paddlepaddle--paddle/test/ir/inference/test_squared_mat_sub_fuse_pass.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 unittest
from functools import partial

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


class TestSquaredMatSubFusePass(PassAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_config(self, draw):
        transpose_X = False
        transpose_Y = False
        alpha1 = 1.0
        alpha2 = 1.0
        axis1 = draw(st.sampled_from([-1, 0]))
        place_type = draw(st.sampled_from([-1, 0]))
        has_str_value = draw(st.booleans())
        str_value = ''
        value = draw(st.floats(min_value=-10, max_value=10))
        shape = draw(st.sampled_from([[1]]))
        axis2 = draw(st.sampled_from([-1, 0]))
        input_dim = draw(st.sampled_from([32, 64]))

        def generate_input(type):
            shape_x = [32, input_dim]
            shape_y = [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_op1 = OpConfig(
            type="matmul",
            inputs={"X": ["input_data1"], "Y": ["input_data2"]},
            outputs={"Out": ["matmul1_output"]},
            attrs={
                "transpose_X": transpose_X,
                "transpose_Y": transpose_Y,
                "alpha": alpha1,
            },
        )

        square_op1 = OpConfig(
            type="square",
            inputs={"X": ["matmul1_output"]},
            outputs={"Out": ["square1_output"]},
            attrs={},
        )

        square_op2 = OpConfig(
            type="square",
            inputs={"X": ["input_data1"]},
            outputs={"Out": ["square2_output"]},
            attrs={},
        )

        square_op3 = OpConfig(
            type="square",
            inputs={"X": ["input_data2"]},
            outputs={"Out": ["square3_output"]},
            attrs={},
        )

        matmul_op2 = OpConfig(
            type="matmul",
            inputs={"X": ["square2_output"], "Y": ["square3_output"]},
            outputs={"Out": ["matmul2_output"]},
            attrs={
                "transpose_X": transpose_X,
                "transpose_Y": transpose_Y,
                "alpha": alpha2,
            },
        )

        elt_sub_op = OpConfig(
            type="elementwise_sub",
            inputs={"X": ["square1_output"], "Y": ["matmul2_output"]},
            outputs={"Out": ["sub_out"]},
            attrs={"axis": axis1},
        )

        if has_str_value:
            fill_constant_op = OpConfig(
                type="fill_constant",
                inputs={},
                outputs={"Out": ["constant_out"]},
                attrs={
                    "dtype": 5,
                    "place_type": place_type,
                    "str_value": str_value,
                    "value": value,
                    "shape": shape,
                },
            )
        else:
            fill_constant_op = OpConfig(
                type="fill_constant",
                inputs={},
                outputs={"Out": ["constant_out"]},
                attrs={
                    "dtype": 5,
                    "place_type": place_type,
                    "value": value,
                    "shape": shape,
                },
            )

        elt_mul_op = OpConfig(
            type="elementwise_mul",
            inputs={"X": ["sub_out"], "Y": ["constant_out"]},
            outputs={"Out": ["mul_out"]},
            attrs={"axis": axis2},
        )

        model_net = [
            matmul_op1,
            square_op1,
            square_op2,
            square_op3,
            matmul_op2,
            elt_sub_op,
            fill_constant_op,
            elt_mul_op,
        ]

        program_config = ProgramConfig(
            ops=model_net,
            weights={},
            inputs={
                "input_data1": TensorConfig(
                    data_gen=partial(generate_input, "x")
                ),
                "input_data2": TensorConfig(
                    data_gen=partial(generate_input, "y")
                ),
            },
            outputs=["mul_out"],
        )

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config()
        yield config, ["fusion_squared_mat_sub"], (1e-5, 1e-5)

    def test(self):
        self.run_and_statistics(
            quant=False, passes=["squared_mat_sub_fuse_pass"]
        )


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