# 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
from functools import partial
from typing import List

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertFlashMultiHeadMatmulTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        ver = paddle_infer.get_trt_compile_version()
        if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8520:
            return False
        return True

    def sample_program_configs(self):
        def generate_input1(batch, dim1):
            return np.random.rand(batch, dim1, 320).astype(np.float32) / 10

        def generate_weight1():
            return np.random.rand(320, 320).astype(np.float32) / 10

        for batch in [1, 2]:
            self.batch = batch
            for reshape_shape in [[0, 0, 8, 40]]:
                for dim1 in [4096]:
                    dics = [
                        {"trans_x": False, "trans_y": False},  # 0,matmul_v2_q
                        {"shape": reshape_shape},  # 1,reshape_q
                        {
                            "axis": [0, 2, 1, 3],
                            "data_format": "AnyLayout",
                        },  # 2,trans_q
                        {"trans_x": False, "trans_y": False},  # 3,matmul_v2_k
                        {"shape": reshape_shape},  # 4,reshape_k
                        {
                            "axis": [0, 2, 1, 3],
                            "data_format": "AnyLayout",
                        },  # 5,trans_k
                        {"trans_x": False, "trans_y": False},  # 6,matmul_v2_q
                        {"shape": reshape_shape},  # 7,reshape_q
                        {
                            "axis": [0, 2, 1, 3],
                            "data_format": "AnyLayout",
                        },  # 8,trans_q
                        {  # 9,matmul_qk
                            "trans_x": False,
                            "trans_y": True,
                        },
                        {  # 10,scale
                            "scale": 0.15811388194561005,
                            "bias": 0.0,
                            "bias_after_scale": True,
                        },
                        {"axis": -1, "is_test": True},  # 11,softmax
                        {"trans_x": False, "trans_y": False},  # 12,matmul_qkv
                        {
                            "axis": [0, 2, 1, 3],
                            "data_format": "AnyLayout",
                        },  # 13,trans_qkv
                        {"shape": [0, 0, 320]},  # 14,reshape_qkv
                    ]

                    ops_config = [
                        {
                            "op_type": "matmul_v2",
                            "op_inputs": {
                                "X": ["input_data1"],
                                "Y": ["mul1_weight"],
                            },
                            "op_outputs": {"Out": ["mul1_output"]},
                            "op_attrs": dics[0],
                        },
                        {
                            "op_type": "reshape2",
                            "op_inputs": {
                                "X": ["mul1_output"],
                            },
                            "op_outputs": {
                                "Out": ["reshape21_output"],
                                "XShape": ["reshape21_output_xshape"],
                            },
                            "op_attrs": dics[1],
                        },
                        {
                            "op_type": "transpose2",
                            "op_inputs": {"X": ["reshape21_output"]},
                            "op_outputs": {
                                "Out": ["transpose21_output"],
                                "XShape": ["transpose21_output_xshape"],
                            },
                            "op_attrs": dics[2],
                        },
                        {
                            "op_type": "matmul_v2",
                            "op_inputs": {
                                "X": ["input_data1"],
                                "Y": ["mul2_weight"],
                            },
                            "op_outputs": {"Out": ["mul2_output"]},
                            "op_attrs": dics[3],
                        },
                        {
                            "op_type": "reshape2",
                            "op_inputs": {"X": ["mul2_output"]},
                            "op_outputs": {
                                "Out": ["reshape22_output"],
                                "XShape": ["reshape22_output_xshape"],
                            },
                            "op_attrs": dics[4],
                        },
                        {
                            "op_type": "transpose2",
                            "op_inputs": {"X": ["reshape22_output"]},
                            "op_outputs": {
                                "Out": ["transpose22_output"],
                                "XShape": ["transpose22_output_xshape"],
                            },
                            "op_attrs": dics[5],
                        },
                        {
                            "op_type": "matmul_v2",
                            "op_inputs": {
                                "X": ["input_data1"],
                                "Y": ["mul3_weight"],
                            },
                            "op_outputs": {"Out": ["mul3_output"]},
                            "op_attrs": dics[6],
                        },
                        {
                            "op_type": "reshape2",
                            "op_inputs": {"X": ["mul3_output"]},
                            "op_outputs": {
                                "Out": ["reshape23_output"],
                                "XShape": ["reshape23_output_xshape"],
                            },
                            "op_attrs": dics[7],
                        },
                        {
                            "op_type": "transpose2",
                            "op_inputs": {"X": ["reshape23_output"]},
                            "op_outputs": {
                                "Out": ["transpose23_output"],
                                "XShape": ["transpose23_output_xshape"],
                            },
                            "op_attrs": dics[8],
                        },
                        {
                            "op_type": "matmul_v2",
                            "op_inputs": {
                                "X": ["transpose21_output"],
                                "Y": ["transpose22_output"],
                            },
                            "op_outputs": {"Out": ["matmul1_output"]},
                            "op_attrs": dics[9],
                        },
                        {
                            "op_type": "scale",
                            "op_inputs": {
                                "X": ["matmul1_output"],
                            },
                            "op_outputs": {"Out": ["scale_output"]},
                            "op_attrs": dics[10],
                        },
                        {
                            "op_type": "softmax",
                            "op_inputs": {"X": ["scale_output"]},
                            "op_outputs": {"Out": ["softmax_output"]},
                            "op_attrs": dics[11],
                        },
                        {
                            "op_type": "matmul_v2",
                            "op_inputs": {
                                "X": ["softmax_output"],
                                "Y": ["transpose23_output"],
                            },
                            "op_outputs": {"Out": ["matmul2_output"]},
                            "op_attrs": dics[12],
                        },
                        {
                            "op_type": "transpose2",
                            "op_inputs": {"X": ["matmul2_output"]},
                            "op_outputs": {
                                "Out": ["transpose24_output"],
                                "XShape": ["transpose24_output_xshape"],
                            },
                            "op_attrs": dics[13],
                        },
                        {
                            "op_type": "reshape2",
                            "op_inputs": {"X": ["transpose24_output"]},
                            "op_outputs": {
                                "Out": ["reshape24_output"],
                                "XShape": ["reshape24_output_xshape"],
                            },
                            "op_attrs": dics[14],
                        },
                    ]
                    ops = self.generate_op_config(ops_config)

                    program_config = ProgramConfig(
                        ops=ops,
                        weights={
                            "mul1_weight": TensorConfig(
                                data_gen=partial(generate_weight1)
                            ),
                            "mul2_weight": TensorConfig(
                                data_gen=partial(generate_weight1)
                            ),
                            "mul3_weight": TensorConfig(
                                data_gen=partial(generate_weight1)
                            ),
                        },
                        inputs={
                            "input_data1": TensorConfig(
                                data_gen=partial(generate_input1, batch, dim1)
                            )
                        },
                        outputs=["reshape24_output"],
                    )

                    yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            # The last dim of input1 and input2 should be static.
            self.dynamic_shape.min_input_shape = {
                "input_data1": [1, 4096, 320],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data1": [16, 4096, 320],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data1": [2, 4096, 320],
            }

        def clear_dynamic_shape():
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        self.trt_param.workspace_size = 2013265920
        yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (1, 2), (1e-3, 1e-3)

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        self.trt_param.workspace_size = 2013265920
        yield self.create_inference_config(), (1, 2), (1e-5, 1e-4)
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (1, 2), (1e-2, 1e-3)

    def add_skip_trt_case(self):
        def teller1(program_config, predictor_config):
            if self.dynamic_shape.min_input_shape == {}:
                return True
            return False

        self.add_skip_case(
            teller1,
            SkipReasons.TRT_NOT_IMPLEMENTED,
            "TThe flash attention trt oss plugin do not support static shape yet",
        )

        def teller2(program_config, predictor_config):
            if self.trt_param.precision == paddle_infer.PrecisionType.Float32:
                return True
            return False

        self.add_skip_case(
            teller2,
            SkipReasons.TRT_NOT_IMPLEMENTED,
            "The flash attention trt oss plugin do not support fp32 yet",
        )

        def teller3(program_config, predictor_config):
            if self.trt_param.precision == paddle_infer.PrecisionType.Int8:
                return True
            return False

        self.add_skip_case(
            teller3,
            SkipReasons.TRT_NOT_IMPLEMENTED,
            "The flash attention trt oss plugin do not support int8 yet.",
        )

    def test(self):
        self.add_skip_trt_case()
        self.run_test()


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