# 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 Any, Dict, List 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 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() class TrtConvertLayerNormTest_2(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 = [2, 64, 3, 3] 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, 64, 3, 3]} self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 3, 9]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 64, 3, 3]} 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()