diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_skip_layernorm.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_skip_layernorm.py new file mode 100644 index 0000000000000..e1b48d9f3e98b --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_skip_layernorm.py @@ -0,0 +1,214 @@ +# 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 + + +class TrtConvertSkipLayernormTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + inputs = program_config.inputs + weights = program_config.weights + outputs = program_config.outputs + + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + + #The input dimension should be less than or equal to the set axis. + if attrs[0]['begin_norm_axis'] >= 0: + if len(inputs['skip_layernorm_inputX_data'].shape) <= attrs[0][ + 'begin_norm_axis']: + return False + + return True + + def sample_program_configs(self): + def generate_input1(attrs: List[Dict[str, Any]], batch): + if self.dims == 4: + return np.ones([batch, 6, 128, 768]).astype(np.float32) + elif self.dims == 3: + return np.ones([batch, 128, 768]).astype(np.float32) + elif self.dims == 2: + return np.ones([batch, 128, 768]).astype(np.float32) + + def generate_input2(attrs: List[Dict[str, Any]], batch): + if self.dims == 4: + return np.ones([batch, 6, 128, 768]).astype(np.float32) + elif self.dims == 3: + return np.ones([batch, 128, 768]).astype(np.float32) + elif self.dims == 2: + return np.ones([batch, 128, 768]).astype(np.float32) + + def generate_weight1(attrs: List[Dict[str, Any]]): + return np.random.random([768]).astype(np.float32) + + def generate_weight2(attrs: List[Dict[str, Any]]): + return np.random.random([768]).astype(np.float32) + + for dims in [3, 4]: + for batch in [1, 2, 4]: + for epsilon in [1e-5]: + for begin_norm_axis in [0, 1, 2, -1]: + for enable_int8 in [False, True]: + self.dims = dims + dics = [{ + "epsilon": epsilon, + "begin_norm_axis": begin_norm_axis, + "enable_int8": enable_int8 + }, {}] + ops_config = [{ + "op_type": "skip_layernorm", + "op_inputs": { + "X": ["skip_layernorm_inputX_data"], + "Y": ["skip_layernorm_inputY_data"], + "Bias": ["Bias"], + "Scale": ["Scale"] + }, + "op_outputs": { + "Out": ["skip_layernorm_out"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + program_config = ProgramConfig( + ops=ops, + weights={ + "Bias": TensorConfig(data_gen=partial( + generate_weight1, dics)), + "Scale": TensorConfig(data_gen=partial( + generate_weight2, dics)) + }, + inputs={ + "skip_layernorm_inputX_data": TensorConfig( + data_gen=partial(generate_input1, dics, + batch)), + "skip_layernorm_inputY_data": TensorConfig( + data_gen=partial(generate_input2, dics, + batch)) + }, + outputs=["skip_layernorm_out"]) + + yield program_config + + def sample_predictor_configs( + self, program_config) -> (paddle_infer.Config, List[int], float): + def generate_dynamic_shape(attrs): + if self.dims == 4: + self.dynamic_shape.min_input_shape = { + "skip_layernorm_inputX_data": [1, 6, 128, 768], + "skip_layernorm_inputY_data": [1, 6, 128, 768], + "Bias": [768], + "Scale": [768] + } + self.dynamic_shape.max_input_shape = { + "skip_layernorm_inputX_data": [4, 6, 768, 3072], + "skip_layernorm_inputY_data": [4, 6, 768, 3072], + "Bias": [3072], + "Scale": [3072] + } + self.dynamic_shape.opt_input_shape = { + "skip_layernorm_inputX_data": [1, 6, 128, 768], + "skip_layernorm_inputY_data": [1, 6, 128, 768], + "Bias": [768], + "Scale": [768] + } + elif self.dims == 3: + self.dynamic_shape.min_input_shape = { + "skip_layernorm_inputX_data": [1, 128, 768], + "skip_layernorm_inputY_data": [1, 128, 768], + "Bias": [768], + "Scale": [768] + } + self.dynamic_shape.max_input_shape = { + "skip_layernorm_inputX_data": [4, 768, 3072], + "skip_layernorm_inputY_data": [4, 768, 3072], + "Bias": [3072], + "Scale": [3072] + } + self.dynamic_shape.opt_input_shape = { + "skip_layernorm_inputX_data": [1, 128, 768], + "skip_layernorm_inputY_data": [1, 128, 768], + "Bias": [768], + "Scale": [768] + } + elif self.dims == 2: + self.dynamic_shape.min_input_shape = { + "skip_layernorm_inputX_data": [1, 768], + "skip_layernorm_inputY_data": [1, 768], + "Bias": [768], + "Scale": [768] + } + self.dynamic_shape.max_input_shape = { + "skip_layernorm_inputX_data": [4, 3072], + "skip_layernorm_inputY_data": [4, 3072], + "Bias": [3072], + "Scale": [3072] + } + self.dynamic_shape.opt_input_shape = { + "skip_layernorm_inputX_data": [1, 768], + "skip_layernorm_inputY_data": [1, 768], + "Bias": [768], + "Scale": [768] + } + + 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 == True: + 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() + + 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-5 + + # 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-5 + + def add_skip_trt_case(self): + pass + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +if __name__ == "__main__": + unittest.main()