# Copyright (c) 2025 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


@unittest.skipIf(
    (not paddle.is_compiled_with_cuda()) or paddle.is_compiled_with_rocm(),
    'should compile with cuda.',
)
class TestMultiScaleAllocator(unittest.TestCase):
    def setUp(self):
        paddle.set_flags({'FLAGS_use_virtual_memory_auto_growth': True})
        self.GB = 1000**3
        self.MB = 1000**2
        self.cmds = [
            ["Alloc", 1 * self.GB, "0x100000000"],
            ["Alloc", 2 * self.GB, "0x100000001"],
            ["Alloc", 1 * self.GB, "0x100000002"],
            ["Alloc", 2 * self.GB, "0x100000003"],
            ["Free", 1 * self.GB, "0x100000000"],
            ["Free", 2 * self.GB, "0x100000003"],
        ]

    def allocate_cmds(self, cmds):
        params = {}
        for op, size, ptr in self.cmds:
            paddle.device.synchronize()
            paddle_reserved1 = paddle.device.cuda.memory_reserved() // self.MB

            if op == "Alloc":
                params[ptr] = paddle.randn(
                    [int(int(size) / 4)], dtype='float32'
                )
            if op == "Free" and ptr in params:
                del params[ptr]

            paddle.device.synchronize()
            paddle_reserved2 = paddle.device.cuda.memory_reserved() // self.MB
            paddle_allocated2 = paddle.device.cuda.memory_allocated() // self.MB
            paddle_max_reserved = (
                paddle.device.cuda.max_memory_reserved() // self.MB
            )
            paddle_max_allocated = (
                paddle.device.cuda.max_memory_allocated() // self.MB
            )

            print(
                f"reserved = {paddle_reserved2} allocated = {paddle_allocated2} auto growth = {paddle_reserved2 - paddle_reserved1} max_allocated = {paddle_max_allocated} max_reserved = {paddle_max_reserved}"
            )
        # for multi stream
        stream = paddle.device.cuda.Stream()
        with paddle.device.cuda.stream_guard(stream):
            x = paddle.empty([int(1 * 1024 * 1024 * 1024)], dtype=paddle.uint8)
            del x
        return params

    def test_multi_scale_alloc_free(self):
        params = self.allocate_cmds(self.cmds)


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