test_orig2prim.py

# Copyright (c) 2022 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

import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.incubate.autograd.primrules import _orig2prim

paddle.enable_static()


# ----------------------- Test orig2prim rules ---------------------------- #
class TestElementWiseAddOrig2Prim(unittest.TestCase):
    def setUp(self):
        self.main_program = paddle.static.Program()
        self.startup_program = paddle.static.Program()
        self.layer_help = LayerHelper('TestOrig2Prim')

        with paddle.static.program_guard(
            self.main_program, self.startup_program
        ):
            self.init_data()

    def init_data(self):
        self.op_type = 'elementwise_add'
        X = paddle.static.data(name='X', shape=[2, 2], dtype='float')
        Y = paddle.static.data(name='Y', shape=[2, 2], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X, Y)
        self.all_ops = ['elementwise_add', 'add_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}

    def test_op(self):
        with paddle.static.program_guard(
            self.main_program, self.startup_program
        ):
            op = self.layer_help.append_op(
                type=self.op_type,
                inputs=self.input,
                outputs=self.output,
                attrs=self.attrs,
            )

            prim_out = _orig2prim(op, *self.orig2prim_args)
            all_ops = [op.type for op in self.main_program.block(0).ops]

            self.assertEqual(sorted(all_ops), sorted(self.all_ops))
            prim_out = paddle.utils.flatten(prim_out)
            for k, v in self.out_map.items():
                self.assertEqual(prim_out[k].shape, v.shape)


class TestSqrtOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'sqrt'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['sqrt', 'sqrt_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestElementWiseMulOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'elementwise_mul'
        X = paddle.static.data(name='X', shape=[8, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[8, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X, Y)
        self.all_ops = ['elementwise_mul', 'mul_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestElementWiseDivOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'elementwise_div'
        X = paddle.static.data(name='X', shape=[8, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[8, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X, Y)
        self.all_ops = ['elementwise_div', 'div_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestMatmulV2Orig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'matmul_v2'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')
        Y = paddle.static.data(name='Y', shape=[4, 3], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'trans_x': True, 'trans_y': True}

        self.orig2prim_args = (X, Y)
        self.all_ops = ['matmul_v2', 'transpose_p', 'transpose_p', 'matmul_p']
        self.out_map = {0: self.output['Out']}


class TestTanhOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'tanh'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['tanh', 'tanh_p']
        self.out_map = {0: self.output['Out']}


class TestSinOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'sin'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['sin', 'sin_p']
        self.out_map = {0: self.output['Out']}


class TestCosOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'cos'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['cos', 'cos_p']
        self.out_map = {0: self.output['Out']}


class TestExpOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'exp'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['exp', 'exp_p']
        self.out_map = {0: self.output['Out']}


class TestErfOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'erf'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['erf', 'erf_p']
        self.out_map = {0: self.output['Out']}


class TestAbsOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'abs'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['abs', 'abs_p']
        self.out_map = {0: self.output['Out']}


class TestLogOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'log'
        X = paddle.static.data(name='X', shape=[3, 4], dtype='float')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['log', 'log_p']
        self.out_map = {0: self.output['Out']}


class TestReshape2Orig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'reshape2'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': X,
            'XShape': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            ),
        }
        self.attrs = {'shape': [6, 5]}

        self.orig2prim_args = (
            None,
            None,
            X,
        )
        self.all_ops = ['reshape2', 'reshape_p', 'fill_constant_p']
        # Do not checke XShape
        self.out_map = {0: self.output['Out']}


class TestConcatOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'concat'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')
        Y = paddle.static.data(name='Y', shape=[3, 6], dtype='int64')

        self.input = {
            'X': [X, Y],
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'axis': 0}

        self.orig2prim_args = (
            None,
            (X, Y),
        )
        self.all_ops = ['concat', 'concat_p']
        self.out_map = {0: self.output['Out']}


class TestSliceOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'slice'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')

        self.input = {
            'Input': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {
            'axes': [0],
            'starts': [1],
            'ends': [4],
        }

        self.orig2prim_args = (None, None, X, None, None)
        self.all_ops = ['slice', 'slice_select_p']
        self.out_map = {0: self.output['Out']}


class TestFillZerosLikeOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'fill_zeros_like'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['fill_zeros_like', 'fill_constant_p']
        self.out_map = {0: self.output['Out']}


class TestFillAnyLikeOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'fill_any_like'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['fill_any_like', 'fill_constant_p']
        self.out_map = {0: self.output['Out']}


class TestFillAnyLikeOrig2Prim2(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'fill_any_like'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'dtype': paddle.float32, 'value': 5}

        self.orig2prim_args = (X,)
        self.all_ops = ['fill_any_like', 'fill_constant_p']
        self.out_map = {0: self.output['Out']}


class TestSumOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'sum'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')
        Y = paddle.static.data(name='Y', shape=[5, 6], dtype='int64')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = ((X, Y),)
        self.all_ops = ['sum', 'add_p']
        self.out_map = {0: self.output['Out']}


class TestPNormOrig2Prim1(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'p_norm'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {
            'porder': 1,
            'asvector': True,
        }

        self.orig2prim_args = (X,)
        self.all_ops = ['p_norm', 'reshape_p', 'abs_p', 'reduce_sum_p']
        self.out_map = {0: self.output['Out']}


class TestPNormOrig2Prim2(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'p_norm'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {
            'porder': 2,
            'asvector': True,
        }

        self.orig2prim_args = (X,)
        self.all_ops = [
            'p_norm',
            'reshape_p',
            'sqrt_p',
            'reduce_sum_p',
            'mul_p',
        ]
        self.out_map = {0: self.output['Out']}


class TestIndexSelectOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'index_select'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int64')
        Index = paddle.static.data(name='Index', shape=[2], dtype='int32')

        self.input = {'X': X, 'Index': Index}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {
            'dim': 0,
        }

        self.orig2prim_args = (
            Index,
            X,
        )
        self.all_ops = ['index_select', 'gather_p']
        self.out_map = {0: self.output['Out']}


class TestElementwiseSubOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'elementwise_sub'
        X = paddle.static.data(name='X', shape=[5, 6], dtype='int32')
        Y = paddle.static.data(name='Y', shape=[6], dtype='int32')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {
            'dim': 0,
        }

        self.orig2prim_args = (
            X,
            Y,
        )
        self.all_ops = ['elementwise_sub', 'broadcast_p', 'sub_p']
        self.out_map = {0: self.output['Out']}


class TestScaleOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'scale'
        X = paddle.static.data(name='X', shape=[10, 7], dtype='int32')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'scale': 2.0, 'bias': 1.0, 'bias_after_scale': True}

        self.orig2prim_args = (
            None,
            X,
        )
        self.all_ops = [
            'scale',
            'fill_constant_p',
            'fill_constant_p',
            'mul_p',
            'add_p',
        ]
        self.out_map = {0: self.output['Out']}


class TestAssignOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'assign'
        X = paddle.static.data(name='X', shape=[10, 7], dtype='int32')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['assign', 'fill_constant_p', 'add_p']
        self.out_map = {0: self.output['Out']}


class TestWhereOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'where'
        Cond = paddle.static.data(name='Condition', shape=[5, 6], dtype='bool')
        X = paddle.static.data(name='X', shape=[5, 6], dtype='float32')
        Y = paddle.static.data(name='Y', shape=[5, 6], dtype='float32')

        self.input = {'Condition': Cond, 'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}
        self.orig2prim_args = (Cond, X, Y)
        self.all_ops = ['where', 'select_p']
        self.out_map = {0: self.output['Out']}


class TestEqualOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'equal'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[5, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype='bool'
            )
        }
        self.attrs = {}
        self.orig2prim_args = (X, Y)
        self.all_ops = ['equal', 'eq_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestNeOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'not_equal'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[5, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype='bool'
            )
        }
        self.attrs = {}
        self.orig2prim_args = (X, Y)
        self.all_ops = ['not_equal', 'ne_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestGtOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'greater_than'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[5, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype='bool'
            )
        }
        self.attrs = {}
        self.orig2prim_args = (X, Y)
        self.all_ops = ['greater_than', 'gt_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestGeOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'greater_equal'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[5, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype='bool'
            )
        }
        self.attrs = {}
        self.orig2prim_args = (X, Y)
        self.all_ops = ['greater_equal', 'ge_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestPowOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'elementwise_pow'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[5, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X, Y)
        self.all_ops = ['elementwise_pow', 'pow_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestMaxOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'elementwise_max'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')
        Y = paddle.static.data(name='Y', shape=[5, 8], dtype='float')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X, Y)
        self.all_ops = ['elementwise_max', 'max_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestGeluOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'gelu'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'approximate': False}

        self.orig2prim_args = (X,)
        self.all_ops = [
            'gelu',
            'add_p',
            'erf_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'mul_p',
            'mul_p',
            'mul_p',
        ]
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestGeluApproximateOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'gelu'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'approximate': True}

        self.orig2prim_args = (X,)
        self.all_ops = [
            'add_p',
            'add_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'gelu',
            'mul_p',
            'mul_p',
            'mul_p',
            'mul_p',
            'pow_p',
            'tanh_p',
        ]
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestDropoutOrig2PrimCase1(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'dropout'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Mask': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.uint8
            ),
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            ),
        }
        self.attrs = {
            'dropout_prob': 0.5,
            'is_test': False,
            'dropout_implementation': 'upscale_in_train',
        }

        self.orig2prim_args = (None, X)
        self.all_ops = [
            'bernoulli_p',
            'mul_p',
            'fill_constant_p',
            'div_p',
            'cast_p',
            'dropout',
        ]
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestDropoutOrig2PrimCase2(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'dropout'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Mask': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.uint8
            ),
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            ),
        }
        self.attrs = {
            'dropout_prob': 0.5,
            'is_test': False,
            'dropout_implementation': 'downgrade_in_infer',
        }

        self.orig2prim_args = (None, X)
        self.all_ops = ['bernoulli_p', 'mul_p', 'cast_p', 'dropout']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestDropoutOrig2PrimCase3(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'dropout'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Mask': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.uint8
            ),
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            ),
        }
        self.attrs = {
            'dropout_prob': 0.5,
            'is_test': True,
            'dropout_implementation': 'upscale_in_train',
        }

        self.orig2prim_args = (None, X)
        self.all_ops = ['bernoulli_p', 'cast_p', 'dropout']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestDropoutOrig2PrimCase4(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'dropout'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Mask': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.uint8
            ),
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            ),
        }
        self.attrs = {
            'dropout_prob': 0.5,
            'is_test': True,
            'dropout_implementation': 'downgrade_in_infer',
        }

        self.orig2prim_args = (None, X)
        self.all_ops = [
            'bernoulli_p',
            'fill_constant_p',
            'mul_p',
            'cast_p',
            'dropout',
        ]
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Mask'], 1: self.output['Out']}


class TestReduceSumOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'reduce_sum'

        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'axis': [0, 1], 'keep_dim': False}

        self.orig2prim_args = (X,)
        self.all_ops = ['reduce_sum', 'reduce_sum_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestReduceMeanOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'reduce_mean'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'axis': [0, 1], 'keep_dim': False}

        self.orig2prim_args = (X,)
        self.all_ops = [
            'reduce_mean',
            'reduce_sum_p',
            'fill_constant_p',
            'div_p',
        ]
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestSizeOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'size'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'Input': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.int64
            )
        }
        self.attrs = {}
        self.orig2prim_args = (X,)
        self.all_ops = ['size', 'fill_constant_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestCastOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'cast'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'in_dtype': X.dtype, 'out_dtype': paddle.float64}
        self.orig2prim_args = (X,)
        self.all_ops = ['cast', 'cast_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestPowScalarOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'pow'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'factor': 2.0}
        self.orig2prim_args = (None, X)
        self.all_ops = ['pow', 'pow_p', 'fill_constant_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestSquareOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'square'
        X = paddle.static.data(name='X', shape=[5, 8], dtype='float')

        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}
        self.orig2prim_args = (X,)
        self.all_ops = ['square', 'pow_p', 'fill_constant_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestRSqrtOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'rsqrt'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.orig2prim_args = (X,)
        self.all_ops = ['rsqrt', 'rsqrt_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestBatchnormOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'batch_norm'
        x = paddle.static.data(name='X', shape=[5, 8], dtype='float')
        m = paddle.static.data(name='Mean', shape=[8], dtype='float')
        v = paddle.static.data(name='Variance', shape=[8], dtype='float')
        w = paddle.static.data(name='Scale', shape=[8], dtype='float')
        b = paddle.static.data(name='Bias', shape=[8], dtype='float')

        self.input = {
            "X": [x],
            "Scale": [w],
            "Bias": [b],
            "Mean": [m],
            "Variance": [v],
        }
        saved_variance = self.layer_help.create_variable_for_type_inference(
            dtype=x.dtype, stop_gradient=True
        )
        batch_norm_out = self.layer_help.create_variable_for_type_inference(
            x.dtype
        )
        saved_mean = self.layer_help.create_variable_for_type_inference(
            dtype=x.dtype, stop_gradient=True
        )
        self.output = {
            "Y": [batch_norm_out],
            "MeanOut": [m],
            "VarianceOut": [v],
            "SavedMean": [saved_mean],
            "SavedVariance": [saved_variance],
        }

        self.attrs = {
            "momentum": 0.9,
            "epsilon": 1e-5,
            "is_test": False,
            "data_layout": 'NCHW',
            "use_mkldnn": False,
            "fuse_with_relu": False,
            "use_global_stats": False,
            "trainable_statistics": False,
        }
        self.orig2prim_args = (b, m, None, w, v, x)
        self.all_ops = [
            'add_p',
            'add_p',
            'add_p',
            'add_p',
            'batch_norm',
            'broadcast_p',
            'broadcast_p',
            'broadcast_p',
            'broadcast_p',
            'broadcast_p',
            'div_p',
            'div_p',
            'div_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'fill_constant_p',
            'mul_p',
            'mul_p',
            'mul_p',
            'mul_p',
            'mul_p',
            'pow_p',
            'reduce_sum_p',
            'reduce_sum_p',
            'reshape_p',
            'reshape_p',
            'reshape_p',
            'reshape_p',
            'sqrt_p',
            'sub_p',
            'sub_p',
            'sub_p',
            'sub_p',
        ]
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {}


class TestFillConstantOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'fill_constant'

        self.attrs = {'value': 1.0, 'shape': (2, 3), 'dtype': paddle.float32}
        self.input = {}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.float32
            )
        }

        self.orig2prim_args = (None, None, None)
        self.all_ops = ['fill_constant', 'fill_constant_p']
        # { prim_op_output_index: orig_op_output_var }
        self.out_map = {0: self.output['Out']}


class TestUniformRandomOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'uniform_random'
        self.input = {}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.float32
            )
        }
        self.attrs = {'shape': [1, 2]}

        self.orig2prim_args = (None, None)
        self.all_ops = ['uniform_random', 'uniform_random_p']
        self.out_map = {0: self.output['Out']}


class TestSigmoidOrig2Prim(TestElementWiseAddOrig2Prim):
    def init_data(self):
        self.op_type = 'sigmoid'
        X = paddle.static.data(name='X', shape=[3], dtype='float32')

        self.attrs = {}
        self.input = {'X': X}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.float32
            )
        }

        self.orig2prim_args = (X,)
        self.all_ops = [
            'sigmoid',
            'div_p',
            'fill_constant_p',
            'add_p',
            'fill_constant_p',
            'exp_p',
            'fill_constant_p',
            'sub_p',
        ]
        self.out_map = {0: self.output['Out']}


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