paddlepaddle--paddle/test/ir/inference/test_trt_convert_index_select.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 itertools
import unittest
from functools import partial

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

import paddle.inference as paddle_infer


class TrtConvertIndexSelectTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        if len(inputs['input_data'].shape) <= attrs[0]['dim']:
            return False

        return True

    def sample_program_configs(self):
        def generate_input1(shape):
            return np.random.random(shape).astype(np.float32)

        def generate_input2(index):
            return np.array(index).astype(np.int32)

        def generate_input4(index):
            return np.array(index).astype(np.int64)

        def generate_input3(axis):
            return np.array([axis]).astype(np.int32)

        for (
            shape,
            index,
            axis,
            overwrite,
            input,
            index_type_int32,
        ) in itertools.product(
            [[32, 64, 16, 32]],
            [[1, 4], [4, 8]],
            [0, 1, 2, 3],
            [True, False],
            [{"X": ["input_data"], "Index": ["index_data"]}],
            [True, False],
        ):
            self.shape = shape
            self.axis = axis
            self.input_num = len(input)
            self.index_type_int32 = index_type_int32
            dics = [{"dim": axis}]
            ops_config = [
                {
                    "op_type": "index_select",
                    "op_inputs": input,
                    "op_outputs": {"Out": ["output_data"]},
                    "op_attrs": dics[0],
                }
            ]
            ops = self.generate_op_config(ops_config)

            program_config = ProgramConfig(
                ops=ops,
                weights={},
                inputs={
                    "index_data": TensorConfig(
                        data_gen=partial(
                            (
                                generate_input2
                                if index_type_int32
                                else generate_input4
                            ),
                            index,
                        )
                    ),
                    "input_data": TensorConfig(
                        data_gen=partial(generate_input1, shape)
                    ),
                },
                outputs=["output_data"],
                no_cast_list=["index_data"],
            )

            yield program_config

    def generate_dynamic_shape(self, attrs):
        if len(self.shape) == 1:
            self.dynamic_shape.min_input_shape = {
                "input_data": [1],
                "index_data": [2],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [128],
                "index_data": [2],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [16],
                "index_data": [2],
            }
        elif len(self.shape) == 2:
            self.dynamic_shape.min_input_shape = {
                "input_data": [2, 4],
                "index_data": [2],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [256, 256],
                "index_data": [2],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [64, 32],
                "index_data": [2],
            }
        elif len(self.shape) == 3:
            self.dynamic_shape.min_input_shape = {
                "input_data": [2, 4, 4],
                "index_data": [2],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [128, 256, 256],
                "index_data": [2],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [16, 64, 32],
                "index_data": [2],
            }
        elif len(self.shape) == 4:
            self.dynamic_shape.min_input_shape = {
                "input_data": [2, 2, 2, 2],
                "index_data": [2],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [128, 256, 64, 128],
                "index_data": [2],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [16, 64, 16, 32],
                "index_data": [2],
            }
        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.max_input_shape = {}
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(dynamic_shape):
            if dynamic_shape:
                ver = paddle_infer.get_trt_compile_version()
                if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8200:
                    return 0, 4
                return 1, 3
            else:
                return 0, 4

        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(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(False),
                1e-3,
            )

        # for dynamic_shape
        self.generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield self.create_inference_config(), generate_trt_nodes_num(True), 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(True), 1e-3

    def test(self):
        self.trt_param.workspace_size = 1 << 10
        self.run_test(run_pir=True)


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