# 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 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 # This is the special test case with weight including batch dimension # I don't want to mess up the code written by others, so I wrote a class specifically class TrtConvertElementwiseTestOneInputSpecialCase0(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape, op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) else: return np.random.random(shape).astype(np.float32) def generate_weight(op_type): if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=[1, 32, 1, 1], dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform( low=0.1, high=1.0, size=[1, 32, 1, 1] ).astype(np.float32) else: return np.random.randn(1, 32, 1, 1).astype(np.float32) for batch in [1, 4]: for shape in [[batch, 32, 16, 32]]: for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", "elementwise_div", "elementwise_pow", "elementwise_min", "elementwise_max", "elementwise_floordiv", "elementwise_mod", ]: for axis in [-1]: self.dims = len(shape) dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data"], "Y": ["weight"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.float32 if op_type != "elementwise_floordiv" else np.int32 }, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "weight": TensorConfig( data_gen=partial(generate_weight, op_type) ) }, inputs={ "input_data": TensorConfig( data_gen=partial( generate_input, shape, op_type ) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data": [1, 32, 4, 4] } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 32, 32] } self.dynamic_shape.opt_input_shape = { "input_data": [4, 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): 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), (1e-5, 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, 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() # This is the special test case class TrtConvertElementwiseTestOneInputSpecialCase1(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape, op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) if op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) else: return np.random.random(shape).astype(np.float32) def generate_weight(op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=[1], dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=[1]).astype( np.float32 ) else: return np.random.randn(1).astype(np.float32) for shape in [[32]]: for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", "elementwise_div", "elementwise_pow", "elementwise_min", "elementwise_max", "elementwise_floordiv", "elementwise_mod", ]: for axis in [-1]: self.dims = len(shape) dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": {"X": ["input_data"], "Y": ["weight"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.float32 if op_type != "elementwise_floordiv" else np.int32 }, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "weight": TensorConfig( data_gen=partial(generate_weight, op_type) ) }, inputs={ "input_data": TensorConfig( data_gen=partial(generate_input, shape, op_type) ), }, 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": [32]} self.dynamic_shape.max_input_shape = {"input_data": [64]} self.dynamic_shape.opt_input_shape = {"input_data": [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 not dynamic_shape: 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), (1e-5, 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, 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 TrtConvertElementwiseTestOneInput(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape, op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) else: return np.random.random(shape).astype(np.float32) def generate_weight(op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=[32], dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=[32]).astype( np.float32 ) else: return np.random.randn(32).astype(np.float32) for batch in [1, 4]: for shape in [ [32], [batch, 32], [batch, 32, 32], [batch, 32, 16, 32], ]: for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", "elementwise_div", "elementwise_pow", "elementwise_min", "elementwise_max", "elementwise_floordiv", "elementwise_mod", ]: for axis in [-1 if len(shape) == 1 else 1]: self.dims = len(shape) dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data"], "Y": ["weight"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.float32 if op_type != "elementwise_floordiv" else np.int32 }, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "weight": TensorConfig( data_gen=partial(generate_weight, op_type) ) }, inputs={ "input_data": TensorConfig( data_gen=partial( generate_input, shape, op_type ) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [4]} self.dynamic_shape.max_input_shape = {"input_data": [32]} self.dynamic_shape.opt_input_shape = {"input_data": [16]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 32]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]} elif self.dims == 3: self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4]} self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 32]} elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data": [1, 32, 4, 4] } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 32, 32] } self.dynamic_shape.opt_input_shape = { "input_data": [4, 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 self.dims == 1 and not dynamic_shape: 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), (1e-5, 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, 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 TrtConvertElementwiseTestTwoInputWithoutBroadcast(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape, op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) else: return np.random.random(shape).astype(np.float32) for shape in [[4], [4, 32], [2, 32, 16], [1, 8, 16, 32]]: for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", "elementwise_div", "elementwise_pow", "elementwise_min", "elementwise_max", "elementwise_floordiv", "elementwise_mod", ]: for axis in [0, -1]: self.dims = len(shape) dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data1"], "Y": ["input_data2"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.float32 if op_type != "elementwise_floordiv" else 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, op_type) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape, op_type) ), }, 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 == 1: self.dynamic_shape.min_input_shape = { "input_data1": [1], "input_data2": [1], } self.dynamic_shape.max_input_shape = { "input_data1": [128], "input_data2": [128], } self.dynamic_shape.opt_input_shape = { "input_data1": [32], "input_data2": [32], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4], "input_data2": [1, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [128, 256], "input_data2": [128, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [32, 64], "input_data2": [32, 64], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4], "input_data2": [1, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [128, 128, 256], "input_data2": [128, 128, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 32, 16], "input_data2": [2, 32, 16], } elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4, 4], "input_data2": [1, 4, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [8, 128, 64, 128], "input_data2": [8, 128, 64, 128], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 64, 32, 32], "input_data2": [2, 64, 32, 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 self.dims == 1 and not dynamic_shape: return 0, 4 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, 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(), (1, 3), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), (1, 3), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertElementwiseTestTwoInputWithBroadcast(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs if len(inputs['input_data1'].shape) != len(inputs['input_data2'].shape): return False return True def sample_program_configs(self): def generate_input(shape, op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) else: return np.random.random(shape).astype(np.float32) input1_shape_list = [[4, 32], [2, 4, 32], [4, 2, 4, 32]] input2_shape1_list = [[32], [4, 32], [2, 4, 32]] input2_shape2_list = [[4, 1], [2, 4, 1], [4, 2, 4, 1]] input2_shape3_list = [[32], [2, 1, 1], [4, 2, 1, 32]] input2_shape4_list = [[32], [4, 32], [4, 1, 4, 32]] input2_shape5_list = [[32], [2, 1, 32], [4, 1, 1, 32]] input2_shape6_list = [[1, 32], [1, 32], [1, 1, 1, 32]] input2_shape_list = [ input2_shape1_list, input2_shape2_list, input2_shape3_list, input2_shape4_list, input2_shape5_list, input2_shape6_list, ] axis1_list = [[-1], [1, -1], [1, -1]] axis2_list = [[-1], [0], [0]] axis3_list = [[-1], [0], [0]] axis4_list = [[-1], [-1], [0]] axis5_list = [[-1, 1], [-1, 0], [-1, 0]] axis6_list = [[-1, 0], [-1, 1], [-1, 0]] axis_list = [ axis1_list, axis2_list, axis3_list, axis4_list, axis5_list, axis6_list, ] for i in range(3): input1_shape = input1_shape_list[i] for j in range(6): input2_shape = input2_shape_list[j][i] for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", "elementwise_div", "elementwise_pow", "elementwise_min", "elementwise_max", "elementwise_floordiv", "elementwise_mod", ]: for axis in axis_list[j][i]: self.shape1 = input1_shape self.shape2 = input2_shape dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data1"], "Y": ["input_data2"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.float32 if op_type != "elementwise_floordiv" else np.int32 }, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial( generate_input, input1_shape, op_type ) ), "input_data2": TensorConfig( data_gen=partial( generate_input, input2_shape, op_type ) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): max_shape = [ [128], [128, 128], [128, 128, 128], [128, 128, 128, 128], ] min_shape = [[1], [1, 1], [1, 1, 1], [1, 1, 1, 1]] opt_shape = [[32], [32, 32], [32, 32, 32], [32, 32, 32, 32]] self.dynamic_shape.min_input_shape = { "input_data1": min_shape[len(self.shape1) - 1], "input_data2": min_shape[len(self.shape2) - 1], } self.dynamic_shape.max_input_shape = { "input_data1": max_shape[len(self.shape1) - 1], "input_data2": max_shape[len(self.shape2) - 1], } self.dynamic_shape.opt_input_shape = { "input_data1": opt_shape[len(self.shape1) - 1], "input_data2": opt_shape[len(self.shape2) - 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() if self.shape1[0] == self.shape2[0]: self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) yield self.create_inference_config(), (1, 3), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), (1, 3), (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(), (1, 3), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), (1, 3), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertElementwiseTestOneInputCornerCase(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape, op_type): # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) else: return np.random.random(shape).astype(np.float32) # use rand not randn to avoiding pow producing `NAN` def generate_weight(op_type): if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=[32], dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=[32]).astype( np.float32 ) else: return np.random.rand(32).astype(np.float32) for batch in [1, 2, 4]: for shape in [ [32], [batch, 32], [batch, 32, 32], [batch, 32, 16, 32], ]: for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", "elementwise_div", "elementwise_pow", "elementwise_min", "elementwise_max", "elementwise_floordiv", "elementwise_mod", ]: self.op_type = op_type for axis in [-1 if len(shape) == 1 else 1]: self.dims = len(shape) dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["weight"], "Y": ["input_data"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.float32 if op_type != "elementwise_floordiv" else np.int32 }, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "weight": TensorConfig( data_gen=partial(generate_weight, op_type) ) }, inputs={ "input_data": TensorConfig( data_gen=partial( generate_input, shape, op_type ) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [4]} self.dynamic_shape.max_input_shape = {"input_data": [64]} self.dynamic_shape.opt_input_shape = {"input_data": [32]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 32]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]} elif self.dims == 3: self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4]} self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 256] } self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 16]} elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data": [1, 32, 4, 4] } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 128, 256] } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 32, 16] } 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), (0, 3), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), (0, 3), (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(), (1, 2), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), (1, 2), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertElementwiseTestTwoInputSkipCase(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: # if program_config.ops[0].type in "round": return True def sample_program_configs(self): def generate_input(shape, op_type): if op_type == "elementwise_pow": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) # Paddle mul support bool and TensorRT not if op_type == "elementwise_mul": return np.random.random(shape).astype(np.bool_) for shape in [[4], [4, 32], [2, 32, 16], [1, 8, 16, 32]]: for op_type in [ "elementwise_pow", "elementwise_mul", ]: for axis in [0, -1]: self.dims = len(shape) dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data1"], "Y": ["input_data2"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.int32 if op_type == "elementwise_pow" else np.bool_ }, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial(generate_input, shape, op_type) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape, op_type) ), }, 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 == 1: self.dynamic_shape.min_input_shape = { "input_data1": [1], "input_data2": [1], } self.dynamic_shape.max_input_shape = { "input_data1": [128], "input_data2": [128], } self.dynamic_shape.opt_input_shape = { "input_data1": [32], "input_data2": [32], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4], "input_data2": [1, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [128, 256], "input_data2": [128, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [32, 64], "input_data2": [32, 64], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4], "input_data2": [1, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [128, 128, 256], "input_data2": [128, 128, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 32, 16], "input_data2": [2, 32, 16], } elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4, 4], "input_data2": [1, 4, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [8, 128, 64, 128], "input_data2": [8, 128, 64, 128], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 64, 32, 32], "input_data2": [2, 64, 32, 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): 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), (1e-5, 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(), (0, 4), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), (0, 4), (1e-3, 1e-3) def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() class TrtConvertPowOp(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape): if len(shape) == 0: return np.random.random([]).astype(np.float32) return np.random.random(shape).astype(np.float32) for batch in [1, 4]: for shape in [ [], [32], [batch, 32], [batch, 32, 32], [batch, 32, 16, 32], ]: for factor in [1.0, 2.0, -1.0, 0.5, -2]: self.dims = len(shape) dics = [{"factor": factor}] ops_config = [ { "op_type": "pow", "op_inputs": { "X": ["input_data"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": {"output_data": np.float32}, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": 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 == 0: self.dynamic_shape.min_input_shape = {"input_data": []} self.dynamic_shape.max_input_shape = {"input_data": []} self.dynamic_shape.opt_input_shape = {"input_data": []} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [4]} self.dynamic_shape.max_input_shape = {"input_data": [32]} self.dynamic_shape.opt_input_shape = {"input_data": [16]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 32]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]} elif self.dims == 3: self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4]} self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 32]} elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data": [1, 32, 4, 4] } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 32, 32] } self.dynamic_shape.opt_input_shape = { "input_data": [4, 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 (self.dims == 1 or self.dims == 0) and not dynamic_shape: 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), (1e-5, 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, 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 TrtConvertElementwise0D(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(dims, op_type): shape = [] if dims == 0: shape = [] elif dims == 1: shape = [8] elif dims == 2: shape = [1, 8] elif dims == 3: shape = [1, 8, 8] else: shape = [1, 8, 8, 8] # elementwise_floordiv is integer only if op_type == "elementwise_floordiv": return np.random.randint( low=1, high=10000, size=shape, dtype=np.int32 ) elif op_type == "elementwise_mod": return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) else: return np.random.random(shape).astype(np.float32) for dims in [[0, 0], [0, 1], [0, 2], [1, 0], [2, 0]]: for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", "elementwise_div", "elementwise_pow", "elementwise_min", "elementwise_max", "elementwise_floordiv", "elementwise_mod", ]: for axis in [-1 if dims[0] == 1 or dims[0] == 0 else 1]: self.dims = dims[0] dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data"], "Y": ["weight"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], "outputs_dtype": { "output_data": np.float32 if op_type != "elementwise_floordiv" else np.int32 }, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "weight": TensorConfig( data_gen=partial( generate_input, dims[1], op_type ) ) }, inputs={ "input_data": TensorConfig( data_gen=partial( generate_input, dims[0], op_type ) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 0: self.dynamic_shape.min_input_shape = {"input_data": []} self.dynamic_shape.max_input_shape = {"input_data": []} self.dynamic_shape.opt_input_shape = {"input_data": []} if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} self.dynamic_shape.max_input_shape = {"input_data": [16]} self.dynamic_shape.opt_input_shape = {"input_data": [8]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 8]} self.dynamic_shape.max_input_shape = {"input_data": [4, 8]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 8]} elif self.dims == 3: self.dynamic_shape.min_input_shape = {"input_data": [1, 1, 4]} self.dynamic_shape.max_input_shape = {"input_data": [4, 16, 16]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 8, 8]} elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data": [1, 8, 8, 8] } self.dynamic_shape.max_input_shape = { "input_data": [4, 8, 8, 8] } self.dynamic_shape.opt_input_shape = { "input_data": [4, 8, 8, 8] } 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 not dynamic_shape and (self.dims == 1 or self.dims == 0): 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), (1e-5, 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, 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 test(self): self.run_test() if __name__ == "__main__": unittest.main()