# coding:utf-8
# Copyright (c) 2021 PaddlePaddle 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.
import abc
import math
import os
from abc import abstractmethod
from multiprocessing import cpu_count
import paddle
from paddle.dataset.common import md5file
from ..utils.env import PPNLP_HOME
from ..utils.log import logger
from .utils import cut_chinese_sent, download_check, download_file, dygraph_mode_guard
[文档]class Task(metaclass=abc.ABCMeta):
"""
The meta classs of task in Taskflow. The meta class has the five abstract function,
the subclass need to inherit from the meta class.
Args:
task(string): The name of task.
model(string): The model name in the task.
kwargs (dict, optional): Additional keyword arguments passed along to the specific task.
"""
def __init__(self, model, task, priority_path=None, **kwargs):
self.model = model
self.is_static_model = kwargs.get("is_static_model", False)
self.task = task
self.kwargs = kwargs
self._priority_path = priority_path
self._usage = ""
# The dygraph model instance
self._model = None
# The static model instance
self._input_spec = None
self._config = None
self._init_class = None
self._custom_model = False
self._param_updated = False
self._num_threads = self.kwargs["num_threads"] if "num_threads" in self.kwargs else math.ceil(cpu_count() / 2)
self._infer_precision = self.kwargs["precision"] if "precision" in self.kwargs else "fp32"
# Default to use Paddle Inference
self._predictor_type = "paddle-inference"
# The root directory for storing Taskflow related files, default to ~/.paddlenlp.
self._home_path = self.kwargs["home_path"] if "home_path" in self.kwargs else PPNLP_HOME
self._task_flag = self.kwargs["task_flag"] if "task_flag" in self.kwargs else self.model
self.from_hf_hub = kwargs.pop("from_hf_hub", False)
# Add mode flag for onnx output path redirection
self.export_type = None
if "task_path" in self.kwargs:
self._task_path = self.kwargs["task_path"]
self._custom_model = True
elif self._priority_path:
self._task_path = os.path.join(self._home_path, "taskflow", self._priority_path)
else:
self._task_path = os.path.join(self._home_path, "taskflow", self.task, self.model)
if self.is_static_model:
self._static_model_name = self._get_static_model_name()
if not self.from_hf_hub:
download_check(self._task_flag)
@abstractmethod
def _construct_model(self, model):
"""
Construct the inference model for the predictor.
"""
@abstractmethod
def _construct_tokenizer(self, model):
"""
Construct the tokenizer for the predictor.
"""
@abstractmethod
def _preprocess(self, inputs, padding=True, add_special_tokens=True):
"""
Transform the raw text to the model inputs, two steps involved:
1) Transform the raw text to token ids.
2) Generate the other model inputs from the raw text and token ids.
"""
@abstractmethod
def _run_model(self, inputs, **kwargs):
"""
Run the task model from the outputs of the `_tokenize` function.
"""
@abstractmethod
def _postprocess(self, inputs):
"""
The model output is the logits and pros, this function will convert the model output to raw text.
"""
@abstractmethod
def _construct_input_spec(self):
"""
Construct the input spec for the convert dygraph model to static model.
"""
def _get_static_model_name(self):
names = []
for file_name in os.listdir(self._task_path):
if ".pdmodel" in file_name:
names.append(file_name[:-8])
if len(names) == 0:
raise IOError(f"{self._task_path} should include '.pdmodel' file.")
if len(names) > 1:
logger.warning(f"{self._task_path} includes more than one '.pdmodel' file.")
return names[0]
def _check_task_files(self):
"""
Check files required by the task.
"""
for file_id, file_name in self.resource_files_names.items():
if self.task in ["information_extraction"]:
dygraph_file = ["model_state.pdparams"]
else:
dygraph_file = ["model_state.pdparams", "config.json"]
if self.is_static_model and file_name in dygraph_file:
continue
path = os.path.join(self._task_path, file_name)
url = self.resource_files_urls[self.model][file_id][0]
md5 = self.resource_files_urls[self.model][file_id][1]
downloaded = True
if not os.path.exists(path):
downloaded = False
else:
if not self._custom_model:
if os.path.exists(path):
# Check whether the file is updated
if not md5file(path) == md5:
downloaded = False
if file_id == "model_state":
self._param_updated = True
else:
downloaded = False
if not downloaded:
download_file(self._task_path, file_name, url, md5)
def _check_predictor_type(self):
if paddle.get_device() == "cpu" and self._infer_precision == "fp16":
logger.warning("The inference precision is change to 'fp32', 'fp16' inference only takes effect on gpu.")
elif paddle.get_device().split(":", 1)[0] == "npu":
if self._infer_precision == "fp16":
logger.info("Inference on npu with fp16 precison")
else:
if self._infer_precision == "fp16":
self._predictor_type = "onnxruntime"
def _construct_ocr_engine(self, lang="ch", use_angle_cls=True):
"""
Construct the OCR engine
"""
try:
from paddleocr import PaddleOCR
except ImportError:
raise ImportError("Please install the dependencies first, pip install paddleocr")
use_gpu = False if paddle.get_device() == "cpu" else True
self._ocr = PaddleOCR(use_angle_cls=use_angle_cls, show_log=False, use_gpu=use_gpu, lang=lang)
def _construce_layout_analysis_engine(self):
"""
Construct the layout analysis engine
"""
try:
from paddleocr import PPStructure
except ImportError:
raise ImportError("Please install the dependencies first, pip install paddleocr")
self._layout_analysis_engine = PPStructure(table=False, ocr=True, show_log=False)
def _prepare_static_mode(self):
"""
Construct the input data and predictor in the PaddlePaddele static mode.
"""
if paddle.get_device() == "cpu":
self._config.disable_gpu()
self._config.enable_mkldnn()
if self._infer_precision == "int8":
# EnableMKLDNN() only works when IR optimization is enabled.
self._config.switch_ir_optim(True)
self._config.enable_mkldnn_int8()
logger.info((">>> [InferBackend] INT8 inference on CPU ..."))
elif paddle.get_device().split(":", 1)[0] == "npu":
self._config.disable_gpu()
self._config.enable_custom_device("npu", self.kwargs["device_id"])
else:
if self._infer_precision == "int8":
logger.info(
">>> [InferBackend] It is a INT8 model which is not yet supported on gpu, use FP32 to inference here ..."
)
self._config.enable_use_gpu(100, self.kwargs["device_id"])
# TODO(linjieccc): enable after fixed
self._config.delete_pass("embedding_eltwise_layernorm_fuse_pass")
self._config.delete_pass("fused_multi_transformer_encoder_pass")
self._config.set_cpu_math_library_num_threads(self._num_threads)
self._config.switch_use_feed_fetch_ops(False)
self._config.disable_glog_info()
self._config.enable_memory_optim()
# TODO(linjieccc): some temporary settings and will be remove in future
# after fixed
if self.task in ["document_intelligence", "knowledge_mining", "zero_shot_text_classification"]:
self._config.switch_ir_optim(False)
if self.model == "uie-data-distill-gp":
self._config.enable_memory_optim(False)
self.predictor = paddle.inference.create_predictor(self._config)
self.input_names = [name for name in self.predictor.get_input_names()]
self.input_handles = [self.predictor.get_input_handle(name) for name in self.predictor.get_input_names()]
self.output_handle = [self.predictor.get_output_handle(name) for name in self.predictor.get_output_names()]
def _prepare_onnx_mode(self):
try:
import onnx
import onnxruntime as ort
import paddle2onnx
from onnxconverter_common import float16
except ImportError:
logger.warning(
"The inference precision is change to 'fp32', please install the dependencies that required for 'fp16' inference, pip install onnxruntime-gpu onnx onnxconverter-common"
)
if self.export_type is None:
onnx_dir = os.path.join(self._task_path, "onnx")
else:
# Compatible multimodal model for saving image and text path
onnx_dir = os.path.join(self._task_path, "onnx", self.export_type)
if not os.path.exists(onnx_dir):
os.makedirs(onnx_dir, exist_ok=True)
float_onnx_file = os.path.join(onnx_dir, "model.onnx")
if not os.path.exists(float_onnx_file) or self._param_updated:
onnx_model = paddle2onnx.command.c_paddle_to_onnx(
model_file=self._static_model_file,
params_file=self._static_params_file,
opset_version=13,
enable_onnx_checker=True,
)
with open(float_onnx_file, "wb") as f:
f.write(onnx_model)
fp16_model_file = os.path.join(onnx_dir, "fp16_model.onnx")
if not os.path.exists(fp16_model_file) or self._param_updated:
onnx_model = onnx.load_model(float_onnx_file)
trans_model = float16.convert_float_to_float16(onnx_model, keep_io_types=True)
onnx.save_model(trans_model, fp16_model_file)
providers = [("CUDAExecutionProvider", {"device_id": self.kwargs["device_id"]})]
sess_options = ort.SessionOptions()
sess_options.intra_op_num_threads = self._num_threads
sess_options.inter_op_num_threads = self._num_threads
self.predictor = ort.InferenceSession(fp16_model_file, sess_options=sess_options, providers=providers)
assert "CUDAExecutionProvider" in self.predictor.get_providers(), (
"The environment for GPU inference is not set properly. "
"A possible cause is that you had installed both onnxruntime and onnxruntime-gpu. "
"Please run the following commands to reinstall: \n "
"1) pip uninstall -y onnxruntime onnxruntime-gpu \n 2) pip install onnxruntime-gpu"
)
self.input_handler = [i.name for i in self.predictor.get_inputs()]
def _get_inference_model(self):
"""
Return the inference program, inputs and outputs in static mode.
"""
if self._custom_model:
param_path = os.path.join(self._task_path, "model_state.pdparams")
if os.path.exists(param_path):
cache_info_path = os.path.join(self._task_path, ".cache_info")
md5 = md5file(param_path)
self._param_updated = True
if os.path.exists(cache_info_path) and open(cache_info_path).read()[:-8] == md5:
self._param_updated = False
elif self.task == "information_extraction" and self.model != "uie-data-distill-gp":
# UIE related models are moved to paddlenlp.transformers after v2.4.5
# So we convert the parameter key names for compatibility
# This check will be discard in future
fp = open(cache_info_path, "w")
fp.write(md5 + "taskflow")
fp.close()
model_state = paddle.load(param_path)
prefix_map = {"UIE": "ernie", "UIEM": "ernie_m", "UIEX": "ernie_layout"}
new_state_dict = {}
for name, param in model_state.items():
if "ernie" in name:
new_state_dict[name] = param
elif "encoder.encoder" in name:
trans_name = name.replace("encoder.encoder", prefix_map[self._init_class] + ".encoder")
new_state_dict[trans_name] = param
elif "encoder" in name:
trans_name = name.replace("encoder", prefix_map[self._init_class])
new_state_dict[trans_name] = param
else:
new_state_dict[name] = param
paddle.save(new_state_dict, param_path)
else:
fp = open(cache_info_path, "w")
fp.write(md5 + "taskflow")
fp.close()
# When the user-provided model path is already a static model, skip to_static conversion
if self.is_static_model:
self.inference_model_path = os.path.join(self._task_path, self._static_model_name)
if not os.path.exists(self.inference_model_path + ".pdmodel") or not os.path.exists(
self.inference_model_path + ".pdiparams"
):
raise IOError(
f"{self._task_path} should include {self._static_model_name + '.pdmodel'} and {self._static_model_name + '.pdiparams'} while is_static_model is True"
)
if self.paddle_quantize_model(self.inference_model_path):
self._infer_precision = "int8"
self._predictor_type = "paddle-inference"
else:
# Since 'self._task_path' is used to load the HF Hub path when 'from_hf_hub=True', we construct the static model path in a different way
_base_path = (
self._task_path
if not self.from_hf_hub
else os.path.join(self._home_path, "taskflow", self.task, self._task_path)
)
self.inference_model_path = os.path.join(_base_path, "static", "inference")
if not os.path.exists(self.inference_model_path + ".pdiparams") or self._param_updated:
with dygraph_mode_guard():
self._construct_model(self.model)
self._construct_input_spec()
self._convert_dygraph_to_static()
self._static_model_file = self.inference_model_path + ".pdmodel"
self._static_params_file = self.inference_model_path + ".pdiparams"
if paddle.get_device().split(":", 1)[0] == "npu" and self._infer_precision == "fp16":
# transform fp32 model tp fp16 model
self._static_fp16_model_file = self.inference_model_path + "-fp16.pdmodel"
self._static_fp16_params_file = self.inference_model_path + "-fp16.pdiparams"
if not os.path.exists(self._static_fp16_model_file) and not os.path.exists(self._static_fp16_params_file):
logger.info("Converting to the inference model from fp32 to fp16.")
paddle.inference.convert_to_mixed_precision(
os.path.join(self._static_model_file),
os.path.join(self._static_params_file),
os.path.join(self._static_fp16_model_file),
os.path.join(self._static_fp16_params_file),
backend=paddle.inference.PlaceType.CUSTOM,
mixed_precision=paddle.inference.PrecisionType.Half,
# Here, npu sigmoid will lead to OOM and cpu sigmoid don't support fp16.
# So, we add sigmoid to black list temporarily.
black_list={"sigmoid"},
)
logger.info(
"The inference model in fp16 precison save in the path:{}".format(self._static_fp16_model_file)
)
self._static_model_file = self._static_fp16_model_file
self._static_params_file = self._static_fp16_params_file
if self._predictor_type == "paddle-inference":
self._config = paddle.inference.Config(self._static_model_file, self._static_params_file)
self._prepare_static_mode()
else:
self._prepare_onnx_mode()
def _convert_dygraph_to_static(self):
"""
Convert the dygraph model to static model.
"""
assert (
self._model is not None
), "The dygraph model must be created before converting the dygraph model to static model."
assert (
self._input_spec is not None
), "The input spec must be created before converting the dygraph model to static model."
logger.info("Converting to the inference model cost a little time.")
static_model = paddle.jit.to_static(self._model, input_spec=self._input_spec)
paddle.jit.save(static_model, self.inference_model_path)
logger.info("The inference model save in the path:{}".format(self.inference_model_path))
def _check_input_text(self, inputs):
"""
Check whether the input text meet the requirement.
"""
inputs = inputs[0]
if isinstance(inputs, str):
if len(inputs) == 0:
raise ValueError("Invalid inputs, input text should not be empty text, please check your input.")
inputs = [inputs]
elif isinstance(inputs, list):
if not (isinstance(inputs[0], str) and len(inputs[0].strip()) > 0):
raise TypeError(
"Invalid inputs, input text should be list of str, and first element of list should not be empty text."
)
else:
raise TypeError(
"Invalid inputs, input text should be str or list of str, but type of {} found!".format(type(inputs))
)
return inputs
def _auto_splitter(self, input_texts, max_text_len, bbox_list=None, split_sentence=False):
"""
Split the raw texts automatically for model inference.
Args:
input_texts (List[str]): input raw texts.
max_text_len (int): cutting length.
bbox_list (List[float, float,float, float]): bbox for document input.
split_sentence (bool): If True, sentence-level split will be performed.
`split_sentence` will be set to False if bbox_list is not None since sentence-level split is not support for document.
return:
short_input_texts (List[str]): the short input texts for model inference.
input_mapping (dict): mapping between raw text and short input texts.
"""
input_mapping = {}
short_input_texts = []
cnt_org = 0
cnt_short = 0
with_bbox = False
if bbox_list:
with_bbox = True
short_bbox_list = []
if split_sentence:
logger.warning(
"`split_sentence` will be set to False if bbox_list is not None since sentence-level split is not support for document."
)
split_sentence = False
for idx in range(len(input_texts)):
if not split_sentence:
sens = [input_texts[idx]]
else:
sens = cut_chinese_sent(input_texts[idx])
for sen in sens:
lens = len(sen)
if lens <= max_text_len:
short_input_texts.append(sen)
if with_bbox:
short_bbox_list.append(bbox_list[idx])
input_mapping.setdefault(cnt_org, []).append(cnt_short)
cnt_short += 1
else:
temp_text_list = [sen[i : i + max_text_len] for i in range(0, lens, max_text_len)]
short_input_texts.extend(temp_text_list)
if with_bbox:
if bbox_list[idx] is not None:
temp_bbox_list = [
bbox_list[idx][i : i + max_text_len] for i in range(0, lens, max_text_len)
]
short_bbox_list.extend(temp_bbox_list)
else:
short_bbox_list.extend([None for _ in range(len(temp_text_list))])
short_idx = cnt_short
cnt_short += math.ceil(lens / max_text_len)
temp_text_id = [short_idx + i for i in range(cnt_short - short_idx)]
input_mapping.setdefault(cnt_org, []).extend(temp_text_id)
cnt_org += 1
if with_bbox:
return short_input_texts, short_bbox_list, input_mapping
else:
return short_input_texts, input_mapping
def _auto_joiner(self, short_results, input_mapping, is_dict=False):
"""
Join the short results automatically and generate the final results to match with the user inputs.
Args:
short_results (List[dict] / List[List[str]] / List[str]): input raw texts.
input_mapping (dict): cutting length.
is_dict (bool): whether the element type is dict, default to False.
return:
short_input_texts (List[str]): the short input texts for model inference.
"""
concat_results = []
elem_type = {} if is_dict else []
for k, vs in input_mapping.items():
single_results = elem_type
for v in vs:
if len(single_results) == 0:
single_results = short_results[v]
elif isinstance(elem_type, list):
single_results.extend(short_results[v])
elif isinstance(elem_type, dict):
for sk in single_results.keys():
if isinstance(single_results[sk], str):
single_results[sk] += short_results[v][sk]
else:
single_results[sk].extend(short_results[v][sk])
else:
raise ValueError(
"Invalid element type, the type of results "
"for each element should be list of dict, "
"but {} received.".format(type(single_results))
)
concat_results.append(single_results)
return concat_results
[文档] def paddle_quantize_model(self, model_path):
"""
Determine whether it is an int8 model.
"""
model = paddle.jit.load(model_path)
program = model.program()
for block in program.blocks:
for op in block.ops:
if op.type.count("quantize"):
return True
return False
[文档] def help(self):
"""
Return the usage message of the current task.
"""
print("Examples:\n{}".format(self._usage))
def __call__(self, *args, **kwargs):
inputs = self._preprocess(*args)
outputs = self._run_model(inputs, **kwargs)
results = self._postprocess(outputs)
return results
