未验证 提交 ae859b7d 编写于 作者: T Thomas Young 提交者: GitHub

Merge branch 'develop' into fix_async_while

...@@ -30,7 +30,7 @@ message( "WITH_GPU = ${WITH_GPU}") ...@@ -30,7 +30,7 @@ message( "WITH_GPU = ${WITH_GPU}")
# Paddle Version should be one of: # Paddle Version should be one of:
# latest: latest develop build # latest: latest develop build
# version number like 1.5.2 # version number like 1.5.2
SET(PADDLE_VERSION "2.2.0") SET(PADDLE_VERSION "2.2.2")
if (WITH_GPU) if (WITH_GPU)
message("CUDA: ${CUDA_VERSION}, CUDNN_MAJOR_VERSION: ${CUDNN_MAJOR_VERSION}") message("CUDA: ${CUDA_VERSION}, CUDNN_MAJOR_VERSION: ${CUDNN_MAJOR_VERSION}")
# cuda 11.0 is not supported, 11.2 would be added. # cuda 11.0 is not supported, 11.2 would be added.
......
...@@ -23,10 +23,13 @@ from .proto import general_model_config_pb2 as m_config ...@@ -23,10 +23,13 @@ from .proto import general_model_config_pb2 as m_config
import paddle.inference as paddle_infer import paddle.inference as paddle_infer
import logging import logging
import glob import glob
from paddle_serving_server.pipeline.error_catch import ErrorCatch, CustomException, CustomExceptionCode, ParamChecker, ParamVerify
check_dynamic_shape_info=ParamVerify.check_dynamic_shape_info
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s") logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("LocalPredictor") logger = logging.getLogger("LocalPredictor")
logger.setLevel(logging.INFO) logger.setLevel(logging.INFO)
from paddle_serving_server.util import kill_stop_process_by_pid
precision_map = { precision_map = {
'int8': paddle_infer.PrecisionType.Int8, 'int8': paddle_infer.PrecisionType.Int8,
...@@ -157,12 +160,12 @@ class LocalPredictor(object): ...@@ -157,12 +160,12 @@ class LocalPredictor(object):
"use_trt:{}, use_lite:{}, use_xpu:{}, precision:{}, use_calib:{}, " "use_trt:{}, use_lite:{}, use_xpu:{}, precision:{}, use_calib:{}, "
"use_mkldnn:{}, mkldnn_cache_capacity:{}, mkldnn_op_list:{}, " "use_mkldnn:{}, mkldnn_cache_capacity:{}, mkldnn_op_list:{}, "
"mkldnn_bf16_op_list:{}, use_feed_fetch_ops:{}, " "mkldnn_bf16_op_list:{}, use_feed_fetch_ops:{}, "
"use_ascend_cl:{}, min_subgraph_size:{}, dynamic_shape_info:{}".format( "use_ascend_cl:{}, min_subgraph_size:{}, dynamic_shape_info:{}".
model_path, use_gpu, gpu_id, use_profile, thread_num, mem_optim, format(model_path, use_gpu, gpu_id, use_profile, thread_num,
ir_optim, use_trt, use_lite, use_xpu, precision, use_calib, mem_optim, ir_optim, use_trt, use_lite, use_xpu, precision,
use_mkldnn, mkldnn_cache_capacity, mkldnn_op_list, use_calib, use_mkldnn, mkldnn_cache_capacity, mkldnn_op_list,
mkldnn_bf16_op_list, use_feed_fetch_ops, use_ascend_cl, mkldnn_bf16_op_list, use_feed_fetch_ops, use_ascend_cl,
min_subgraph_size, dynamic_shape_info)) min_subgraph_size, dynamic_shape_info))
self.feed_names_ = [var.alias_name for var in model_conf.feed_var] self.feed_names_ = [var.alias_name for var in model_conf.feed_var]
self.fetch_names_ = [var.alias_name for var in model_conf.fetch_var] self.fetch_names_ = [var.alias_name for var in model_conf.fetch_var]
...@@ -223,11 +226,20 @@ class LocalPredictor(object): ...@@ -223,11 +226,20 @@ class LocalPredictor(object):
use_static=False, use_static=False,
use_calib_mode=use_calib) use_calib_mode=use_calib)
@ErrorCatch
@ParamChecker
def dynamic_shape_info_helper(dynamic_shape_info:lambda dynamic_shape_info: check_dynamic_shape_info(dynamic_shape_info)):
pass
_, resp = dynamic_shape_info_helper(dynamic_shape_info)
if resp.err_no != CustomExceptionCode.OK.value:
print("dynamic_shape_info configure error, it should contain [min_input_shape', 'max_input_shape', 'opt_input_shape' {}".format(resp.err_msg))
kill_stop_process_by_pid("kill", os.getpgid(os.getpid()))
if len(dynamic_shape_info): if len(dynamic_shape_info):
config.set_trt_dynamic_shape_info( config.set_trt_dynamic_shape_info(
dynamic_shape_info['min_input_shape'], dynamic_shape_info['min_input_shape'],
dynamic_shape_info['max_input_shape'], dynamic_shape_info['max_input_shape'],
dynamic_shape_info['opt_input_shape']) dynamic_shape_info['opt_input_shape'])
# set lite # set lite
if use_lite: if use_lite:
config.enable_lite_engine( config.enable_lite_engine(
...@@ -269,7 +281,18 @@ class LocalPredictor(object): ...@@ -269,7 +281,18 @@ class LocalPredictor(object):
if mkldnn_bf16_op_list is not None: if mkldnn_bf16_op_list is not None:
config.set_bfloat16_op(mkldnn_bf16_op_list) config.set_bfloat16_op(mkldnn_bf16_op_list)
self.predictor = paddle_infer.create_predictor(config) @ErrorCatch
def create_predictor_check(config):
predictor = paddle_infer.create_predictor(config)
return predictor
predictor, resp = create_predictor_check(config)
if resp.err_no != CustomExceptionCode.OK.value:
logger.critical(
"failed to create predictor: {}".format(resp.err_msg),
exc_info=False)
print("failed to create predictor: {}".format(resp.err_msg))
kill_stop_process_by_pid("kill", os.getpgid(os.getpid()))
self.predictor = predictor
def predict(self, feed=None, fetch=None, batch=False, log_id=0): def predict(self, feed=None, fetch=None, batch=False, log_id=0):
""" """
...@@ -315,7 +338,8 @@ class LocalPredictor(object): ...@@ -315,7 +338,8 @@ class LocalPredictor(object):
# Assemble the input data of paddle predictor, and filter invalid inputs. # Assemble the input data of paddle predictor, and filter invalid inputs.
input_names = self.predictor.get_input_names() input_names = self.predictor.get_input_names()
for name in input_names: for name in input_names:
if isinstance(feed[name], list): if isinstance(feed[name], list) and not isinstance(feed[name][0],
str):
feed[name] = np.array(feed[name]).reshape(self.feed_shapes_[ feed[name] = np.array(feed[name]).reshape(self.feed_shapes_[
name]) name])
if self.feed_types_[name] == 0: if self.feed_types_[name] == 0:
...@@ -342,6 +366,9 @@ class LocalPredictor(object): ...@@ -342,6 +366,9 @@ class LocalPredictor(object):
feed[name] = feed[name].astype("complex64") feed[name] = feed[name].astype("complex64")
elif self.feed_types_[name] == 11: elif self.feed_types_[name] == 11:
feed[name] = feed[name].astype("complex128") feed[name] = feed[name].astype("complex128")
elif isinstance(feed[name], list) and isinstance(feed[name][0],
str):
pass
else: else:
raise ValueError("local predictor receives wrong data type") raise ValueError("local predictor receives wrong data type")
......
...@@ -34,6 +34,7 @@ from .error_catch import CustomExceptionCode as ChannelDataErrcode ...@@ -34,6 +34,7 @@ from .error_catch import CustomExceptionCode as ChannelDataErrcode
_LOGGER = logging.getLogger(__name__) _LOGGER = logging.getLogger(__name__)
class ChannelDataType(enum.Enum): class ChannelDataType(enum.Enum):
""" """
Channel data type Channel data type
...@@ -167,7 +168,8 @@ class ChannelData(object): ...@@ -167,7 +168,8 @@ class ChannelData(object):
elif isinstance(npdata, dict): elif isinstance(npdata, dict):
# batch_size = 1 # batch_size = 1
for _, value in npdata.items(): for _, value in npdata.items():
if not isinstance(value, np.ndarray): if not isinstance(value, np.ndarray) and not (isinstance(
value, list) and isinstance(value[0], str)):
error_code = ChannelDataErrcode.TYPE_ERROR.value error_code = ChannelDataErrcode.TYPE_ERROR.value
error_info = "Failed to check data: the value " \ error_info = "Failed to check data: the value " \
"of data must be np.ndarray, but get {}.".format( "of data must be np.ndarray, but get {}.".format(
......
...@@ -227,5 +227,17 @@ class ParamVerify(object): ...@@ -227,5 +227,17 @@ class ParamVerify(object):
if key not in right_fetch_list: if key not in right_fetch_list:
return False return False
return True return True
@staticmethod
def check_dynamic_shape_info(dynamic_shape_info):
if not isinstance(dynamic_shape_info, dict):
return False
if len(dynamic_shape_info) == 0:
return True
shape_info_keys = ["min_input_shape", "max_input_shape", "opt_input_shape"]
if all(key in dynamic_shape_info for key in shape_info_keys):
return True
else:
return False
ErrorCatch = ErrorCatch() ErrorCatch = ErrorCatch()
...@@ -46,6 +46,7 @@ from .util import NameGenerator ...@@ -46,6 +46,7 @@ from .util import NameGenerator
from .profiler import UnsafeTimeProfiler as TimeProfiler from .profiler import UnsafeTimeProfiler as TimeProfiler
from . import local_service_handler from . import local_service_handler
from .pipeline_client import PipelineClient as PPClient from .pipeline_client import PipelineClient as PPClient
from paddle_serving_server.util import kill_stop_process_by_pid
_LOGGER = logging.getLogger(__name__) _LOGGER = logging.getLogger(__name__)
_op_name_gen = NameGenerator("Op") _op_name_gen = NameGenerator("Op")
...@@ -1328,7 +1329,12 @@ class Op(object): ...@@ -1328,7 +1329,12 @@ class Op(object):
# init ops # init ops
profiler = None profiler = None
try: @ErrorCatch
def check_helper(self, is_thread_op, model_config, workdir,
thread_num, device_type, devices, mem_optim, ir_optim,
precision, use_mkldnn, mkldnn_cache_capacity, mkldnn_op_list,
mkldnn_bf16_op_list, min_subgraph_size, dynamic_shape_info):
if is_thread_op == False and self.client_type == "local_predictor": if is_thread_op == False and self.client_type == "local_predictor":
self.service_handler = local_service_handler.LocalServiceHandler( self.service_handler = local_service_handler.LocalServiceHandler(
model_config=model_config, model_config=model_config,
...@@ -1354,12 +1360,21 @@ class Op(object): ...@@ -1354,12 +1360,21 @@ class Op(object):
concurrency_idx) concurrency_idx)
# check all ops initialized successfully. # check all ops initialized successfully.
profiler = self._initialize(is_thread_op, concurrency_idx) profiler = self._initialize(is_thread_op, concurrency_idx)
return profiler
except Exception as e: profiler, resp = check_helper(self, is_thread_op, model_config, workdir,
thread_num, device_type, devices, mem_optim, ir_optim,
precision, use_mkldnn, mkldnn_cache_capacity, mkldnn_op_list,
mkldnn_bf16_op_list, min_subgraph_size, dynamic_shape_info)
if resp.err_no != CustomExceptionCode.OK.value:
_LOGGER.critical( _LOGGER.critical(
"{} failed to init op: {}".format(op_info_prefix, e), "{} failed to init op: {}".format(op_info_prefix, resp.err_msg),
exc_info=True) exc_info=False)
os._exit(-1)
print("{} failed to init op: {}".format(op_info_prefix, resp.err_msg))
kill_stop_process_by_pid("kill", os.getpgid(os.getpid()))
_LOGGER.info("{} Succ init".format(op_info_prefix)) _LOGGER.info("{} Succ init".format(op_info_prefix))
batch_generator = self._auto_batching_generator( batch_generator = self._auto_batching_generator(
......
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