mindspore2022/mindspore/_akg/message.py

# Copyright 2019 Huawei Technologies Co., Ltd
#
# 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.

"""message"""
import importlib.util
import json
import json.decoder as jd
import logging
import traceback
import os.path
from pathlib import Path
import _akg.tvm
from _akg.utils import validation_check as vc_util
from _akg.utils.dsl_create import TensorUtils
from . import gpu
from . import op_build


@vc_util.check_input_type(str)
def compilewithjson(json_str):
    """compile with json."""
    try:
        kernel_info = json.loads(json_str)
    except jd.JSONDecodeError:
        logging.error(traceback.format_exc())
        return False

    op_name = kernel_info['name']
    op_func = None
    processor = 'aicore'
    if 'process' in kernel_info:
        processor = kernel_info['process']
    # get custom ops implementation first.
    if 'impl_path' in kernel_info and kernel_info['impl_path'] is not None:
        impl_path = os.path.realpath(kernel_info['impl_path'])
        if os.path.isfile(impl_path):
            custom_mod_name = Path(impl_path).resolve().stem
            mod_spec = importlib.util.spec_from_file_location(custom_mod_name, impl_path)
            custom_mod = importlib.util.module_from_spec(mod_spec)
            mod_spec.loader.exec_module(custom_mod)
            op_func = getattr(custom_mod, op_name, None)

    # get built-in ops.
    if op_func is None:
        if processor == 'cuda':
            op_func = getattr(gpu, op_name, None)

    if op_func is None:
        logging.error("this op not supported, please check op name %s", str(op_name))
        return False

    args = {}
    tsr = []
    for input_desc in kernel_info['input_desc']:
        if len(input_desc) == 1:
            tensor_shape = input_desc[0]['shape']
            tensor_shape = (1,) if not tensor_shape else tensor_shape
            vc_util.shape_dtype_max_size_check(tensor_shape)
            args[input_desc[0]['name']] = _akg.tvm.placeholder(
                shape=tensor_shape, name=input_desc[0]['tensor_name'], dtype=input_desc[0]['data_type'])
            tsr.append(args[input_desc[0]['name']])
        else:
            tmp_input = []
            for tmp_desc in input_desc:
                tensor_shape = tmp_desc['shape']
                tensor_shape = (1,) if not tensor_shape else tensor_shape
                vc_util.shape_dtype_max_size_check(tensor_shape)
                tmp_input.append(_akg.tvm.placeholder(
                    shape=tensor_shape, name=tmp_desc['tensor_name'], dtype=tmp_desc['data_type']))
            args[input_desc[0]['name']] = tmp_input
            tsr = tsr + tmp_input

    if kernel_info['attr']:
        for ext_arg in kernel_info['attr']:
            args[ext_arg['name']] = ext_arg['value']

    output = op_func(**args)

    schedule_func = None
    attrs = {}
    if isinstance(output, (list, tuple)):
        from inspect import isfunction
        tmp_outputs = []
        for elem in output:
            if isfunction(elem):
                schedule_func = elem
            elif isinstance(elem, dict):
                for key, value in elem.items():
                    if key not in attrs or not attrs[key]:
                        attrs[key] = value
            else:
                tmp_outputs.append(elem)

        output = tmp_outputs
    else:
        output = [output]


    tsr = tsr + [i for i in output if TensorUtils.is_output_value(i)]
    return op_build([op_name], output, tsr, schedule_func, processor, kernel_info['op'], attrs)