From 6327c33bff3bcf1d127e589698ae375ccf28ce46 Mon Sep 17 00:00:00 2001 From: JingZhuangzhuang <75348594+JZZ-NOTE@users.noreply.github.com> Date: Tue, 14 Sep 2021 19:23:48 +0800 Subject: [PATCH] Add dropout convert test (#35488) * add dropout convert test * modify dropout convert test Co-authored-by: xiaoxiaohehe001 --- .../ir/simplify_with_basic_ops_pass.cc | 2 +- paddle/fluid/inference/tensorrt/op_teller.cc | 11 ++ .../ir/inference/test_trt_convert_dropout.py | 157 ++++++++++++++++++ 3 files changed, 169 insertions(+), 1 deletion(-) create mode 100644 python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py diff --git a/paddle/fluid/framework/ir/simplify_with_basic_ops_pass.cc b/paddle/fluid/framework/ir/simplify_with_basic_ops_pass.cc index 282bac4e16..b2b1a7515f 100644 --- a/paddle/fluid/framework/ir/simplify_with_basic_ops_pass.cc +++ b/paddle/fluid/framework/ir/simplify_with_basic_ops_pass.cc @@ -157,7 +157,7 @@ bool SimplifyWithBasicOpsPass::SimplifyDropout( float scale = 1.0f - BOOST_GET_CONST(float, dropout_op_desc->GetAttr("dropout_prob")); - framework::OpDesc new_op_desc; + framework::OpDesc new_op_desc(dropout_op_desc->Block()); new_op_desc.SetType("scale"); new_op_desc.SetInput("X", {dropout_x->Name()}); new_op_desc.SetOutput("Out", {dropout_out->Name()}); diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index 451fa0790e..906521d77a 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -757,6 +757,17 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, } } + if (op_type == "scale") { + auto* block = desc.Block(); + auto x_var_name = desc.Input("X")[0]; + auto* x_var_desc = block->FindVar(x_var_name); + const auto x_shape = x_var_desc->GetShape(); + if (x_shape.size() == 1) { + VLOG(3) << "dropout op does not support input's dim is 1 in tensorrt."; + return false; + } + } + if (op_type == "prelu") { if (desc.Input("X").size() != 1) { VLOG(3) << "Invalid input X's size of prelu TRT converter. " diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py new file mode 100644 index 0000000000..28a85ce96c --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py @@ -0,0 +1,157 @@ +# 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 TrtConvertDropoutTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): + def generate_input1(dims, batch, attrs: List[Dict[str, Any]]): + if dims == 1: + return np.ones([64]).astype(np.float32) + elif dims == 2: + return np.ones([3, 64]).astype(np.float32) + elif dims == 3: + return np.ones([3, 64, 64]).astype(np.float32) + else: + return np.ones([batch, 3, 64, 64]).astype(np.float32) + + for dims in [1, 2, 3, 4]: + for batch in [1, 2, 4]: + for fix_seed in [False, True]: + for dropout_implementation in [ + "downgrade_in_infer", "upscale_in_train" + ]: + for dropout_prob in [np.random.random()]: + for seed in [0, 64, 128, 512]: + self.dims = dims + dics = [{ + "fix_seed": fix_seed, + "dropout_implementation": + dropout_implementation, + "dropout_prob": dropout_prob, + "seed": seed, + "is_test": True + }] + + ops_config = [{ + "op_type": "dropout", + "op_inputs": { + "X": ["input_data"], + }, + "op_outputs": { + "Out": ["dropout_output_data"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + + program_config = ProgramConfig( + ops=ops, + weights={}, + inputs={ + "input_data": TensorConfig( + data_gen=partial(generate_input1, + dims, batch, dics)) + }, + outputs=["dropout_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_data": [1]} + self.dynamic_shape.max_input_shape = {"input_data": [128]} + self.dynamic_shape.opt_input_shape = {"input_data": [64]} + elif self.dims == 2: + self.dynamic_shape.min_input_shape = {"input_data": [1, 32]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 64]} + self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]} + elif self.dims == 3: + self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 64]} + self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]} + else: + self.dynamic_shape.min_input_shape = { + "input_data": [1, 3, 32, 32] + } + self.dynamic_shape.max_input_shape = { + "input_data": [4, 3, 64, 64] + } + self.dynamic_shape.opt_input_shape = { + "input_data": [1, 3, 64, 64] + } + + 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 attrs[0]['dropout_implementation'] == "upscale_in_train": + return 0, 2 + elif self.dims == 1: + return 0, 3 + else: + 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-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): + def teller1(program_config, predictor_config): + if self.dims == 2: + return True + return False + + self.add_skip_case( + teller1, SkipReasons.TRT_NOT_IMPLEMENTED, + "When input dims is 2, pulgin will product a 4 dims output.") + + def test(self): + self.run_test() + + +if __name__ == "__main__": + unittest.main() -- GitLab