diff --git a/paddle/fluid/inference/tensorrt/convert/pad_op.cc b/paddle/fluid/inference/tensorrt/convert/pad_op.cc index d6711bbbd2cb52fc4508f100ab1e5f1781cc4177..e04c287ab13a3dfc8bbc402154726da631423ac3 100644 --- a/paddle/fluid/inference/tensorrt/convert/pad_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/pad_op.cc @@ -44,22 +44,7 @@ class PadOpConverter : public OpConverter { const std::vector paddings = BOOST_GET_CONST(std::vector, op_desc.GetAttr("paddings")); - nvinfer1::Dims input_shape = input->getDimensions(); - int nbDims = input_shape.nbDims; int pad_size = static_cast(paddings.size()); - PADDLE_ENFORCE_GE( - nbDims, 2, - platform::errors::InvalidArgument( - "Input X[0]'s dimension should greater than or equal to 2. " - "But received %d.", - nbDims)); - PADDLE_ENFORCE_EQ( - (nbDims + 1) * 2, pad_size, - platform::errors::InvalidArgument("Input X[0]'s dimension(nbDims for " - "short) should meet the condition:" - "(nbDims + 1) * 2 == pad_size. But " - "received nbDims:%d, pad_size:%d.", - nbDims, pad_size)); nvinfer1::DimsHW pre_pad(paddings[pad_size - 4], paddings[pad_size - 2]); nvinfer1::DimsHW post_pad(paddings[pad_size - 3], paddings[pad_size - 1]); diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index e14db5a835cd57b58cc07b16f26b14752d07230f..7c484b4722f36fa243adf61865afc5a5fd934dfb 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -686,6 +686,29 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, VLOG(3) << "The pad layer of TRT only support zero."; return false; } + std::vector shape; + auto* block = desc.Block(); + for (auto& param_name : desc.Inputs()) { + for (auto& var_name : param_name.second) { + auto* var_desc = block->FindVar(var_name); + shape = var_desc->GetShape(); + } + } + int nbDims = shape.size(); + std::vector paddings = + BOOST_GET_CONST(std::vector, desc.GetAttr("paddings")); + int pad_size = paddings.size(); + if (nbDims < 2) { + return false; + } + if (nbDims * 2 != pad_size) { + return false; + } + for (int i = 0; i < pad_size - 4; i++) { + if (paddings[i] != 0) { + return false; + } + } } if (op_type == "prelu") { diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad.py new file mode 100644 index 0000000000000000000000000000000000000000..446f7717e3b504c1c7f8e4eb6904bc5225e76cb3 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad.py @@ -0,0 +1,138 @@ +# 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 +import unittest + + +class TrtConvertPadTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + inputs = program_config.inputs + weights = program_config.weights + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + + if attrs[0]['pad_value'] != 0.0: + return False + for x in attrs[0]['paddings']: + if x < 0: + return False + + return True + + def sample_program_configs(self): + def generate_input1(attrs: List[Dict[str, Any]]): + return np.ones([1, 3, 64, 64]).astype(np.float32) + + def generate_weight1(attrs: List[Dict[str, Any]]): + return np.random.random([24, 3, 3, 3]).astype(np.float32) + + for pad_value in [0.0, 1.0, 2.0, -100, 100.0]: + for paddings in [[0, 0, 0, 0, 1, 1, 1, 1], + [0, 0, 0, 0, 1, 2, 3, 4], + [0, 0, 1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, -1, -1, 1, 1]]: + dics = [{"pad_value": pad_value, "paddings": paddings}, {}] + + ops_config = [{ + "op_type": "pad", + "op_inputs": { + "X": ["input_data"] + }, + "op_outputs": { + "Out": ["pad_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, dics)) + }, + outputs=["pad_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": [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): + for x in range(len(program_config.ops[0].attrs['paddings']) - 4): + if program_config.ops[0].attrs['paddings'][x] != 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 + 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-2 + + # 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-2 + + def add_skip_trt_case(self): + def teller1(program_config, predictor_config): + for x in range(len(program_config.ops[0].attrs['paddings']) - 4): + if program_config.ops[0].attrs['paddings'][x] != 0: + return True + return False + + self.add_skip_case( + teller1, SkipReasons.TRT_NOT_IMPLEMENTED, + "NOT Implemented: we need to add support pad not only inplement on h or w, such as paddings = [0, 0, 1, 1, 1, 1, 1, 1]" + ) + pass + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +if __name__ == "__main__": + unittest.main()