# 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 TrtConvertReshapeTest(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 self.dims == 1: if len(attrs[0]['shape']) != 1: return False # To test if the shape contains 0 if len(attrs[0]['shape']) == 3: if attrs[0]['shape'][1] == 0: if self.dims != 3: return False if len(attrs[0]['shape']) == 4: if attrs[0]['shape'][2] == 0: if self.dims != 4: return False return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: self.input_shape = [1, 2, 4, 6] return np.ones([1, 2, 4, 6]).astype(np.float32) elif self.dims == 3: self.input_shape = [1, 8, 6] return np.ones([1, 8, 6]).astype(np.float32) elif self.dims == 2: self.input_shape = [1, 48] return np.ones([1, 48]).astype(np.float32) elif self.dims == 1: self.input_shape = [48] return np.ones([48]).astype(np.float32) def generate_weight1(attrs: List[Dict[str, Any]]): return np.array([1, 48]).astype(np.int32) def generate_shapeT1_data(attrs: List[Dict[str, Any]]): return np.array([2]).astype(np.int32) def generate_shapeT2_data(attrs: List[Dict[str, Any]]): return np.array([24]).astype(np.int32) for dims in [4, 3, 2, 1]: for shape in [ [1, 6, 8], [1, 2, 4, 6], [1, 1, 0, 12], [1, 0, 6], [1, -1, 12], [2, -1], [3, 16], [3, 4, 4], [48], [-1, 48], ]: dics = [ { "shape": shape, }, ] self.dims = dims dics_intput = [{"X": ["reshape_input"]}] ops_config = [ { "op_type": "reshape", "op_inputs": dics_intput[0], "op_outputs": {"Out": ["reshape_out"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "reshape_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["reshape_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 = { "reshape_input": [1, 2, 4, 6] } self.dynamic_shape.max_input_shape = { "reshape_input": [4, 2, 4, 6] } self.dynamic_shape.opt_input_shape = { "reshape_input": [1, 2, 4, 6] } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "reshape_input": [1, 8, 6] } self.dynamic_shape.max_input_shape = { "reshape_input": [4, 8, 6] } self.dynamic_shape.opt_input_shape = { "reshape_input": [1, 8, 6] } elif self.dims == 2: self.dynamic_shape.min_input_shape = {"reshape_input": [1, 48]} self.dynamic_shape.max_input_shape = {"reshape_input": [4, 48]} self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 48]} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"reshape_input": [48]} self.dynamic_shape.max_input_shape = {"reshape_input": [48]} self.dynamic_shape.opt_input_shape = {"reshape_input": [48]} 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): # in static shape mode, here is consistent with op_teller.cc if not dynamic_shape: if attrs[0]['shape'][0] == 0: return 1, 2 elif len(attrs[0]['shape']) == 1: return 0, 3 elif np.prod(attrs[0]['shape'][1:]) == np.prod( self.input_shape[1:] ): return 1, 2 else: 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-3 # 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-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() # reshape having three inputs. class TrtConvertReshapeTest2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: return np.random.random([1, 2, 4, 6]).astype(np.float32) elif self.dims == 3: return np.random.random([1, 8, 6]).astype(np.float32) elif self.dims == 2: return np.random.random([1, 48]).astype(np.float32) elif self.dims == 1: return np.random.random([48]).astype(np.float32) for dims in [4, 3, 2, 1]: for shape in [[-1, 48]]: dics = [ { "shape": shape, }, {}, ] self.dims = dims dics_intput = [ { "X": ["reshape_input"], "ShapeTensor": ["shapeT1_data", "shapeT2_data"], }, ] ops_config = [ { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["shapeT1_data"]}, "op_attrs": { "dtype": 2, "str_value": "2", "shape": [1], }, }, { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["shapeT2_data"]}, "op_attrs": { "dtype": 2, "str_value": "24", "shape": [1], }, }, { "op_type": "reshape", "op_inputs": dics_intput[0], "op_outputs": {"Out": ["reshape_out"]}, "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "reshape_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["reshape_out"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 4: self.dynamic_shape.min_input_shape = { "reshape_input": [1, 2, 4, 6] } self.dynamic_shape.max_input_shape = { "reshape_input": [4, 2, 4, 6] } self.dynamic_shape.opt_input_shape = { "reshape_input": [1, 2, 4, 6] } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "reshape_input": [1, 8, 6] } self.dynamic_shape.max_input_shape = { "reshape_input": [4, 8, 6] } self.dynamic_shape.opt_input_shape = { "reshape_input": [1, 8, 6] } elif self.dims == 2: self.dynamic_shape.min_input_shape = {"reshape_input": [1, 48]} self.dynamic_shape.max_input_shape = {"reshape_input": [4, 48]} self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 48]} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"reshape_input": [48]} self.dynamic_shape.max_input_shape = {"reshape_input": [48]} self.dynamic_shape.opt_input_shape = {"reshape_input": [48]} # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 2), 1e-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() # reshape having 2 inputs. class TrtConvertReshapeTest3(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: return np.random.random([1, 2, 12, 6]).astype(np.float32) elif self.dims == 3: return np.random.random([1, 8, 18]).astype(np.float32) elif self.dims == 2: return np.random.random([1, 144]).astype(np.float32) elif self.dims == 1: return np.random.random([144]).astype(np.float32) for dims in [4, 3, 2, 1]: for shape in [[-1, 144]]: dics = [ { "shape": shape, }, {}, ] self.dims = dims dics_intput = [ { "X": ["reshape_input"], "shape_data": ["shape_data"], }, ] ops_config = [ { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["shape_data"]}, "op_attrs": { "dtype": 2, "str_value": "12", "shape": [2], }, }, { "op_type": "reshape", "op_inputs": dics_intput[0], "op_outputs": {"Out": ["reshape_out"]}, "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "reshape_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["reshape_out"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 4: self.dynamic_shape.min_input_shape = { "reshape_input": [1, 2, 12, 6] } self.dynamic_shape.max_input_shape = { "reshape_input": [4, 2, 12, 6] } self.dynamic_shape.opt_input_shape = { "reshape_input": [1, 2, 12, 6] } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "reshape_input": [1, 8, 18] } self.dynamic_shape.max_input_shape = { "reshape_input": [4, 8, 18] } self.dynamic_shape.opt_input_shape = { "reshape_input": [1, 8, 18] } elif self.dims == 2: self.dynamic_shape.min_input_shape = {"reshape_input": [1, 144]} self.dynamic_shape.max_input_shape = {"reshape_input": [4, 144]} self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 144]} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"reshape_input": [144]} self.dynamic_shape.max_input_shape = {"reshape_input": [144]} self.dynamic_shape.opt_input_shape = {"reshape_input": [144]} # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 2), 1e-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertReshapeZeroDimsTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): if self.dims > 0: self.input_shape = [1] * self.dims return np.random.random(self.input_shape).astype(np.float32) elif self.dims == 0: self.input_shape = [] return np.random.random([]).astype(np.float32) for dims in [0, 1, 2, 3]: for shape in [ [], [1, 1], ]: dics = [ { "shape": shape, }, ] self.dims = dims dics_intput = [{"X": ["reshape_input"]}] ops_config = [ { "op_type": "reshape", "op_inputs": dics_intput[0], "op_outputs": {"Out": ["reshape_out"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "reshape_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["reshape_out"], ) 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 = { "reshape_input": self.input_shape } self.dynamic_shape.max_input_shape = { "reshape_input": self.input_shape } self.dynamic_shape.opt_input_shape = { "reshape_input": self.input_shape } 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): # only test dynamic shape mode return 1, 2 attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] # 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-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()