From 9f4d201a075f13569adf31c4b7a72b61bb9506e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=B4=A5?= <33440396+xingjing1@users.noreply.github.com> Date: Wed, 15 Sep 2021 10:40:51 +0800 Subject: [PATCH] add test (#35316) --- .../inference/test_trt_convert_layer_norm.py | 140 ++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_layer_norm.py diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_layer_norm.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_layer_norm.py new file mode 100644 index 00000000000..13c932d55b8 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_layer_norm.py @@ -0,0 +1,140 @@ +# 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 trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons +from program_config import TensorConfig, ProgramConfig +import numpy as np +import paddle.inference as paddle_infer +from functools import partial +from typing import Optional, List, Callable, Dict, Any, Set +import unittest + + +class TrtConvertLayerNormTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + inputs = program_config.inputs + weights = program_config.weights + 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 + if attrs[0]['begin_norm_axis'] <= 0 or attrs[0]['begin_norm_axis'] >= ( + len(inputs['input_data'].shape) - 1): + return False + + return True + + def sample_program_configs(self): + def generate_input1(attrs: List[Dict[str, Any]], shape_input): + return np.ones(shape_input).astype(np.float32) + + def generate_input2(attrs: List[Dict[str, Any]], shape_input): + begin = attrs[0]["begin_norm_axis"] + sum = 1 + for x in range(begin, len(shape_input)): + sum *= shape_input[x] + return np.ones([sum]).astype(np.float32) + + for epsilon in [0.0005, -1, 1]: + for begin_norm_axis in [1, 0, -1, 2, 3]: + dics = [{ + "epsilon": epsilon, + "begin_norm_axis": begin_norm_axis + }, {}] + + ops_config = [{ + "op_type": "layer_norm", + "op_inputs": { + "X": ["input_data"], + "Scale": ["scale_data"], + "Bias": ["bias_data"] + }, + "op_outputs": { + "Y": ["y_data"], + "Mean": ["saved_mean_data"], + "Variance": ["saved_variance_data"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + shape_input = [1, 3, 64, 64] + program_config = ProgramConfig( + ops=ops, + weights={ + "bias_data": TensorConfig(data_gen=partial( + generate_input2, dics, shape_input)), + "scale_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 sample_predictor_configs( + self, program_config) -> (paddle_infer.Config, List[int], float): + def generate_dynamic_shape(attrs): + self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]} + self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]} + + 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): + inputs = program_config.inputs + #if not dynamic_shape: + # if attrs[0]["begin_norm_axis"] >= len(inputs["input_data"].shape) - 1: + # print ("iiiiiii") + # 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() + self.trt_param.precision = paddle_infer.PrecisionType.Float32 + yield self.create_inference_config(), generate_trt_nodes_num( + attrs, False), 1e-5 + self.trt_param.precision = paddle_infer.PrecisionType.Half + yield self.create_inference_config(), generate_trt_nodes_num( + attrs, False), 1e-2 + + # for dynamic_shape + generate_dynamic_shape(attrs) + 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-2 + + def test(self): + self.run_test() + + +if __name__ == "__main__": + unittest.main() -- GitLab