test_prim2orig.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 _prim2orig

paddle.enable_static()


# ------------------------ Test prim2orig rules ---------------------------- #
class TestAddPPrim2Orig(unittest.TestCase):
    def setUp(self):
        self.main_program = paddle.static.Program()
        self.startup_program = paddle.static.Program()
        self.layer_help = LayerHelper('TestPrim2Orig')

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

    def init_data(self):
        self.op_type = 'add_p'
        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 = {
            'Z': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}

        self.prim2orig_args = (X, Y)
        self.all_ops = ['add_p', 'elementwise_add']
        # { prim_op_output_var: orign_op_out_index }
        self.out_map = {self.output['Z']: 0}

    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,
            )

            orig_out = _prim2orig(op, *self.prim2orig_args)
            all_ops = [op.type for op in self.main_program.block(0).ops]
            self.assertEqual(sorted(all_ops), sorted(self.all_ops))
            orig_out = paddle.utils.flatten(orig_out)
            for k, v in self.out_map.items():
                self.assertEqual(k.shape, orig_out[v].shape)


class TestSubPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'sub_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['sub_p', 'elementwise_sub']
        self.out_map = {self.output['Z']: 0}


class TestMulPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'mul_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['mul_p', 'elementwise_mul']
        self.out_map = {self.output['Z']: 0}


class TestDivPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'div_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['div_p', 'elementwise_div']
        self.out_map = {self.output['Z']: 0}


class TestSqrtPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'sqrt_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['sqrt_p', 'sqrt']
        self.out_map = {self.output['Y']: 0}


class TestTanhPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'tanh_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['tanh_p', 'tanh']
        self.out_map = {self.output['Y']: 0}


class TestSinPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'sin_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['sin_p', 'sin']
        self.out_map = {self.output['Y']: 0}


class TestCosPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'cos_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['cos_p', 'cos']
        self.out_map = {self.output['Y']: 0}


class TestExpPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'exp_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['exp_p', 'exp']
        self.out_map = {self.output['Y']: 0}


class TestErfPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'erf_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['erf_p', 'erf']
        self.out_map = {self.output['Y']: 0}


class TestAbsPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'abs_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['abs_p', 'abs']
        self.out_map = {self.output['Y']: 0}


class TestLogPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'log_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['log_p', 'log']
        self.out_map = {self.output['Y']: 0}


class TestReshapePPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'reshape_p'
        X = paddle.static.data(name='X', shape=[2, 8], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'shape': [4, 4]}

        self.prim2orig_args = (X,)
        self.all_ops = ['reshape_p', 'reshape2']
        self.out_map = {self.output['Y']: 0}


class TestBroadcastPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'broadcast_p'
        X = paddle.static.data(name='X', shape=[2, 8], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'shape': [10, 2, 8]}

        self.prim2orig_args = (X,)
        self.all_ops = ['broadcast_p', 'expand_v2']
        self.out_map = {self.output['Y']: 0}


class TestTransposePPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'transpose_p'
        X = paddle.static.data(name='X', shape=[7, 8, 9, 10], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'axis': [1, 2, 0, 3]}

        self.prim2orig_args = (X,)
        self.all_ops = ['transpose_p', 'transpose2']
        self.out_map = {self.output['Y']: 0}


class TestSplitPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'split_p'
        X = paddle.static.data(name='X', shape=[3, 9, 5], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'YS': [
                self.layer_help.create_variable_for_type_inference(
                    dtype=X.dtype
                )
                for i in range(3)
            ]
        }
        self.attrs = {'num_or_sections': [2, 3, 4], 'axis': 1}

        self.prim2orig_args = (X,)
        self.all_ops = ['split_p', 'split']
        self.out_map = {
            self.output['YS'][0]: 0,
            self.output['YS'][1]: 1,
            self.output['YS'][2]: 2,
        }


class TestConcatPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'concat_p'
        X = paddle.static.data(name='X', shape=[3, 9, 5], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[2, 9, 5], dtype='float64')
        Z = paddle.static.data(name='Z', shape=[1, 9, 5], dtype='float64')

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

        self.prim2orig_args = ((X, Y, Z),)
        self.all_ops = ['concat_p', 'concat']
        self.out_map = {self.output['Y']: 0}


class TestReducePPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'reduce_sum_p'
        X = paddle.static.data(name='X', shape=[3, 9, 5], dtype='float64')

        self.input = {'X': X}
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'axis': [1], 'keepdim': True}

        self.prim2orig_args = (X,)
        self.all_ops = ['reduce_sum_p', 'reduce_sum']
        self.out_map = {self.output['Y']: 0}


class TestMatmulPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'matmul_p'
        X = paddle.static.data(name='X', shape=[9, 5], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[5, 9], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['matmul_p', 'matmul_v2']
        self.out_map = {self.output['Z']: 0}


class TestSliceSelectPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'slice_select_p'
        X = paddle.static.data(name='X', shape=[9, 5], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'axis': [0], 'starts': [1], 'ends': [8], 'strides': [2]}

        self.prim2orig_args = (X,)
        self.all_ops = ['slice_select_p', 'strided_slice']
        self.out_map = {self.output['Y']: 0}


class TestSliceAssignPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'slice_assign_p'
        X = paddle.static.data(name='X', shape=[9, 5], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[9, 3], dtype='float64')

        self.input = {'X': X, 'Y': Y}
        self.output = {
            'Z': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'axis': [1], 'starts': [0], 'ends': [3], 'strides': [1]}

        self.prim2orig_args = (X, Y)
        self.all_ops = ['slice_assign_p', 'assign', 'set_value']
        self.out_map = {self.output['Z']: 0}


class TestGatherPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'gather_p'
        X = paddle.static.data(name='X', shape=[9, 5], dtype='float64')
        IndexTensor = paddle.static.data(
            name='IndexTensor', shape=[3], dtype='int32'
        )

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

        self.prim2orig_args = (
            IndexTensor,
            X,
        )
        self.all_ops = ['gather_p', 'gather']
        self.out_map = {self.output['Y']: 0}


class TestScatterAddPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'scatter_add_p'
        X = paddle.static.data(name='X', shape=[9, 5], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[3, 5], dtype='float64')
        IndexTensor = paddle.static.data(
            name='IndexTensor', shape=[3], dtype='int32'
        )

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

        self.prim2orig_args = (IndexTensor, X, Y)
        self.all_ops = [
            'scatter_add_p',
            'fill_any_like',
            'scatter',
            'elementwise_add',
        ]
        self.out_map = {self.output['Z']: 0}


class TestFillConstantPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'fill_constant_p'

        self.input = {}
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                paddle.int32
            )
        }
        self.attrs = {'value': 10, 'shape': [5, 5], 'dtype': paddle.int32}

        self.prim2orig_args = ()
        self.all_ops = ['fill_constant_p', 'fill_constant']
        self.out_map = {self.output['Y']: 0}


class TestSelectPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'select_p'
        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 = {
            'Z': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {}
        self.prim2orig_args = (Cond, X, Y)
        self.all_ops = ['select_p', 'where']
        self.out_map = {self.output['Z']: 0}


class TestEqPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'eq_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['eq_p', 'equal']
        self.out_map = {self.output['Z']: 0}


class TestNePPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'ne_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['ne_p', 'not_equal']
        self.out_map = {self.output['Z']: 0}


class TestGtPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'gt_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['gt_p', 'greater_than']
        self.out_map = {self.output['Z']: 0}


class TestGePPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'ge_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['ge_p', 'greater_equal']
        self.out_map = {self.output['Z']: 0}


class TestPowPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'pow_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['pow_p', 'elementwise_pow']
        self.out_map = {self.output['Z']: 0}


class TestMaxPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'max_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')
        Y = paddle.static.data(name='Y', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X, Y)
        self.all_ops = ['max_p', 'elementwise_max']
        self.out_map = {self.output['Z']: 0}


class TestBernoulliPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'bernoulli_p'

        self.input = {}
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.float64
            )
        }
        self.attrs = {'shape': [7, 8], 'dtype': paddle.float64, 'p': 0.5}

        self.prim2orig_args = ()
        self.all_ops = ['bernoulli_p', 'fill_constant', 'bernoulli']
        self.out_map = {self.output['Y']: 0}


class TestCastPPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'cast_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

        self.input = {
            'X': X,
        }
        self.output = {
            'Y': self.layer_help.create_variable_for_type_inference(
                dtype=X.dtype
            )
        }
        self.attrs = {'dtype': paddle.int64}

        self.prim2orig_args = (X,)
        self.all_ops = ['cast_p', 'cast']
        self.out_map = {self.output['Y']: 0}


class TestRsqrtPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'rsqrt_p'
        X = paddle.static.data(name='X', shape=[7, 8], dtype='float64')

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

        self.prim2orig_args = (X,)
        self.all_ops = ['rsqrt_p', 'rsqrt']
        self.out_map = {self.output['Y']: 0}


class TestUniformRandomPrim2Orig(TestAddPPrim2Orig):
    def init_data(self):
        self.op_type = 'uniform_random_p'

        self.input = {}
        self.output = {
            'Out': self.layer_help.create_variable_for_type_inference(
                dtype=paddle.float64
            )
        }
        self.attrs = {
            'shape': [1, 2, 3],
            'min': -1.0,
            'max': 1.0,
            'seed': 0,
            'dtype': paddle.float64,
        }

        self.prim2orig_args = ()
        self.all_ops = ['uniform_random_p', 'uniform_random']
        self.out_map = {self.output['Out']: 0}


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