paddlepaddle--paddle/test/onednn/test_elementwise_sub_onednn_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 unittest

import numpy as np
from op_test import OpTest, OpTestTool, convert_float_to_uint16

import paddle
from paddle import enable_static
from paddle.base import core
from paddle.base.framework import _current_expected_place


@OpTestTool.skip_if(
    not (isinstance(_current_expected_place(), core.CPUPlace)),
    "GPU is not supported",
)
class TestOneDNNElementwiseSubOp(OpTest):
    def setUp(self):
        self.op_type = "elementwise_sub"
        self.python_api = paddle.subtract
        self.public_python_api = paddle.subtract
        self.prim_op_type = "prim"
        self.init_dtype()
        self.init_input_output()
        self.init_kernel_type()
        self.init_axis()
        self.if_check_prim()
        self.if_enable_cinn()

        self.inputs = {
            'X': OpTest.np_dtype_to_base_dtype(self.x),
            'Y': OpTest.np_dtype_to_base_dtype(self.y),
        }
        self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
        self.outputs = {'Out': self.out}

    def init_input_output(self):
        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def test_check_grad_normal(self):
        # TODO: Enable grad check (Backward)
        # self.check_grad(['X', 'Y'], 'Out')
        pass

    def test_check_grad_ignore_x(self):
        # TODO: Enable grad check (Backward)
        # self.check_grad(['Y'], 'Out', no_grad_set=set("X"))
        pass

    def test_check_grad_ignore_y(self):
        # TODO: Enable grad check (Backward)
        # self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
        pass

    def init_axis(self):
        self.axis = -1

    def init_kernel_type(self):
        self.use_onednn = True

    def init_dtype(self):
        self.dtype = np.float32

    def test_check_output(self):
        self.check_output(check_pir=True, check_pir_onednn=True)

    def if_check_prim(self):
        self.check_prim = self.axis == -1

    def if_enable_cinn(self):
        pass


class TestOneDNNElementwiseSubOp2(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.random((100,)).astype(self.dtype)
        self.y = np.random.random((100,)).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)


class TestOneDNNElementwiseSubOp3(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)


class TestOneDNNElementwiseSubOp4(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(1, 2, [2, 3, 4, 32]).astype(self.dtype)
        self.y = np.random.uniform(1, 2, [4, 32]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)


class TestOneDNNElementwiseSubOp5(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
        self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)


class TestOneDNNElementwiseSubOp6(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(0.1, 2, [180, 1]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [1, 256]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)


class TestOneDNNElementwiseSubOp7(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(0.1, 2, [1, 180]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [256, 1]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)


class TestOneDNNElementwiseSubOp_broadcast(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
        self.y = np.random.rand(1, 10, 12, 1).astype(self.dtype)
        self.out = self.x - self.y.reshape(1, 10, 12, 1)

    def init_axis(self):
        self.axis = -1


class TestElementwiseSubOp_xsize_lessthan_ysize_sub(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.rand(10, 12).astype(self.dtype)
        self.y = np.random.rand(2, 2, 10, 12).astype(self.dtype)
        self.out = self.x - self.y

    def init_axis(self):
        self.axis = 2


class TestOneDNNElementwiseSubOpZeroDim(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.random((100,)).astype(self.dtype)
        self.y = np.array(3.0).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def test_check_grad_normal(self):
        pass

    def test_check_grad_ignore_x(self):
        pass

    def test_check_grad_ignore_y(self):
        pass


class TestOneDNNElementwiseSubOpZeroDim2(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.array(3.0).astype(self.dtype)
        self.y = np.random.random((100,)).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def test_check_grad_normal(self):
        pass

    def test_check_grad_ignore_x(self):
        pass

    def test_check_grad_ignore_y(self):
        pass


class TestOneDNNElementwiseSubOpZeroDim3(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.array(3.0).astype(self.dtype)
        self.y = np.array(3.0).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def test_check_grad_normal(self):
        pass

    def test_check_grad_ignore_x(self):
        pass

    def test_check_grad_ignore_y(self):
        pass


# Special cases for swin transformer, will ignore grad check
class TestOneDNNElementwiseSubSrcDifferentShape(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.random((6, 1, 144)).astype(self.dtype)
        self.y = np.random.random((6, 144, 1)).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def test_check_grad_normal(self):
        pass

    def test_check_grad_ignore_x(self):
        pass

    def test_check_grad_ignore_y(self):
        pass


@OpTestTool.skip_if_not_cpu_bf16()
class TestBf16(TestOneDNNElementwiseSubOp):
    def setUp(self):
        self.op_type = "elementwise_sub"
        self.init_dtype()
        self.init_input_output()
        self.init_kernel_type()
        self.init_axis()

        self.x_bf16 = convert_float_to_uint16(self.x)
        self.y_bf16 = convert_float_to_uint16(self.y)
        self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
        self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
        self.outputs = {'Out': convert_float_to_uint16(self.out)}

    def init_dtype(self):
        self.dtype = np.float32
        self.onednn_data_type = "bfloat16"

    def init_input_output(self):
        self.x = np.random.random(
            100,
        ).astype(self.dtype)
        self.y = np.random.random(
            100,
        ).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def test_check_output(self):
        self.check_output_with_place(core.CPUPlace(), check_pir_onednn=True)

    def test_check_grad_normal(self):
        self.check_grad_with_place(
            core.CPUPlace(),
            ["X", "Y"],
            "Out",
            user_defined_grads=[self.x, -self.x],
            user_defined_grad_outputs=[self.x_bf16],
            check_pir_onednn=True,
        )

    def test_check_grad_ignore_x(self):
        self.check_grad_with_place(
            core.CPUPlace(),
            ["Y"],
            "Out",
            user_defined_grads=[-self.y],
            user_defined_grad_outputs=[self.y_bf16],
            check_pir_onednn=True,
        )

    def test_check_grad_ignore_y(self):
        self.check_grad_with_place(
            core.CPUPlace(),
            ["X"],
            "Out",
            user_defined_grads=[self.x],
            user_defined_grad_outputs=[self.x_bf16],
            check_pir_onednn=True,
        )


class TestBf16Broadcasting(TestBf16):
    def init_input_output(self):
        self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
        self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def compute_reduced_gradients(self, out_grads):
        part_sum = np.add.reduceat(out_grads, [0], axis=0)
        part_sum = np.add.reduceat(part_sum, [0], axis=1)
        part_sum = np.add.reduceat(part_sum, [0], axis=2)
        return -part_sum.flatten()

    def test_check_grad_normal(self):
        self.check_grad_with_place(
            core.CPUPlace(),
            ["X", "Y"],
            "Out",
            user_defined_grads=[self.x, self.compute_reduced_gradients(self.x)],
            user_defined_grad_outputs=[self.x_bf16],
            check_pir_onednn=True,
        )

    def test_check_grad_ignore_x(self):
        self.check_grad_with_place(
            core.CPUPlace(),
            ["Y"],
            "Out",
            user_defined_grads=[self.compute_reduced_gradients(self.x)],
            user_defined_grad_outputs=[self.x_bf16],
            check_pir_onednn=True,
        )


# Comment this case since currently Paddle only supports:
# complex64, int16, float64, bfloat16, complex128, float32, int32, int64
'''class TestInt8(TestOneDNNElementwiseSubOp):
    def init_kernel_type(self):
        self.use_onednn = True
        self._cpu_only = True

    def init_dtype(self):
        self.dtype = np.int8

    def init_input_output(self):
        self.x = np.random.randint(0, 3, (12, 9)).astype("int8")
        self.y = np.random.randint(0, 3, (12, 9)).astype("int8")
        self.out = np.subtract(self.x, self.y)

    def init_scales(self):
        self.attrs['Scale_x'] = 1.0
        self.attrs['Scale_y'] = 1.0
        self.attrs['Scale_out'] = 1.0

    def test_check_output(self):
        self.init_scales()
        self.check_output(check_pir=True)

    def test_check_grad_normal(self):
        pass

    def test_check_grad_ignore_x(self):
        pass

    def test_check_grad_ignore_y(self):
        pass
'''


class TestOneDNNElementwiseSubOpZeroSize(TestOneDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(0.1, 1, [0, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [1, 17]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)

    def test_check_grad_ignore_x(self):
        self.check_grad_with_place(
            core.CPUPlace(),
            ["Y"],
            "Out",
            check_pir_onednn=True,
        )

    def test_check_grad_ignore_y(self):
        self.check_grad_with_place(
            core.CPUPlace(),
            ["X"],
            "Out",
            check_pir_onednn=True,
        )

    def if_check_prim(self):
        self.check_prim = True


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