def __init__(self,
iterations_per_loop=1,
profiling_config=None,
model_dir=None,
tf_random_seed=None,
save_summary_steps=0,
save_checkpoints_steps=None,
save_checkpoints_secs=None,
session_config=None,
keep_checkpoint_max=5,
keep_checkpoint_every_n_hours=10000,
log_step_count_steps=100,
distribute=None,
enable_data_pre_proc=True,
precision_mode=None,
enable_reduce_precision=False,
variable_format_optimize=True,
mix_compile_mode=False,
hcom_parallel=False,
graph_memory_max_size=None,
variable_memory_max_size=None,
auto_tune_mode=None,
dump_config=None,
stream_max_parallel_num=None,
is_tailing_optimization=False,
horovod_mode=False,
graph_run_mode=1,
op_debug_level=0,
enable_scope_fusion_passes=None,
enable_exception_dump=0,
op_select_implmode=None,
optypelist_for_implmode=None,
dynamic_input_config=None,
mstune_mode=None,
work_path=None,
buffer_optimize="l2_optimize",
enable_small_channel=0,
fusion_switch_file=None,
enable_compress_weight=False,
compress_weight_conf=None,
op_compiler_cache_mode=None,
op_compiler_cache_dir=None,
debug_dir=None,
hcom_multi_mode=False,
dynamic_input=False,
dynamic_graph_execute_mode="dynamic_execute",
dynamic_inputs_shape_range=None,
train_distribute=None,
eval_distribute=None,
local_rank_id=None,
local_device_list=None,
session_device_id=None,
distribute_config=None,
op_tune_mode=None):
"""
Constructs a NPUConfig.
Args:
iterations_per_loop: This is the number of train steps running in NPU
system before returning to CPU host for each `Session.run`. This means
global step is increased `iterations_per_loop` times in one `Session.run`.
It is recommended to be set as number of global steps for next checkpoint.
profiling_config: The profiling configuration.
model_dir: Directory where model parameters, graph, etc are saved. If
`PathLike` object, the path will be resolved. If `None`, will use a
default value set by the Estimator.
tf_random_seed: Random seed for TensorFlow initializers.
Setting this value allows consistency between reruns.
save_summary_steps: Save summaries every this many steps.
save_checkpoints_steps: Save checkpoints every this many steps. Can not be
specified with `save_checkpoints_secs`.
save_checkpoints_secs: Save checkpoints every this many seconds. Can not
be specified with `save_checkpoints_steps`. Defaults to 600 seconds if
both `save_checkpoints_steps` and `save_checkpoints_secs` are not set
in constructor. If both `save_checkpoints_steps` and
`save_checkpoints_secs` are None, then checkpoints are disabled.
session_config: A ConfigProto used to set session parameters, or None.
keep_checkpoint_max: The maximum number of recent checkpoint files to
keep. As new files are created, older files are deleted. If None or 0,
all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent
checkpoint files are kept.)
keep_checkpoint_every_n_hours: Number of hours between each checkpoint
to be saved. The default value of 10,000 hours effectively disables
the feature.
log_step_count_steps: The frequency, in number of global steps, that the
global step/sec and the loss will be logged during training.
enabel_data_pre_proc: This is the switch of data preprocess.
precision_mode: if train, default is: allow_fp32_to_fp16; if inference, default is: force_fp16.
variable_format_optimize: enable or disable variable format optimize while graph
engineer optimize process.
mix_compile_mode: This is the swith of mix_compile_mode. When the value is
False, all graphs run on device. Otherwise, some graphs run on host.
hcom_parallel: This is the switch of hcom parallel. When the value is True,
hcom will execute with parallel mode. Otherwise, hcom will execute with
serialize mode.
graph_memory_max_size: The max size of ge graph memory size.
variable_memory_max_size: The max size of ge variable memory size.
auto_tune_mode: None, or `GA` ,or `RL` or `GA|RL`
dump_config: The dump configuration.
stream_max_parallel_num: Specify the degree of parallelism of the AICPU / AICORE engine
to achieve parallel execution between AICPU / AICORE operators.
op_select_implmode: Selecting whether the operator is implemented with high precision
or high performance.
optypelist_for_implmode: Operator list.
dynamic_input_config:Dynamic dims configuration
mstune_mode: Optimization Task Type."1": model tune; "2": optune;
"3": model tune & optune; "4": gradient split tune.
work_path: Stores temporary files generated during optimization.
buffer_optimize: Whether to enable buffer optimization.
enable_small_channel: Whether to enable small channel optimization.
fusion_switch_file: Fusion switch configuration file path.
enable_compress_weight: Whether to enable global weight compression.
compress_weight_conf:Path and file name of the node list configuration file to be compressed.
dynamic_input:Whether Input is dynamic.
dynamic_graph_execute_mode:Dynamic graph execute mode. lazy_recompile or dynamic_execute
dynamic_inputs_shape_range:Inputs shape range.
local_rank_id: Local sequence number of the device in a group.
local_device_list: Available devices.
distribute_config: Specify the NCA configuration file path
op_tune_mode: None, or `GA` ,or `RL` or `GA|RL`, use with mstune_mode.
"""
# Check iterations_per_loop.
util.check_positive_integer(iterations_per_loop, "iterations_per_loop")
if isinstance(mix_compile_mode, bool) == False:
raise ValueError('"mix_compile_mode" type must be bool')
if mix_compile_mode is True and iterations_per_loop != 1:
raise ValueError(
'"iterations_per_loop" must be 1 with "mix_compile_mode" is True'
)
tf_config = json.loads(
os.environ.get(run_config_lib._TF_CONFIG_ENV, '{}'))
tmp_cluster_spec = server_lib.ClusterSpec(
tf_config.get(run_config_lib._CLUSTER_KEY, {}))
if ((tmp_cluster_spec
and not isinstance(distribute, ParameterServerStrategy))
or (not tmp_cluster_spec
and isinstance(distribute, ParameterServerStrategy))):
raise ValueError(
'"cluster" and "distribute" must all be set in ps mode')
if tmp_cluster_spec and mix_compile_mode is False:
raise ValueError(
'"mix_compile_mode" can only be True with "cluster" is set')
self.iterations_per_loop = iterations_per_loop
self.mix_compile_mode = mix_compile_mode
self.enable_data_pre_proc = enable_data_pre_proc
self.is_tailing_optimization = is_tailing_optimization
if save_checkpoints_secs == None and save_checkpoints_steps == None:
save_checkpoints_steps = 100
self._profiling_config = profiling_config
# mix precision configuration
self._precision_mode = precision_mode
self._enable_reduce_precision = enable_reduce_precision
self._variable_format_optimize = variable_format_optimize
self._hcom_parallel = hcom_parallel
self._graph_memory_max_size = graph_memory_max_size
self._variable_memory_max_size = variable_memory_max_size
self._auto_tune_mode = auto_tune_mode
if dump_config is not None and not isinstance(dump_config, DumpConfig):
raise ValueError(
'`dump_config` must be provided with type `DumpConfig`')
self._dump_config = dump_config
self._stream_max_parallel_num = stream_max_parallel_num
if isinstance(horovod_mode, bool) == False:
raise ValueError('"horovod_mode" type must be bool')
self.horovod_mode = horovod_mode
util.check_nonnegative_integer(graph_run_mode, "graph_run_mode")
if graph_run_mode > 1:
raise ValueError('"graph_run_mode" value must be 0 or 1')
self.graph_run_mode = graph_run_mode
self.op_debug_level = op_debug_level
self.enable_scope_fusion_passes = enable_scope_fusion_passes
experimental_distribute = None
if tmp_cluster_spec and isinstance(distribute,
ParameterServerStrategy):
experimental_distribute = DistributeConfig(distribute, distribute,
None)
util.check_nonnegative_integer(enable_exception_dump,
"enable_exception_dump")
self.enable_exception_dump = enable_exception_dump
self._op_select_implmode = op_select_implmode
self._optypelist_for_implmode = optypelist_for_implmode
if dynamic_input_config is not None and not isinstance(
dynamic_input_config, DynamicInputConfig):
raise ValueError(
'dynamic_input_config must be provided with type DynamicInputConfig'
)
self._dynamic_input_config = dynamic_input_config
self._mstune_mode = mstune_mode
self._work_path = work_path
self._buffer_optimize = buffer_optimize
self._enable_small_channel = enable_small_channel
self._fusion_switch_file = fusion_switch_file
self._enable_compress_weight = enable_compress_weight
self._compress_weight_conf = compress_weight_conf
self._op_compiler_cache_mode = op_compiler_cache_mode
self._op_compiler_cache_dir = op_compiler_cache_dir
self._debug_dir = debug_dir
self._hcom_multi_mode = hcom_multi_mode
self._dynamic_input = dynamic_input
self._dynamic_graph_execute_mode = dynamic_graph_execute_mode
self._dynamic_inputs_shape_range = dynamic_inputs_shape_range
self._local_rank_id = local_rank_id
self._local_device_list = local_device_list
self._session_device_id = session_device_id
self._distribute_config = distribute_config
self._op_tune_mode = op_tune_mode
super(NPURunConfig, self).__init__(
model_dir=model_dir,
tf_random_seed=tf_random_seed,
save_summary_steps=save_summary_steps,
save_checkpoints_steps=save_checkpoints_steps,
save_checkpoints_secs=save_checkpoints_secs,
session_config=session_config,
keep_checkpoint_max=keep_checkpoint_max,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
log_step_count_steps=log_step_count_steps,
experimental_distribute=experimental_distribute,
train_distribute=train_distribute,
eval_distribute=eval_distribute)
def npu_resource_init(graph_run_mode=1,
op_debug_level=0,
enable_profiling=False,
profiling_options=None,
auto_tune_mode=None,
precision_mode=None,
enable_scope_fusion_passes=None,
enable_exception_dump=0,
aoe_mode=None,
work_path=None,
op_compiler_cache_mode=None,
op_compiler_cache_dir=None,
debug_dir=None,
hcom_multi_mode=False,
distribute_config=None):
"""Initialize NPU resource"""
util.check_nonnegative_integer(graph_run_mode, "graph_run_mode")
check_graph_run_mode(graph_run_mode)
util.check_nonnegative_integer(enable_exception_dump,
"enable_exception_dump")
util.check_nonnegative_integer(op_debug_level, "op_debug_level")
util.check_bool_type(enable_profiling, "enable_profiling")
enable_profiling = util.convert_bool_to_int(enable_profiling)
init = {}
init[__option_graph_run_mode] = str(graph_run_mode)
init[__op_debug_level] = str(op_debug_level)
init[__option_exec_profiling_mode] = str(enable_profiling)
if enable_profiling:
if profiling_options is None:
raise ValueError(
'profiling_options must be set when use profiling')
init[__option_exec_profiling_options] = str(profiling_options)
if auto_tune_mode is not None:
init[__auto_tune_mode] = str(auto_tune_mode)
if precision_mode is not None:
init["ge.exec.precision_mode"] = str(precision_mode)
else:
if graph_run_mode:
init["ge.exec.precision_mode"] = str("allow_fp32_to_fp16")
else:
init["ge.exec.precision_mode"] = str("force_fp16")
if enable_scope_fusion_passes is not None:
init[__option_exec_enable_scope_fusion_passes] = str(
enable_scope_fusion_passes)
init["ge.exec.enable_exception_dump"] = str(enable_exception_dump)
if aoe_mode is not None:
util.check_aoe_mode(aoe_mode)
init["ge.jobType"] = str(aoe_mode)
if work_path is not None:
init["ge.tuningPath"] = str(util.check_path(work_path))
else:
init["ge.tuningPath"] = str(util.check_path("./"))
if distribute_config is not None:
init["distribute_config"] = str(distribute_config)
init_op_compiler_cache_mode(init, op_compiler_cache_mode)
init_op_compiler_cache_dir(init, op_compiler_cache_dir)
init_debug_dir(init, debug_dir)
util.check_bool_type(hcom_multi_mode, "hcom_multi_mode")
hcom_multi_mode = util.convert_bool_to_int(hcom_multi_mode)
init["ge.hcomMultiMode"] = str(hcom_multi_mode)
init_options = tf_adapter.map_string_string(init)
tf_adapter.PluginInit(init_options)
def __init__(self,
iterations_per_loop=1,
profiling_config=None,
model_dir=None,
tf_random_seed=None,
save_summary_steps=0,
save_checkpoints_steps=None,
save_checkpoints_secs=None,
session_config=None,
keep_checkpoint_max=5,
keep_checkpoint_every_n_hours=10000,
log_step_count_steps=100,
distribute=None,
enable_data_pre_proc=True,
precision_mode=None,
enable_reduce_precision=False,
variable_format_optimize=True,
mix_compile_mode=False,
hcom_parallel=False,
graph_memory_max_size=None,
variable_memory_max_size=None,
auto_tune_mode=None,
dump_config=None,
stream_max_parallel_num=None,
is_tailing_optimization=False,
horovod_mode=False,
graph_run_mode=1,
op_debug_level=0,
enable_scope_fusion_passes=None,
enable_exception_dump=0,
op_select_implmode=None,
optypelist_for_implmode=None,
dynamic_input_config=None,
aoe_mode=None,
work_path=None,
buffer_optimize="l2_optimize",
enable_small_channel=0,
fusion_switch_file=None,
enable_compress_weight=False,
compress_weight_conf=None,
op_compiler_cache_mode=None,
op_compiler_cache_dir=None,
debug_dir=None,
hcom_multi_mode=False,
dynamic_input=False,
dynamic_graph_execute_mode="dynamic_execute",
dynamic_inputs_shape_range=None,
train_distribute=None,
eval_distribute=None,
local_rank_id=None,
local_device_list=None,
session_device_id=None,
distribute_config=None,
modify_mixlist=None,
op_precision_mode=None,
device_type="default_device_type",
soc_config=None,
hccl_timeout=None,
op_wait_timeout=None,
op_execute_timeout=None,
HCCL_algorithm=None,
customize_dtypes=None,
op_debug_config=None,
memory_config=None):
"""
Constructs a NPUConfig.
Args:
iterations_per_loop: This is the number of train steps running in NPU
system before returning to CPU host for each `Session.run`. This means
global step is increased `iterations_per_loop` times in one `Session.run`.
It is recommended to be set as number of global steps for next checkpoint.
profiling_config: The profiling configuration.
model_dir: Reference tensorflow tf.estimator.RunConfig model_dir.
tf_random_seed: Reference tensorflow tf.estimator.RunConfig tf_random_seed.
save_summary_steps: Reference tensorflow tf.estimator.RunConfig save_summary_steps.
save_checkpoints_steps: Reference tensorflow tf.estimator.RunConfig save_checkpoints_steps.
save_checkpoints_secs: Reference tensorflow tf.estimator.RunConfig save_checkpoints_secs.
session_config: Reference tensorflow tf.estimator.RunConfig session_config.
keep_checkpoint_max: Reference tensorflow tf.estimator.RunConfig keep_checkpoint_max.
keep_checkpoint_every_n_hours: Reference tensorflow tf.estimator.RunConfig keep_checkpoint_every_n_hours.
log_step_count_steps: Reference tensorflow tf.estimator.RunConfig log_step_count_steps.
enabel_data_pre_proc: This is the switch of data preprocess.
precision_mode: if train, default is: allow_fp32_to_fp16; if inference, default is: force_fp16.
variable_format_optimize: enable or disable variable format optimize while graph
engineer optimize process.
mix_compile_mode: This is the swith of mix_compile_mode. When the value is
False, all graphs run on device. Otherwise, some graphs run on host.
hcom_parallel: This is the switch of hcom parallel. When the value is True,
hcom will execute with parallel mode. Otherwise, hcom will execute with
serialize mode.
graph_memory_max_size: The max size of ge graph memory size.
variable_memory_max_size: The max size of ge variable memory size.
auto_tune_mode: None, or `GA` ,or `RL` or `GA|RL`
dump_config: The dump configuration.
stream_max_parallel_num: Specify the degree of parallelism of the AICPU / AICORE engine
to achieve parallel execution between AICPU / AICORE operators.
op_select_implmode: Selecting whether the operator is implemented with high_precision
or high_performance or high_precision_for_all or high_performance_for_all.
optypelist_for_implmode: Operator list.
dynamic_input_config:Dynamic dims configuration
aoe_mode: Optimization Task Type."1": model tune; "2": optune;
"3": model tune & optune; "4": gradient split tune.
work_path: Stores temporary files generated during optimization, default is current path.
buffer_optimize: Whether to enable buffer optimization.
enable_small_channel: Whether to enable small channel optimization.
fusion_switch_file: Fusion switch configuration file path.
enable_compress_weight: Whether to enable global weight compression.
compress_weight_conf:Path and file name of the node list configuration file to be compressed.
dynamic_input:Whether Input is dynamic.
dynamic_graph_execute_mode:Dynamic graph execute mode. lazy_recompile or dynamic_execute
dynamic_inputs_shape_range:Inputs shape range.
local_rank_id: Local sequence number of the device in a group.
local_device_list: Available devices.
distribute_config: Specify the NCA configuration file path
modify_mixlist: Set the path of operator mixed precision configuration file.
op_precision_mode: Set the path of operator precision mode configuration file (.ini)
"""
# Check iterations_per_loop.
util.check_positive_integer(iterations_per_loop, "iterations_per_loop")
if not isinstance(mix_compile_mode, bool):
raise ValueError('"mix_compile_mode" type must be bool')
if mix_compile_mode is True and iterations_per_loop != 1:
raise ValueError(
'"iterations_per_loop" must be 1 with "mix_compile_mode" is True'
)
tf_config = json.loads(
os.environ.get(run_config_lib._TF_CONFIG_ENV, '{}'))
tmp_cluster_spec = server_lib.ClusterSpec(
tf_config.get(run_config_lib._CLUSTER_KEY, {}))
if ((tmp_cluster_spec
and not isinstance(distribute, ParameterServerStrategy))
or (not tmp_cluster_spec
and isinstance(distribute, ParameterServerStrategy))):
raise ValueError(
'"cluster" and "distribute" must all be set in ps mode')
if tmp_cluster_spec and mix_compile_mode is False:
raise ValueError(
'"mix_compile_mode" can only be True with "cluster" is set')
if aoe_mode is None and os.getenv("AOE_MODE") is None:
self.iterations_per_loop = iterations_per_loop
else:
self.iterations_per_loop = 1
self.mix_compile_mode = mix_compile_mode
self.enable_data_pre_proc = enable_data_pre_proc
self.is_tailing_optimization = is_tailing_optimization
save_checkpoints_steps = self._get_save_checkpoints_steps(
save_checkpoints_secs, save_checkpoints_steps)
self._profiling_config = profiling_config
# mix precision configuration
self._precision_mode = precision_mode
self._enable_reduce_precision = enable_reduce_precision
self._variable_format_optimize = variable_format_optimize
self._hcom_parallel = hcom_parallel
self._graph_memory_max_size = graph_memory_max_size
self._variable_memory_max_size = variable_memory_max_size
self._auto_tune_mode = auto_tune_mode
self._dump_config = self._get_dump_config(dump_config)
self._stream_max_parallel_num = stream_max_parallel_num
self.horovod_mode = self._get_horovod_mode(horovod_mode)
util.check_nonnegative_integer(graph_run_mode, "graph_run_mode")
self.graph_run_mode = self._get_graph_run_mode(graph_run_mode)
self.op_debug_level = op_debug_level
self.enable_scope_fusion_passes = enable_scope_fusion_passes
experimental_distribute = self._get_experimental_distribute(
tmp_cluster_spec, distribute)
util.check_nonnegative_integer(enable_exception_dump,
"enable_exception_dump")
self.enable_exception_dump = enable_exception_dump
self._op_select_implmode = op_select_implmode
self._optypelist_for_implmode = optypelist_for_implmode
self._dynamic_input_config = self._get_dynamic_input_config(
dynamic_input_config)
self._aoe_mode = aoe_mode
self._work_path = work_path
self._buffer_optimize = buffer_optimize
self._enable_small_channel = enable_small_channel
self._fusion_switch_file = fusion_switch_file
self._enable_compress_weight = enable_compress_weight
self._compress_weight_conf = compress_weight_conf
self._op_compiler_cache_mode = op_compiler_cache_mode
self._op_compiler_cache_dir = op_compiler_cache_dir
self._debug_dir = debug_dir
self._hcom_multi_mode = hcom_multi_mode
self._dynamic_input = dynamic_input
self._dynamic_graph_execute_mode = dynamic_graph_execute_mode
self._dynamic_inputs_shape_range = dynamic_inputs_shape_range
self._local_rank_id = local_rank_id
self._local_device_list = local_device_list
self._session_device_id = session_device_id
self._distribute_config = distribute_config
self._modify_mixlist = modify_mixlist
self._op_precision_mode = op_precision_mode
self._device_type = device_type
self._soc_config = soc_config
self._hccl_timeout = hccl_timeout
self._op_wait_timeout = op_wait_timeout
self._op_execute_timeout = op_execute_timeout
self._HCCL_algorithm = HCCL_algorithm
self._customize_dtypes = customize_dtypes
self._op_debug_config = op_debug_config
self._memory_config = memory_config
super(NPURunConfig, self).__init__(
model_dir=model_dir,
tf_random_seed=tf_random_seed,
save_summary_steps=save_summary_steps,
save_checkpoints_steps=save_checkpoints_steps,
save_checkpoints_secs=save_checkpoints_secs,
session_config=session_config,
keep_checkpoint_max=keep_checkpoint_max,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
log_step_count_steps=log_step_count_steps,
experimental_distribute=experimental_distribute,
train_distribute=train_distribute,
eval_distribute=eval_distribute)
def _read_job_info(self, file_name):
"""Read the job information.
Args:
file_name: it's a json file which contains the job info from CSA.
Returns:
The job information.
"""
try:
with open(file_name, 'r', encoding='UTF-8') as f:
content = f.read()
data = json.loads(content, encoding='UTF-8')
# 1. Get the device_info and check it.
device_info = data.get('device_info')
util_lib.check_not_none(device_info, 'device_info')
index = device_info.get('Index', None)
util_lib.check_nonnegative_integer(index, 'Index')
# 2. Get the rank_table_file and check it.
rank_table_file = data.get('rank_table_file', None)
util_lib.check_not_none(rank_table_file, 'rank_table_file')
# 3. Get the rank_size and check it.
rank_size = data.get('rank_size', None)
util_lib.check_positive_integer(rank_size, 'rank_size')
# 4. Get the local_checkpoint_dir and check it.
local_checkpoint_dir = data.get('local_checkpoint_dir', None)
# 5. Init the JobInfo.
device_info = DeviceInfo(index=str(index))
job_info = JobInfo(device_info=device_info, rank_table_file=rank_table_file,
local_checkpoint_dir=local_checkpoint_dir, rank_size=rank_size)
return job_info
except IOError:
logging.warning('Warning:job config file does not exist')
job_id = os.getenv('JOB_ID', "")
if(job_id == ""):
logging.error('Error:can not get job config from env')
return None
heartbeat = os.getenv('HEARTBEAT', "")
rank_table_file = os.getenv('RANK_TABLE_FILE', "")
identity = os.getenv('POD_NAME', "")
if(identity == ""):
identity = os.getenv('RANK_ID', "")
checkpoint_dir = os.getenv('LOCAL_CHECKPOINT_DIR', "")
# cann't get rank_size from env, set to default 1
rank_size = os.getenv('RANK_SIZE', 1)
if(rank_size.isdigit() is False):
print("set rank_size to default 1")
rank_size = 1
device_info = DeviceInfo(index=str(identity))
job_info = JobInfo(job_id=job_id,
heartbeat_time=heartbeat,
device_info=device_info,
rank_table_file=rank_table_file,
local_checkpoint_dir=checkpoint_dir,
rank_size=int(rank_size)
)
return job_info
def npu_global_init(graph_run_mode=1,
op_debug_level=0,
is_tailing_optimization=False,
enable_profiling=False,
enable_options=["training_trace"],
auto_tune_mode=None,
precision_mode=None,
enable_scope_fusion_passes=None,
enable_exception_dump=0,
fp_point=None,
bp_point=None):
util.check_nonnegative_integer(graph_run_mode, "graph_run_mode")
if graph_run_mode > 1:
raise ValueError('"graph_run_mode" value must be 0 or 1')
util.check_nonnegative_integer(enable_exception_dump,
"enable_exception_dump")
util.check_nonnegative_integer(op_debug_level, "op_debug_level")
util.check_bool_type(is_tailing_optimization, "is_tailing_optimization")
util.check_bool_type(enable_profiling, "enable_profiling")
is_tailing_optimization = str(
util.convert_bool_to_int(is_tailing_optimization))
enable_profiling = util.convert_bool_to_int(enable_profiling)
init = {}
init[__option_graph_run_mode] = str(graph_run_mode)
init[__op_debug_level] = str(op_debug_level)
init["ge.exec.isTailingOptimization"] = is_tailing_optimization
init[__option_exec_profiling_mode] = str(enable_profiling)
if enable_profiling:
enable_options = str(util.check_profiling_options(enable_options))
init[__option_exec_profiling_options] = enable_options
if "task_trace" in enable_options or "training_trace" in enable_options:
if fp_point is None or bp_point is None:
logging.warning(
"profiling training_trace option should use with bp_point and fp_point"
)
else:
init[__option_exec_fp_point] = str(fp_point)
init[__option_exec_bp_point] = str(bp_point)
else:
init[__option_exec_profiling_options] = str("training_trace")
if auto_tune_mode is not None:
init[__auto_tune_mode] = str(auto_tune_mode)
if precision_mode is not None:
init["ge.exec.precision_mode"] = str(precision_mode)
else:
if graph_run_mode:
init["ge.exec.precision_mode"] = str("allow_fp32_to_fp16")
else:
init["ge.exec.precision_mode"] = str("force_fp16")
if enable_scope_fusion_passes is not None:
init[__option_exec_enable_scope_fusion_passes] = str(
enable_scope_fusion_passes)
init["ge.exec.enable_exception_dump"] = str(enable_exception_dump)
init_options = tf_adapter.map_string_string(init)
tf_adapter.PluginInit(init_options)