[Paddle-TRT] support group_norm (#31040)
* add group norm plugin * fix compile problems * move concat axis check to trt op teller * add nbDims for scale and bias nv dims * add group norm unit test * fix unittest * add trt version restriction for group norm op teller * fix unittestrevert-31068-fix_conv3d_windows
Pei Yang
4 years ago
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GitHub
parent
c209751c8d
commit
00b09e86ac
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/* Copyright (c) 2021 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 <vector>
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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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class GroupNormOpConverter : 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 group_norm op";
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framework::OpDesc op_desc(op, nullptr);
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auto* input_itensor = engine_->GetITensor(op_desc.Input("X").front());
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int groups = BOOST_GET_CONST(int, op_desc.GetAttr("groups"));
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float epsilon = BOOST_GET_CONST(float, op_desc.GetAttr("epsilon"));
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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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// get the presistable var's data
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auto get_persistable_data = [&](const std::string& var_name,
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framework::DDim* dims) -> float* {
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auto* temp_var = scope.FindVar(var_name);
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auto* temp_tensor = temp_var->GetMutable<framework::LoDTensor>();
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(*dims) = temp_tensor->dims();
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auto* temp_data = engine_->GetWeightCPUData(var_name, temp_tensor, false);
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return temp_data;
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};
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framework::DDim scale_dims;
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framework::DDim bias_dims;
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float* scale_data = get_persistable_data(scale_name, &scale_dims);
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float* bias_data = get_persistable_data(bias_name, &bias_dims);
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int64_t scale_numel = framework::product(scale_dims);
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int64_t bias_numel = framework::product(bias_dims);
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TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(scale_data),
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static_cast<size_t>(scale_numel)};
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TensorRTEngine::Weight bias_weights{nvinfer1::DataType::kFLOAT,
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static_cast<void*>(bias_data),
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static_cast<size_t>(bias_numel)};
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nvinfer1::Dims scale_nv_dims;
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nvinfer1::Dims bias_nv_dims;
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scale_nv_dims.nbDims = scale_dims.size();
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bias_nv_dims.nbDims = bias_dims.size();
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for (int i = 0; i < scale_dims.size(); i++) {
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scale_nv_dims.d[i] = scale_dims.at(i);
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}
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for (int i = 0; i < bias_dims.size(); i++) {
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bias_nv_dims.d[i] = bias_dims.at(i);
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}
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auto* scale_layer = TRT_ENGINE_ADD_LAYER(engine_, Constant, scale_nv_dims,
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scale_weights.get());
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auto* bias_layer = TRT_ENGINE_ADD_LAYER(engine_, Constant, bias_nv_dims,
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bias_weights.get());
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std::vector<nvinfer1::ITensor*> plugin_inputs;
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plugin_inputs.emplace_back(input_itensor);
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plugin_inputs.emplace_back(scale_layer->getOutput(0));
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plugin_inputs.emplace_back(bias_layer->getOutput(0));
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const std::vector<nvinfer1::PluginField> fields{
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{"eps", &epsilon, nvinfer1::PluginFieldType::kFLOAT32, 1},
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{"num_groups", &groups, nvinfer1::PluginFieldType::kINT32, 1},
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};
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nvinfer1::PluginFieldCollection* plugin_collections =
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static_cast<nvinfer1::PluginFieldCollection*>(
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malloc(sizeof(*plugin_collections) +
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fields.size() * sizeof(nvinfer1::PluginField)));
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plugin_collections->nbFields = static_cast<int>(fields.size());
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plugin_collections->fields = fields.data();
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auto creator =
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GetPluginRegistry()->getPluginCreator("GroupNormalizationPlugin", "1");
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auto group_norm_plugin =
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creator->createPlugin("GroupNormalizationPlugin", plugin_collections);
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free(plugin_collections);
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auto group_norm_plugin_layer = engine_->network()->addPluginV2(
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plugin_inputs.data(), plugin_inputs.size(), *group_norm_plugin);
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auto output_name = op_desc.Output("Y")[0];
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RreplenishLayerAndOutput(group_norm_plugin_layer, "group_norm",
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{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(group_norm, GroupNormOpConverter);
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@ -0,0 +1,78 @@
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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 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 TRTGroupNormTest(InferencePassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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data = fluid.data(
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name="data", shape=[-1, 512, 12, 12], dtype="float32")
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relu_out = fluid.layers.relu(data)
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relu6_out = fluid.layers.relu6(relu_out)
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tanh_out = fluid.layers.tanh(relu6_out)
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conv_out = fluid.layers.conv2d(
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input=tanh_out,
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num_filters=512,
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filter_size=3,
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groups=1,
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padding=[1, 1],
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bias_attr=False,
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act=None)
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out = self.append_group_norm(conv_out)
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self.feeds = {
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"data": np.random.random([1, 512, 12, 12]).astype("float32"),
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}
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self.enable_trt = True
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self.trt_parameters = TRTGroupNormTest.TensorRTParam(
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1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
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self.dynamic_shape_params = TRTGroupNormTest.DynamicShapeParam({
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'data': [1, 512, 12, 12]
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}, {'data': [1, 512, 12, 12]}, {'data': [1, 512, 12, 12]}, False)
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self.fetch_list = [out]
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def append_group_norm(self, data):
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param_attr = fluid.ParamAttr(
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name='group_norm_scale',
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initializer=fluid.initializer.Constant(value=1.0))
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bias_attr = fluid.ParamAttr(
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name='group_norm_bias',
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initializer=fluid.initializer.Constant(value=0.0))
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return fluid.layers.group_norm(
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data,
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groups=32,
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epsilon=0.000009999999747378752,
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param_attr=param_attr,
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bias_attr=bias_attr)
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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use_gpu = True
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self.check_output_with_option(use_gpu)
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self.assertTrue(
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PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
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if __name__ == "__main__":
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unittest.main()
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