test_onednn_matmulv2_op.py

# Copyright (c) 2023 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 unittest
from functools import partial

import hypothesis.strategies as st
import numpy as np
from auto_scan_test import OnednnAutoScanTest
from hypothesis import given
from program_config import OpConfig, ProgramConfig, TensorConfig


class TestOnednnMatmulv2Op(OnednnAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        if len(program_config.inputs["input_data2"].shape) == 4:
            if (
                program_config.inputs["input_data1"].shape[-4] != 1
                and program_config.inputs["input_data2"].shape[-4] != 1
            ):
                if (
                    program_config.inputs["input_data1"].shape[-4]
                    != program_config.inputs["input_data2"].shape[-4]
                ):
                    return False

        if (
            program_config.inputs["input_data1"].shape[-3] != 1
            and program_config.inputs["input_data2"].shape[-3] != 1
        ):
            if (
                program_config.inputs["input_data1"].shape[-3]
                != program_config.inputs["input_data2"].shape[-3]
            ):
                return False
        return True

    def sample_program_configs(self, *args, **kwargs):
        def generate_input(type, *args, **kwargs):
            transpose_X = kwargs["transpose_X"]
            transpose_Y = kwargs["transpose_Y"]
            batch_size1 = kwargs["batch_size1"]
            batch_size2 = kwargs["batch_size2"]
            channel1 = kwargs["channel1"]
            channel2 = kwargs["channel2"]
            input_dim = kwargs["input_dim"]
            y_dim_len = kwargs["y_dim_len"]
            if transpose_X and transpose_Y:
                shape_x = [batch_size1, channel1, input_dim, 32]
                if y_dim_len == 4:
                    shape_y = [batch_size2, channel2, 64, input_dim]
                elif y_dim_len == 3:
                    shape_y = [channel2, 64, input_dim]
            elif transpose_X:
                shape_x = [batch_size1, channel1, input_dim, 32]
                if y_dim_len == 4:
                    shape_y = [batch_size2, channel2, input_dim, 64]
                elif y_dim_len == 3:
                    shape_y = [channel2, input_dim, 64]
            elif transpose_Y:
                shape_x = [batch_size1, channel1, 32, input_dim]
                if y_dim_len == 4:
                    shape_y = [batch_size2, channel2, 8, input_dim]
                elif y_dim_len == 3:
                    shape_y = [channel2, 8, input_dim]
            else:
                shape_x = [batch_size1, channel1, 32, input_dim]
                if y_dim_len == 4:
                    shape_y = [batch_size2, channel2, input_dim, 16]
                elif y_dim_len == 3:
                    shape_y = [channel2, input_dim, 16]

            if type == "x":
                return np.random.random(shape_x).astype(np.float32)
            else:
                return np.random.random(shape_y).astype(np.float32)

        matmul_op = OpConfig(
            type="matmul_v2",
            inputs={"X": ["input_data1"], "Y": ["input_data2"]},
            outputs={"Out": ["matmul_output"]},
            attrs={
                "trans_x": kwargs["transpose_X"],
                "trans_y": kwargs["transpose_Y"],
            },
        )

        program_config = ProgramConfig(
            ops=[matmul_op],
            weights={},
            inputs={
                "input_data1": TensorConfig(
                    data_gen=partial(generate_input, "x", *args, **kwargs)
                ),
                "input_data2": TensorConfig(
                    data_gen=partial(generate_input, "y", *args, **kwargs)
                ),
            },
            outputs=["matmul_output"],
        )

        yield program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(use_onednn=True)
        yield config, (1e-5, 1e-5)

    @given(
        transpose_X=st.booleans(),
        transpose_Y=st.booleans(),
        y_dim_len=st.sampled_from([3, 4]),
        batch_size1=st.integers(min_value=1, max_value=4),
        batch_size2=st.integers(min_value=1, max_value=4),
        channel1=st.sampled_from([1, 16, 32, 64]),
        channel2=st.sampled_from([1, 16, 32, 64]),
        input_dim=st.sampled_from([16, 32, 64]),
    )
    def test(self, *args, **kwargs):
        self.run_test(*args, **kwargs)


if __name__ == "__main__":
    unittest.main()