diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index 19ecc4eaf47452ac909e45ca5d7d517a262e0733..ebb2ecc136f031b5bb6302c915e050c8cb41a424 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -563,7 +563,19 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, } } } - + if (op_type == "scale") { + auto scale_inputs = desc.Inputs(); + if (scale_inputs.find("ScaleTensor") != scale_inputs.end()) { + if (desc.Input("ScaleTensor").size() >= 1) { + return false; + } + } + 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 (!with_dynamic_shape && x_shape.size() == 1) return false; + } if (op_type == "slice") { if (!desc.HasAttr("axes") || !desc.HasAttr("starts") || !desc.HasAttr("ends") || !desc.HasAttr("decrease_axis")) { diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scale.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scale.py new file mode 100644 index 0000000000000000000000000000000000000000..8a44617dc8dc3c27c915291bc4e973876eeaf534 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scale.py @@ -0,0 +1,168 @@ +# 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 TrtConvertScaleTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): + def generate_input1(attrs: List[Dict[str, Any]], batch): + if self.dims == 4: + return np.ones([batch, 3, 24, 24]).astype(np.float32) + elif self.dims == 3: + return np.ones([batch, 3, 24]).astype(np.float32) + elif self.dims == 2: + return np.ones([batch, 24]).astype(np.float32) + elif self.dims == 1: + return np.ones([24]).astype(np.float32) + + def generate_weight1(attrs: List[Dict[str, Any]]): + return np.ones([1]).astype(np.float32) + + for num_input in [0, 1]: + for dims in [1, 2, 3, 4]: + for batch in [1, 2]: + for scale in [0.1, 1.0]: + for bias in [0.0, 1.2]: + for bias_after_scale in [False, True]: + self.num_input = num_input + self.dims = dims + dics = [{ + "scale": scale, + "bias": bias, + "bias_after_scale": bias_after_scale + }, {}] + + dics_intput = [{ + "X": ["scale_input"], + "ScaleTensor": ["ScaleTensor"], + }, { + "X": ["scale_input"] + }] + dics_intputs = [{ + "ScaleTensor": TensorConfig( + data_gen=partial(generate_weight1, + dics)) + }, {}] + + ops_config = [{ + "op_type": "scale", + "op_inputs": dics_intput[num_input], + "op_outputs": { + "Out": ["scale_out"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + program_config = ProgramConfig( + ops=ops, + weights=dics_intputs[num_input], + inputs={ + "scale_input": TensorConfig( + data_gen=partial(generate_input1, + dics, batch)) + }, + outputs=["scale_out"]) + + yield program_config + + def sample_predictor_configs( + self, program_config) -> (paddle_infer.Config, List[int], float): + def generate_dynamic_shape(attrs): + if self.dims == 4: + self.dynamic_shape.min_input_shape = { + "scale_input": [1, 3, 24, 24] + } + self.dynamic_shape.max_input_shape = { + "scale_input": [9, 3, 48, 48] + } + self.dynamic_shape.opt_input_shape = { + "scale_input": [1, 3, 48, 24] + } + elif self.dims == 3: + self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24]} + self.dynamic_shape.max_input_shape = {"scale_input": [9, 6, 48]} + self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24]} + elif self.dims == 2: + self.dynamic_shape.min_input_shape = {"scale_input": [1, 24]} + self.dynamic_shape.max_input_shape = {"scale_input": [9, 48]} + self.dynamic_shape.opt_input_shape = {"scale_input": [1, 24]} + elif self.dims == 1: + self.dynamic_shape.min_input_shape = {"scale_input": [24]} + self.dynamic_shape.max_input_shape = {"scale_input": [48]} + self.dynamic_shape.opt_input_shape = {"scale_input": [24]} + + 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): + 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 len(program_config.weights) == 1: + return True + return False + + self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT, + "INPUT ScaleTensor and Shape NOT SUPPORT") + + def teller2(program_config, predictor_config): + if self.dims == 1 and self.dynamic_shape.min_input_shape == 0: + return True + return False + + self.add_skip_case(teller2, SkipReasons.TRT_NOT_SUPPORT, + "INPUT DIM EQUAL TO 1 OF STATIC SHAPE NOT SUPPORT") + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +if __name__ == "__main__": + unittest.main()