceptions
config.floating_point_behaviour.esr = prng
config.floating_point_behaviour.nanoo = nanoo
config.norms.experimental.distributed_batch_norm_replica_group_size = (
number_of_distributed_batch_norm_replicas)
return config