# 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 os 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 SkipReasons, TrtLayerAutoScanTest import paddle.inference as paddle_infer class TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: # The output has diff between gpu and trt in CI windows # if ( and self.trt_param.precision == paddle_infer.PrecisionType.Half): # return False return True def sample_program_configs(self): def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.ones([1]).astype(np.int32) ops_config = [ { "op_type": "gather_nd", "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": {}, } ] ops = self.generate_op_config(ops_config) for i in range(10): program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig( data_gen=partial(generate_input1) ), "index_data": TensorConfig( data_gen=partial(generate_input2) ), }, outputs=["output_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": [2, 32, 64, 64], "index_data": [1], } self.dynamic_shape.max_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [1], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [1], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} 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(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "Under Windows Ci, this case will sporadically fail.", ) def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.array([1, 2]).astype(np.int32) ops_config = [ { "op_type": "gather_nd", "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": {}, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, outputs=["output_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": [2, 32, 64, 64], "index_data": [2], } self.dynamic_shape.max_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [2], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [2], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} 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(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "Under Windows Ci, this case will sporadically fail.", ) def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.ones([2, 2]).astype(np.int32) ops_config = [ { "op_type": "gather_nd", "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": {}, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, outputs=["output_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": [2, 32, 64, 64], "index_data": [2, 2], } self.dynamic_shape.max_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [2, 2], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [2, 2], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} 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(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "Under Windows Ci, this case will sporadically fail.", ) def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.ones([2, 2, 4]).astype(np.int32) ops_config = [ { "op_type": "gather_nd", "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": {}, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, outputs=["output_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": [2, 32, 64, 64], "index_data": [2, 2, 4], } self.dynamic_shape.max_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [2, 2, 4], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], "index_data": [2, 2, 4], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} 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(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "Under Windows Ci, this case will sporadically fail.", ) def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(): return np.random.random([2, 32]).astype(np.float32) def generate_input2(): return np.array([[0, 3], [1, 9]]).astype(np.int32) ops_config = [ { "op_type": "gather_nd", "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": {}, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, outputs=["output_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": [2, 32], "index_data": [2, 2], } self.dynamic_shape.max_input_shape = { "input_data": [2, 32], "index_data": [2, 2], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32], "index_data": [2, 2], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} 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(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "Under Windows Ci, this case will sporadically fail.", ) def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(): return np.random.random([16, 32, 256]).astype(np.float32) def generate_input2(): return np.array([[[2, 5], [3, 8]], [[0, 2], [0, 3]]]).astype( np.int32 ) ops_config = [ { "op_type": "gather_nd", "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": {}, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, outputs=["output_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": [16, 32, 256], "index_data": [2, 2, 2], } self.dynamic_shape.max_input_shape = { "input_data": [16, 32, 256], "index_data": [2, 2, 2], } self.dynamic_shape.opt_input_shape = { "input_data": [16, 32, 256], "index_data": [2, 2, 2], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} 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(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), 1e-3 def test(self): self.run_test() if __name__ == "__main__": unittest.main()