# Copyright (c) 2020 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 os
import shutil
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import PassVersionChecker
from paddle.fluid.core import AnalysisConfig


class TensorRTSubgraphPassFcTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 6, 64, 64],
                              dtype="float32")
            fc_out = fluid.layers.fc(input=[data], act=None, size=1000)
            reshape_out = fluid.layers.reshape(x=fc_out, shape=[1, 1000])
        self.feeds = {
            "data": np.random.random([1, 6, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassFcTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [reshape_out]

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            # TRT output shape of fc is (1, 1000, 1, 1). To compare the output value only, flatten the results.
            self.check_output_with_option(use_gpu, flatten=True)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassConcatTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data1 = fluid.data(name="data1",
                               shape=[-1, 3, 64, 64],
                               dtype="float32")
            data2 = fluid.data(name="data2",
                               shape=[-1, 3, 64, 64],
                               dtype="float32")
            concat_out = fluid.layers.concat([data1, data2], axis=2)
            out = fluid.layers.batch_norm(concat_out, is_test=True)
        self.feeds = {
            "data1": np.random.random([1, 3, 64, 64]).astype("float32"),
            "data2": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassConcatTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [out]

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassSplitTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 3, 64, 64],
                              dtype="float32")
            split_out = fluid.layers.split(data, dim=-1, num_or_sections=2)
            out = fluid.layers.batch_norm(split_out[0], is_test=True)
        self.feeds = {
            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassSplitTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [out]

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassSplitSerializeTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 3, 64, 64],
                              dtype="float32")
            split_out = fluid.layers.split(data, dim=-1, num_or_sections=2)
            out = fluid.layers.batch_norm(split_out[0], is_test=True)
        self.feeds = {
            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassSplitTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, True, False)
        self.fetch_list = [out]

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            if os.path.exists(self.path + "_opt_cache"):
                shutil.rmtree(self.path + "_opt_cache")
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassDynamicSplitFp16SerializeTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 3, 64, 64],
                              dtype="float32")
            split_out = fluid.layers.split(data, dim=-1, num_or_sections=2)
            out = fluid.layers.batch_norm(split_out[0], is_test=True)
        self.feeds = {
            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassSplitTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False)
        self.dynamic_shape_params = TensorRTSubgraphPassDynamicSplitFp16SerializeTest.DynamicShapeParam(
            {'data': [1, 3, 8, 64]}, {'data': [1, 3, 512, 64]},
            {'data': [1, 3, 256, 64]}, False)
        self.fetch_list = [out]

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            if os.path.exists(self.path + "_opt_cache"):
                shutil.rmtree(self.path + "_opt_cache")
            self.check_output_with_option(use_gpu, 1e-3)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassInstanceNormTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 3, 64, 64],
                              dtype="float32")
            param_attr = fluid.ParamAttr(
                name='instance_norm_w',
                initializer=fluid.initializer.Constant(value=1.0))
            bias_attr = fluid.ParamAttr(
                name='instance_norm_b',
                initializer=fluid.initializer.Constant(value=0.0))
            out = fluid.layers.instance_norm(input=data,
                                             param_attr=param_attr,
                                             bias_attr=bias_attr)
        self.feeds = {
            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassInstanceNormTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [out]

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu, atol=1e-4, flatten=True)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassTransposeTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 6, 64, 64],
                              dtype="float32")
            transpose_out = self.append_transpose(data)
            out = fluid.layers.batch_norm(transpose_out, is_test=True)
        self.feeds = {
            "data": np.random.random([1, 6, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassTransposeTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [out]

    def append_transpose(self, data):
        return fluid.layers.transpose(data, [0, 3, 1, 2])

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassLayerNormTest(InferencePassTest):

    def setUp(self):
        self.set_params()
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 3, 64, 64],
                              dtype="float32")
            out = fluid.layers.layer_norm(data,
                                          begin_norm_axis=self.begin_norm_axis)
        self.feeds = {
            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassLayerNormTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [out]

    def set_params(self):
        self.begin_norm_axis = 1

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassLayerNormDynamicTest(InferencePassTest):

    def setUp(self):
        self.set_params()
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 3, 64, 64],
                              dtype="float32")
            out = fluid.layers.layer_norm(data,
                                          begin_norm_axis=self.begin_norm_axis)
        self.feeds = {
            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.set_trt_params()
        self.fetch_list = [out]

    def set_trt_params(self):
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassLayerNormDynamicTest.TensorRTParam(
            1 << 30, 32, 0, self.precision, self.serialize, False)
        self.dynamic_shape_params = TensorRTSubgraphPassLayerNormDynamicTest.DynamicShapeParam(
            {
                'data': [1, 3, 64, 64],
            }, {
                'data': [8, 8, 64, 64],
            }, {
                'data': [4, 4, 64, 64],
            }, False)

    def set_params(self):
        self.begin_norm_axis = 2
        self.precision = AnalysisConfig.Precision.Float32
        self.serialize = True

    def test_check_output(self):
        if os.path.exists(self.path + "_opt_cache"):
            shutil.rmtree(self.path + "_opt_cache")
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassLayerNormDynamicFP16Test(
        TensorRTSubgraphPassLayerNormDynamicTest):

    def set_params(self):
        self.begin_norm_axis = 2
        self.precision = AnalysisConfig.Precision.Half
        self.serialize = True

    def test_check_output(self):
        if os.path.exists(self.path + "_opt_cache"):
            shutil.rmtree(self.path + "_opt_cache")
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu, atol=0.01, rtol=0.01)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassLayerNormBeginNormAxis2Test(
        TensorRTSubgraphPassLayerNormTest):

    def set_params(self):
        self.begin_norm_axis = 2


class TensorRTSubgraphPassLayerNormBeginNormAxis3Test(
        TensorRTSubgraphPassLayerNormTest):

    def set_params(self):
        self.begin_norm_axis = 3


class TensorRTSubgraphPassElementwiseTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data1 = fluid.data(name="data1",
                               shape=[-1, 3, 64, 64],
                               dtype="float32")
            data2 = fluid.data(name="data2",
                               shape=[-1, 3, 64, 64],
                               dtype="float32")
            eltwise_out = self.append_eltwise(data1, data2)
            out = fluid.layers.batch_norm(eltwise_out, is_test=True)
        self.feeds = {
            "data1": np.random.random([1, 3, 64, 64]).astype("float32"),
            "data2": np.random.random([1, 3, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassElementwiseTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [out]

    def append_eltwise(self, data1, data2):
        return fluid.layers.elementwise_add(x=data1, y=data2)

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassElementwiseMulTest(TensorRTSubgraphPassElementwiseTest
                                             ):

    def append_eltwise(self, data1, data2):
        return fluid.layers.elementwise_mul(x=data1, y=data2)


class TensorRTSubgraphPassElementwiseSerializeTest(
        TensorRTSubgraphPassElementwiseTest):

    def setUp(self):
        super(TensorRTSubgraphPassElementwiseSerializeTest, self).setUp()
        self.trt_parameters = TensorRTSubgraphPassElementwiseTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, True, False)

    def test_check_output(self):
        if os.path.exists(self.path + "_opt_cache"):
            shutil.rmtree(self.path + "_opt_cache")
        super(TensorRTSubgraphPassElementwiseSerializeTest,
              self).test_check_output()


class TensorRTSubgraphPassElementwiseBroadcastDynamicTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data1 = fluid.data(name="data1",
                               shape=[-1, 3, 64, 64],
                               dtype="float32")
            data2 = fluid.data(name="data2", shape=[64, 64], dtype="float32")
            eltwise_out = self.append_eltwise(data1, data2)
            out = fluid.layers.batch_norm(eltwise_out, is_test=True)
        self.feeds = {
            "data1": np.random.random([1, 3, 64, 64]).astype("float32"),
            "data2": np.random.random([64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassElementwiseBroadcastDynamicTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, True, False)
        self.dynamic_shape_params = TensorRTSubgraphPassElementwiseBroadcastDynamicTest.DynamicShapeParam(
            {
                'data1': [1, 3, 8, 64],
                'data2': [8, 64]
            }, {
                'data1': [1, 3, 512, 64],
                'data2': [512, 64]
            }, {
                'data1': [1, 3, 256, 64],
                'data2': [256, 64]
            }, False)
        self.fetch_list = [out]

    def append_eltwise(self, data1, data2):
        return fluid.layers.elementwise_add(x=data1, y=data2)

    def test_check_output(self):
        if os.path.exists(self.path + "_opt_cache"):
            shutil.rmtree(self.path + "_opt_cache")
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


class TensorRTSubgraphPassShuffleChannelTest(InferencePassTest):

    def setUp(self):
        with fluid.program_guard(self.main_program, self.startup_program):
            data = fluid.data(name="data",
                              shape=[-1, 6, 64, 64],
                              dtype="float32")
            sc_out = fluid.layers.shuffle_channel(data, group=3)
            out = fluid.layers.batch_norm(sc_out, is_test=True)
        self.feeds = {
            "data": np.random.random([1, 6, 64, 64]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = TensorRTSubgraphPassShuffleChannelTest.TensorRTParam(
            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
        self.fetch_list = [out]

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)
            self.assertTrue(
                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))


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