# Copyright (c) 2022 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 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 TrtConvertLogicalTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): return np.random.random(shape).astype(np.float32) for shape in [[2, 16], [2, 16, 32], [1, 32, 16, 32]]: for op_type in ["logical_and", "logical_or", "logical_xor"]: for axis in [-1]: self.dims = len(shape) dics = [ {"axis": axis}, {"in_dtype": 5, "out_dtype": 0}, {"in_dtype": 0, "out_dtype": 5}, ] ops_config = [ { "op_type": "cast", "op_inputs": {"X": ["input_data1"]}, "op_outputs": {"Out": ["cast_output_data1"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data1": np.bool_}, }, { "op_type": "cast", "op_inputs": {"X": ["input_data2"]}, "op_outputs": {"Out": ["cast_output_data3"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data3": np.bool_}, }, { "op_type": op_type, "op_inputs": { "X": ["cast_output_data1"], "Y": ["cast_output_data3"], }, "op_outputs": {"Out": ["cast_output_data0"]}, "op_attrs": dics[0], }, { "op_type": "cast", "op_inputs": {"X": ["cast_output_data0"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[2], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial(generate_input, shape) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } if self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } if self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } 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(attrs, dynamic_shape): if dynamic_shape: ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8400: return 0, 7 return 1, 3 return 0, 7 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, 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( attrs, False ), (1e-3, 1e-3) # for dynamic_shape 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( attrs, 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( attrs, True ), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertCompareTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): return np.random.random(shape).astype(np.float32) for shape in [[2, 16], [2, 16, 32], [1, 32, 16, 32]]: for op_type in ["less_than", "greater_than"]: for axis in [-1]: self.dims = len(shape) dics = [ {"axis": axis}, {"in_dtype": 0, "out_dtype": 5}, ] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data1"], "Y": ["input_data2"], }, "op_outputs": {"Out": ["cast_output_data0"]}, "op_attrs": dics[0], }, { "op_type": "cast", "op_inputs": {"X": ["cast_output_data0"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[1], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial(generate_input, shape) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } if self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } if self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } 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(attrs, dynamic_shape): ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8400: return 0, 5 if not dynamic_shape: return 0, 5 return 1, 3 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, 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( attrs, False ), (1e-3, 1e-3) # for dynamic_shape 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( attrs, 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( attrs, True ), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertLessEqualTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): return np.random.random(shape).astype(np.float32) for shape in [[2, 16], [2, 16, 32], [1, 32, 16, 32]]: for op_type in ["less_equal"]: for axis in [-1]: self.dims = len(shape) dics = [ {"axis": axis}, {"in_dtype": 5, "out_dtype": 2}, {"in_dtype": 0, "out_dtype": 5}, ] ops_config = [ { "op_type": "cast", "op_inputs": {"X": ["input_data1"]}, "op_outputs": {"Out": ["cast_output_data1"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data1": np.int32}, }, { "op_type": "cast", "op_inputs": {"X": ["input_data2"]}, "op_outputs": {"Out": ["cast_output_data2"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data2": np.int32}, }, { "op_type": op_type, "op_inputs": { "X": ["cast_output_data1"], "Y": ["cast_output_data2"], }, "op_outputs": {"Out": ["cast_output_data0"]}, "op_attrs": dics[0], }, { "op_type": "cast", "op_inputs": {"X": ["cast_output_data0"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[2], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial(generate_input, shape) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } if self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } if self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } 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(attrs, dynamic_shape): ver = paddle_infer.get_trt_compile_version() if ( ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8400 or not dynamic_shape ): return 2, 5 else: return 1, 3 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, 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( attrs, False ), (1e-3, 1e-3) # for dynamic_shape 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( attrs, 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( attrs, True ), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertGreaterEqualTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): return np.random.random(shape).astype(np.float32) for shape in [[2, 16], [2, 16, 32], [1, 32, 16, 32]]: for op_type in ["greater_equal"]: for axis in [-1]: self.dims = len(shape) dics = [ {"axis": axis}, {"in_dtype": 5, "out_dtype": 2}, {"in_dtype": 0, "out_dtype": 5}, ] ops_config = [ { "op_type": "cast", "op_inputs": {"X": ["input_data1"]}, "op_outputs": {"Out": ["cast_output_data1"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data1": np.int32}, }, { "op_type": "cast", "op_inputs": {"X": ["input_data2"]}, "op_outputs": {"Out": ["cast_output_data2"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data2": np.int32}, }, { "op_type": op_type, "op_inputs": { "X": ["cast_output_data1"], "Y": ["cast_output_data2"], }, "op_outputs": {"Out": ["cast_output_data0"]}, "op_attrs": dics[0], }, { "op_type": "cast", "op_inputs": {"X": ["cast_output_data0"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[2], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial(generate_input, shape) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16], "input_data2": [2, 16], } if self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 16, 32], "input_data2": [2, 16, 32], } if self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.max_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } self.dynamic_shape.opt_input_shape = { "input_data1": [1, 32, 16, 32], "input_data2": [1, 32, 16, 32], } 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(attrs, dynamic_shape): ver = paddle_infer.get_trt_compile_version() if ( ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8400 or not dynamic_shape ): return 2, 5 else: return 1, 3 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, 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( attrs, False ), (1e-3, 1e-3) # for dynamic_shape 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( attrs, 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( attrs, True ), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertCompareSkipTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): return np.random.random(shape).astype(np.int32) for shape in [[2, 16], [2, 16, 32], [1, 32, 16, 32]]: for op_type in ["less_than", "greater_than"]: for axis in [-1]: self.dims = len(shape) dics = [ {"axis": axis}, {"in_dtype": 2, "out_dtype": 0}, {"in_dtype": 0, "out_dtype": 2}, ] ops_config = [ { "op_type": "cast", "op_inputs": {"X": ["input_data1"]}, "op_outputs": {"Out": ["cast_output_data1"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data1": np.bool_}, }, { "op_type": "cast", "op_inputs": {"X": ["input_data2"]}, "op_outputs": {"Out": ["cast_output_data2"]}, "op_attrs": dics[1], "outputs_dtype": {"cast_output_data2": np.bool_}, }, { "op_type": op_type, "op_inputs": { "X": ["cast_output_data1"], "Y": ["cast_output_data2"], }, "op_outputs": {"Out": ["cast_output_data0"]}, "op_attrs": dics[0], "outputs_dtype": {"cast_output_data0": np.bool_}, }, { "op_type": "cast", "op_inputs": {"X": ["cast_output_data0"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[2], "outputs_dtype": {"output_data": np.int32}, }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial(generate_input, shape) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 2: shape_data = [2, 16] if self.dims == 3: shape_data = [2, 16, 32] if self.dims == 4: shape_data = [1, 32, 16, 32] shape_info = { "input_data1": shape_data, "input_data2": shape_data, "cast_output_data0": shape_data, "cast_output_data1": shape_data, "cast_output_data2": shape_data, } self.dynamic_shape.min_input_shape = shape_info self.dynamic_shape.max_input_shape = shape_info self.dynamic_shape.opt_input_shape = shape_info 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(attrs, dynamic_shape): ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8400: return 0, 7 if not dynamic_shape: return 0, 7 return 3, 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, 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( attrs, False ), (1e-3, 1e-3) # for dynamic_shape 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( attrs, 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( attrs, True ), (1e-3, 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()