test_assign_op.py

# 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 sys
sys.path.append('../')

from auto_scan_test import AutoScanTest, IgnoreReasons
from program_config import TensorConfig, ProgramConfig, OpConfig, CxxConfig, TargetType, PrecisionType, DataLayoutType, Place
import unittest

import hypothesis
from hypothesis import given, settings, seed, example, assume
import hypothesis.strategies as st
import argparse


class TestAssignOp(AutoScanTest):
    def __init__(self, *args, **kwargs):
        AutoScanTest.__init__(self, *args, **kwargs)
        self.enable_testing_on_place(
            TargetType.Host,
            PrecisionType.FP32,
            DataLayoutType.NCHW,
            thread=[1, 2])
        self.enable_testing_on_place(TargetType.NNAdapter, PrecisionType.FP32)
        self.enable_devices_on_nnadapter(device_names=[
            "nvidia_tensorrt", "intel_openvino", "kunlunxin_xtcl"
        ])

    def is_program_valid(self,
                         program_config: ProgramConfig,
                         predictor_config: CxxConfig) -> bool:
        return True

    def sample_program_configs(self, draw):
        in_shape = draw(
            st.lists(
                st.integers(
                    min_value=1, max_value=8), min_size=0, max_size=4))
        assign_op = OpConfig(
            type="assign",
            inputs={"X": ["input_data"]},
            outputs={"Out": ["output_data"]},
            attrs={})
        program_config = ProgramConfig(
            ops=[assign_op],
            weights={},
            inputs={"input_data": TensorConfig(shape=in_shape)},
            outputs=["output_data"])
        return program_config

    def sample_predictor_configs(self):
        return self.get_predictor_configs(), ["assign"], (1e-5, 1e-5)

    def add_ignore_pass_case(self):
        def teller1(program_config, predictor_config):
            if "nvidia_tensorrt" in self.get_nnadapter_device_name():
                in_shape = program_config.inputs["input_data"].shape
                if len(in_shape) == 1:
                    return True

        self.add_ignore_check_case(
            teller1, IgnoreReasons.PADDLELITE_NOT_SUPPORT,
            "Lite does not support 'in_shape_size == 1' on nvidia_tensorrt.")

        def _teller2(program_config, predictor_config):
            target_type = predictor_config.target()
            in_x_shape = list(program_config.inputs["input_data"].shape)
            if target_type not in [
                    TargetType.ARM, TargetType.Host, TargetType.X86,
                    TargetType.Metal, TargetType.OpenCL
            ]:
                if len(in_x_shape) == 0:
                    return True

        self.add_ignore_check_case(
            _teller2, IgnoreReasons.PADDLELITE_NOT_SUPPORT,
            "0D-tensor is not supported on this target now.")

    def test(self, *args, **kwargs):
        self.run_and_statis(quant=False, max_examples=25)


if __name__ == "__main__":
    unittest.main(argv=[''])