# Copyright (c) 2023 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 amp_base_models import AmpTestBase, build_conv_model

import paddle
from paddle.static import amp


class TestStaticAmpPromoteStats(AmpTestBase):
    def check_promote_results(
        self, use_amp, dtype, level, use_promote, expected_op_calls, debug_info
    ):
        paddle.enable_static()
        (
            main_program,
            startup_program,
            optimizer,
            feed_vars,
            fetch_vars,
        ) = build_conv_model(use_amp, dtype, level, use_promote)
        self.assertEqual(main_program.num_blocks, 1)

        amp.debugging.collect_operator_stats(main_program)
        op_stats_list = amp.debugging._get_op_stats_list(main_program)

        self._check_op_calls(
            op_stats_list[0],
            expected_fp16_calls=expected_op_calls,
            debug_info=debug_info,
        )

        place = paddle.CUDAPlace(0)
        exe = paddle.static.Executor(place)

        max_iters = 2
        x_fp32 = np.random.random(size=[1, 1, 6, 6]).astype("float32")
        losses_o1 = self.run_program(
            main_program,
            startup_program,
            optimizer,
            feed_vars,
            fetch_vars,
            place,
            exe,
            x_fp32,
            max_iters,
            dtype,
            level,
        )
        paddle.disable_static()

    def test_static_amp_o1(self):
        expected_fp16_calls = {
            "conv2d": 1,
            "elementwise_add": 0,
            "relu": 0,
            "matmul_v2": 1,
            "softmax": 0,
            "reduce_mean": 0,
            "adamw": 0,
        }
        self.check_promote_results(
            True,
            'float16',
            'O1',
            use_promote=True,
            expected_op_calls=expected_fp16_calls,
            debug_info="TestStaticAmpPromoteStats/test_static_amp_o1",
        )

    def test_static_amp_o2(self):
        expected_fp16_calls = {
            "conv2d": 1,
            "elementwise_add": 2,
            "relu": 0,
            "matmul_v2": 1,
            "softmax": 1,
            "reduce_mean": 1,
            "adamw": 4,
        }
        self.check_promote_results(
            True,
            'float16',
            'O2',
            use_promote=True,
            expected_op_calls=expected_fp16_calls,
            debug_info="TestStaticAmpPromoteStats/test_static_amp_o2",
        )


class TestEagerAmpPromoteStats(AmpTestBase):
    def check_promote_results(
        self, dtype, level, use_promote, expected_op_calls, debug_info
    ):
        model, optimizer, scaler = build_conv_model(
            use_amp=True,
            amp_dtype=dtype,
            amp_level=level,
            use_promote=use_promote,
        )
        model.train()

        paddle.amp.debugging.enable_operator_stats_collection()
        with paddle.amp.auto_cast(
            enable=True, dtype=dtype, level=level, use_promote=use_promote
        ):
            x = paddle.rand(shape=[1, 1, 6, 6], dtype='float32')
            out = model(x)
            loss = paddle.mean(out)
        scaled = scaler.scale(loss)
        scaled.backward()
        scaler.minimize(optimizer, scaled)
        optimizer.clear_grad()
        paddle.amp.debugging.disable_operator_stats_collection()
        op_stats = paddle.fluid.core.get_low_precision_op_list()

        self._check_op_calls(
            op_stats,
            expected_fp16_calls=expected_op_calls,
            debug_info=debug_info,
        )

    def test_o2_promote_on(self):
        expected_fp16_calls = {
            "conv2d": 1,
            "elementwise_add": 2,
            "relu": 0,
            "matmul_v2": 1,
            "softmax": 1,
            "reduce_mean": 1,
            "adamw_": 4,
        }
        self.check_promote_results(
            'float16',
            'O2',
            use_promote=True,
            expected_op_calls=expected_fp16_calls,
            debug_info="TestEagerAmpPromoteStats/test_o2_promote_on",
        )

    def test_o2_promote_off(self):
        expected_fp16_calls = {
            "conv2d": 1,
            "elementwise_add": 2,
            "relu": 1,
            "matmul_v2": 1,
            "softmax": 1,
            "reduce_mean": 1,
            "adamw_": 4,
        }
        self.check_promote_results(
            'float16',
            'O2',
            use_promote=False,
            expected_op_calls=expected_fp16_calls,
            debug_info="TestEagerAmpPromoteStats/test_o2_promote_off",
        )


class TestEagerAmpPromoteSimple(AmpTestBase):
    def setUp(self):
        self._conv = paddle.nn.Conv2D(
            in_channels=1, out_channels=6, kernel_size=3, bias_attr=False
        )
        self._linear = paddle.nn.Linear(in_features=4, out_features=4)

    def test_o2_use_promote_on(self):
        with paddle.amp.auto_cast(level='O2'):
            x = paddle.rand(shape=[1, 1, 6, 6], dtype='float32')
            conv_out = self._conv(x)
            y = paddle.rand(shape=conv_out.shape, dtype='float16')
            add_out = conv_out + y
            linear_out = self._linear(add_out)

        self.assertEqual(conv_out.dtype, paddle.float16)
        self.assertEqual(add_out.dtype, paddle.float16)
        self.assertEqual(linear_out.dtype, paddle.float32)

    def test_o2_use_promote_off(self):
        with paddle.amp.auto_cast(level='O2', use_promote=False):
            x = paddle.rand(shape=[1, 1, 6, 6], dtype='float32')
            conv_out = self._conv(x)
            y = paddle.rand(shape=conv_out.shape, dtype='float16')
            add_out = conv_out + y
            linear_out = self._linear(add_out)

        self.assertEqual(conv_out.dtype, paddle.float16)
        self.assertEqual(add_out.dtype, paddle.float16)
        self.assertEqual(linear_out.dtype, paddle.float16)


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