paddlepaddle--paddle/test/ir/inference/test_trt_convert_instance_norm.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.

from __future__ import annotations

import os
import unittest
from functools import partial
from typing import Any

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 TrtConvertInstanceNormTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        if attrs[0]['epsilon'] < 0 or attrs[0]['epsilon'] > 0.001:
            return False

        return True

    def sample_program_configs(self):
        def generate_input1(attrs: list[dict[str, Any]], shape_input):
            return np.random.random(shape_input).astype(np.float32)

        def generate_input2(attrs: list[dict[str, Any]], shape_input):
            return np.random.random(shape_input[1]).astype(np.float32)

        for batch in [1, 2, 4]:
            for shape_input in [
                [batch, 16, 32, 64],
            ]:
                self.in_dim = len(shape_input)
                for epsilon in [
                    0.0005,
                    -1,
                    1,
                    0.000009999999747378752,
                    0.00001,
                ]:
                    dics = [{"epsilon": epsilon}]
                    ops_config = [
                        {
                            "op_type": "instance_norm",
                            "op_inputs": {
                                "X": ["input_data"],
                                "Scale": ["scale_data"],
                                "Bias": ["bias_data"],
                            },
                            "op_outputs": {
                                "Y": ["y_data"],
                                "SavedMean": ["saved_mean_data"],
                                "SavedVariance": ["saved_variance_data"],
                            },
                            "op_attrs": dics[0],
                        }
                    ]
                    ops = self.generate_op_config(ops_config)
                    program_config = ProgramConfig(
                        ops=ops,
                        weights={
                            "scale_data": TensorConfig(
                                data_gen=partial(
                                    generate_input2, dics, shape_input
                                )
                            ),
                            "bias_data": TensorConfig(
                                data_gen=partial(
                                    generate_input2, dics, shape_input
                                )
                            ),
                        },
                        inputs={
                            "input_data": TensorConfig(
                                data_gen=partial(
                                    generate_input1, dics, shape_input
                                )
                            )
                        },
                        outputs=["y_data"],
                    )

                    yield program_config

    def generate_dynamic_shape(self):
        if self.in_dim == 2:
            self.dynamic_shape.min_input_shape = {"input_data": [1, 16]}
            self.dynamic_shape.max_input_shape = {"input_data": [4, 16]}
            self.dynamic_shape.opt_input_shape = {"input_data": [2, 16]}
        elif self.in_dim == 3:
            self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 64]}
            self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64]}
            self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 64]}
        elif self.in_dim == 4:
            self.dynamic_shape.min_input_shape = {"input_data": [1, 16, 32, 64]}
            self.dynamic_shape.max_input_shape = {"input_data": [4, 16, 32, 64]}
            self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 32, 64]}
        return self.dynamic_shape

    def sample_predictor_configs(
        self, program_config, run_pir=False
    ) -> tuple[paddle_infer.Config, list[int], float]:
        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            if dynamic_shape:
                return 1, 2
            if self.in_dim != 4:
                return 0, 3
            return 1, 2

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

        # for static_shape
        clear_dynamic_shape()
        if not run_pir:
            self.trt_param.precision = paddle_infer.PrecisionType.Float32
            program_config.set_input_type(np.float32)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                1e-5,
            )
            self.trt_param.precision = paddle_infer.PrecisionType.Half
            program_config.set_input_type(np.float16)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                (1e-3, 1e-3),
            )

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

    def add_skip_trt_case(self):
        def teller2(program_config, predictor_config):
            if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt':
                return True
            return False

        self.add_skip_case(
            teller2,
            SkipReasons.TRT_NOT_SUPPORT,
            "The output has diff between gpu and trt in Windows.",
        )

    def test(self):
        self.add_skip_trt_case()
        self.run_test(run_pir=True)


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