[Paddle-TRT] trt affine channel converter (#31628)
* trt affine channel converter * add trt affine channel base test * add trt affine channel NHWC * remove asterisk for python2 compatibility * trt affine channel converter * add trt affine channel base test * add trt affine channel NHWC * remove asterisk for python2 compatibility * fix rebase * move LodTensor to Tensor * add dbg info * affine channel converter only support NCHW * scale,bias are parameters, use create_parameters api * reduce test input size to not exceed the timelimit of ci * refine affine channel unittest and add serialization/dynamic test * change super to InferencePassTest for python2 compatibility * change super to InferencePassTest for python2 compatibility * fix affine channel fp16 serialize settingdevelop
zlsh80826
4 years ago
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/framework/data_layout.h"
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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namespace paddle {
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namespace framework {
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class Scope;
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namespace proto {
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class OpDesc;
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} // namespace proto
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} // namespace framework
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} // namespace paddle
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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/*
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* Affine Channel Op
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*/
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class AffineChannelOpConverter : public OpConverter {
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public:
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void operator()(const framework::proto::OpDesc& op,
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const framework::Scope& scope, bool test_mode) override {
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VLOG(3) << "convert a fluid affine_channel op to tensorrt scale nd layer";
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framework::OpDesc op_desc(op, nullptr);
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std::string input_name = op_desc.Input("X").front();
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std::string scale_name = op_desc.Input("Scale").front();
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std::string bias_name = op_desc.Input("Bias").front();
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std::string output_name = op_desc.Output("Out").front();
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auto input_tensor = engine_->GetITensor(input_name);
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auto idim = input_tensor->getDimensions();
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auto* scale_v = scope.FindVar(scale_name);
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auto* scale_t = scale_v->GetMutable<framework::LoDTensor>();
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float* scale_ptr = engine_->GetWeightCPUData(scale_name, scale_t, false);
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auto* bias_v = scope.FindVar(bias_name);
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auto* bias_t = bias_v->GetMutable<framework::LoDTensor>();
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float* bias_ptr = engine_->GetWeightCPUData(bias_name, bias_t, false);
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auto data_layout = framework::StringToDataLayout(
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BOOST_GET_CONST(std::string, op_desc.GetAttr("data_layout")));
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PADDLE_ENFORCE_EQ(
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data_layout, framework::DataLayout::kNCHW,
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platform::errors::InvalidArgument(
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"TensorRT affine channel converter can only convert NCHW format. "
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"Other format should be run in fluid mode. Report a bug on github "
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"issue if you see this line."));
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// tensorrt scalend layer only support spatial dims >= 2,
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// so nhwc is not availabe (spatial dims == 0)
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const int channel_axis = engine_->with_dynamic_shape();
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TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(scale_ptr),
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(size_t)idim.d[channel_axis]};
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TensorRTEngine::Weight bias_weights{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(bias_ptr),
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(size_t)idim.d[channel_axis]};
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TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
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0};
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auto layer = TRT_ENGINE_ADD_LAYER(engine_, ScaleNd, *input_tensor,
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nvinfer1::ScaleMode::kCHANNEL,
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bias_weights.get(), scale_weights.get(),
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power_weights.get(), channel_axis);
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RreplenishLayerAndOutput(layer, "affine_channel", {output_name}, test_mode);
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}
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};
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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REGISTER_TRT_OP_CONVERTER(affine_channel, AffineChannelOpConverter);
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@ -0,0 +1,141 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import itertools
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import numpy as np
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from inference_pass_test import InferencePassTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.core import PassVersionChecker
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from paddle.fluid.core import AnalysisConfig
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class TRTAffineChannelTest(InferencePassTest):
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def setUp(self):
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self.bs = 2
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self.channel = 8
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self.height = 16
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self.width = 16
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self.data_layout = 'NCHW'
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self.precision = AnalysisConfig.Precision.Float32
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self.serialize = False
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self.enable_trt = True
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def build(self):
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# set min_graph_size to 2,
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# because affine channel doesn't support nhwc format
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self.trt_parameters = InferencePassTest.TensorRTParam(
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1 << 30, self.bs, 2, self.precision, self.serialize, False)
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with fluid.program_guard(self.main_program, self.startup_program):
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if self.data_layout == 'NCHW':
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shape = [-1, self.channel, self.height, self.width]
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else:
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shape = [-1, self.height, self.width, self.channel]
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data = fluid.data(name='in', shape=shape, dtype='float32')
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# set scale, bias by constant
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scale = fluid.layers.create_parameter(
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shape=[self.channel],
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dtype='float32',
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default_initializer=fluid.initializer.Constant(2.))
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bias = fluid.layers.create_parameter(
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shape=[self.channel],
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dtype='float32',
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default_initializer=fluid.initializer.Constant(.5))
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affine_channel_out = fluid.layers.affine_channel(
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data, scale=scale, bias=bias, data_layout=self.data_layout)
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out = fluid.layers.batch_norm(affine_channel_out, is_test=True)
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shape[0] = self.bs
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self.feeds = {'in': np.random.random(shape).astype('float32'), }
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self.fetch_list = [out]
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def check_output(self):
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if core.is_compiled_with_cuda():
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use_gpu = True
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atol = 1e-5
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if self.trt_parameters.precision == AnalysisConfig.Precision.Half:
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atol = 1e-3
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self.check_output_with_option(use_gpu, atol, flatten=True)
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self.assertTrue(
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PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
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def run_test(self):
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self.build()
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self.check_output()
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def run_test_all(self):
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precision_opt = [
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AnalysisConfig.Precision.Float32, AnalysisConfig.Precision.Half
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]
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serialize_opt = [False, True]
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if self.data_layout == 'NCHW':
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min_shape = [
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self.bs, self.channel, self.height // 2, self.width // 2
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]
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max_shape = [self.bs, self.channel, self.height * 2, self.width * 2]
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opt_shape = [self.bs, self.channel, self.height, self.width]
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if self.data_layout == 'NHWC':
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min_shape = [
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self.bs, self.height // 2, self.width // 2, self.channel
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]
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max_shape = [self.bs, self.height * 2, self.width * 2, self.channel]
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opt_shape = [self.bs, self.height, self.width, self.channel]
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dynamic_shape_profile = InferencePassTest.DynamicShapeParam({
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'in': min_shape
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}, {'in': max_shape}, {'in': opt_shape}, False)
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dynamic_shape_opt = [None, dynamic_shape_profile]
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for precision, serialize, dynamic_shape in itertools.product(
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precision_opt, serialize_opt, dynamic_shape_opt):
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self.precision = precision
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self.serialize = serialize
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self.dynamic_shape_params = dynamic_shape
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self.run_test()
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def test_base(self):
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self.run_test()
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def test_fp16(self):
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self.precision = AnalysisConfig.Precision.Half
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self.run_test()
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def test_serialize(self):
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self.serialize = True
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self.run_test()
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def test_dynamic(self):
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self.dynamic_shape_params = InferencePassTest.DynamicShapeParam({
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'in': [self.bs, self.channel, self.height // 2, self.width // 2]
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}, {'in': [self.bs, self.channel, self.height * 2, self.width * 2]
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}, {'in': [self.bs, self.channel, self.height, self.width]}, False)
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self.run_test()
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def test_nchw_all(self):
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self.run_test_all()
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def test_nhwc(self):
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self.data_layout = 'NHWC'
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self.run_test_all()
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if __name__ == "__main__":
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unittest.main()
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