wehub-resource-sync
730 lines
28 KiB
Plaintext
730 lines
28 KiB
Plaintext
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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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*/
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#include "common/checkMacrosPlugin.h"
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#include "instanceNormalizationPluginLegacy.h"
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#include "instanceNormCommon.h"
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#include <algorithm>
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#include <cuda_fp16.h>
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#include <memory>
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#include <stdexcept>
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#include <string_view>
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using namespace nvinfer1;
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using namespace nvinfer1::plugin;
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using namespace nvinfer1::pluginInternal;
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using namespace instance_norm_impl;
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using nvinfer1::plugin::InstanceNormalizationPlugin;
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using nvinfer1::plugin::InstanceNormalizationPluginV2;
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using nvinfer1::plugin::InstanceNormalizationPluginCreator;
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using nvinfer1::plugin::InstanceNormalizationPluginCreatorV2;
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namespace
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{
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constexpr char const* gInstancePluginVersion{"1"};
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constexpr char const* gInstancePluginVersionV2{"2"};
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constexpr char const* gInstancePluginName{"InstanceNormalization_TRT"};
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} // namespace
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InstanceNormalizationPlugin::InstanceNormalizationPlugin(
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float epsilon, std::vector<float> const& scale, std::vector<float> const& bias, int32_t relu, float alpha)
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: mEpsilon(epsilon)
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, mAlpha(alpha)
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, mRelu(relu)
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, mNchan(scale.size())
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, mHostScale(scale)
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, mHostBias(bias)
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{
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PLUGIN_VALIDATE(scale.size() == bias.size());
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}
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InstanceNormalizationPlugin::InstanceNormalizationPlugin(
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float epsilon, nvinfer1::Weights const& scale, nvinfer1::Weights const& bias, int32_t relu, float alpha)
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: mEpsilon(epsilon)
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, mAlpha(alpha)
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, mRelu(relu)
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, mNchan(scale.count)
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{
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PLUGIN_VALIDATE(scale.count == bias.count);
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auto const copyWeights = [](nvinfer1::Weights const& input, std::vector<float>& output)
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{
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output.reserve(input.count);
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if (input.type == nvinfer1::DataType::kFLOAT)
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{
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output.assign(
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static_cast<float const*>(input.values), static_cast<float const*>(input.values) + input.count);
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}
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else if (input.type == nvinfer1::DataType::kHALF)
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{
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for (int32_t c = 0; c < input.count; ++c)
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{
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auto const value = static_cast<unsigned short const*>(input.values);
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output.push_back(__internal_half2float(value[c]));
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}
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}
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else
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{
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PLUGIN_ERROR("Unsupported scale/bias dtype");
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}
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};
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copyWeights(scale, mHostScale);
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copyWeights(bias, mHostBias);
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}
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InstanceNormalizationPlugin::InstanceNormalizationPlugin(void const* serialData, size_t serialLength)
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{
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deserialize_value(&serialData, &serialLength, &mEpsilon);
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deserialize_value(&serialData, &serialLength, &mNchan);
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deserialize_value(&serialData, &serialLength, &mHostScale);
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deserialize_value(&serialData, &serialLength, &mHostBias);
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deserialize_value(&serialData, &serialLength, &mRelu);
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deserialize_value(&serialData, &serialLength, &mAlpha);
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}
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InstanceNormalizationPlugin::~InstanceNormalizationPlugin()
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{
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terminate();
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}
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// InstanceNormalizationPlugin returns one output.
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int32_t InstanceNormalizationPlugin::getNbOutputs() const noexcept
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{
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return 1;
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}
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DimsExprs InstanceNormalizationPlugin::getOutputDimensions(int32_t outputIndex, nvinfer1::DimsExprs const* inputs,
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int32_t nbInputs, nvinfer1::IExprBuilder& exprBuilder) noexcept
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{
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nvinfer1::DimsExprs output(inputs[0]);
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return output;
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}
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int32_t InstanceNormalizationPlugin::initialize() noexcept
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{
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if (!mInitialized)
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{
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnCreate(&mCudnnHandle));
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnCreateTensorDescriptor(&mBDescriptor));
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnCreateTensorDescriptor(&mXDescriptor));
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnCreateTensorDescriptor(&mYDescriptor));
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// NDHWC path
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// Device info.
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int32_t device;
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PLUGIN_CHECK_CUDA(cudaGetDevice(&device));
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cudaDeviceProp props;
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PLUGIN_CHECK_CUDA(cudaGetDeviceProperties(&props, device));
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mContext.sm_count = props.multiProcessorCount;
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mContext.sm_shared_size = props.sharedMemPerMultiprocessor;
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mContext.sm_version = props.major * 100 + props.minor * 10;
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PLUGIN_CHECK_CUDA(cudaMalloc(&mDeviceScale, mNchan * sizeof(float)));
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PLUGIN_CHECK_CUDA(cudaMalloc(&mDeviceBias, mNchan * sizeof(float)));
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PLUGIN_CHECK_CUDA(cudaMemcpy(mDeviceScale, mHostScale.data(), mNchan * sizeof(float), cudaMemcpyHostToDevice));
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PLUGIN_CHECK_CUDA(cudaMemcpy(mDeviceBias, mHostBias.data(), mNchan * sizeof(float), cudaMemcpyHostToDevice));
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PLUGIN_CHECK_CUDA(cudaDriverGetVersion(&mCudaDriverVersion));
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}
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mInitialized = true;
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return 0;
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}
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void InstanceNormalizationPlugin::terminate() noexcept
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{
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if (mInitialized)
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{
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnDestroyTensorDescriptor(mYDescriptor));
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnDestroyTensorDescriptor(mXDescriptor));
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnDestroyTensorDescriptor(mBDescriptor));
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnDestroy(mCudnnHandle));
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PLUGIN_CUASSERT(cudaFree(mDeviceBias));
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PLUGIN_CUASSERT(cudaFree(mDeviceScale));
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}
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mInitialized = false;
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}
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size_t InstanceNormalizationPlugin::getWorkspaceSize(nvinfer1::PluginTensorDesc const* inputs, int32_t nbInputs,
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nvinfer1::PluginTensorDesc const* outputs, int32_t nbOutputs) const noexcept
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{
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nvinfer1::Dims input_dims = inputs[0].dims;
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PLUGIN_ASSERT(input_dims.nbDims == 4 || input_dims.nbDims == 5);
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if (inputs[0].format == nvinfer1::PluginFormat::kLINEAR)
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{
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nvinfer1::Dims input_dims = inputs[0].dims;
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int32_t n = input_dims.d[0];
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int32_t c = input_dims.d[1];
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size_t nchan_bytes = c * sizeof(float);
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size_t scale_size = n * nchan_bytes;
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size_t bias_size = n * nchan_bytes;
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size_t total_wss = scale_size + bias_size;
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return total_wss;
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}
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else if (inputs[0].format == nvinfer1::PluginFormat::kDHWC8 || inputs[0].format == nvinfer1::PluginFormat::kCDHW32)
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{
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PLUGIN_ASSERT(input_dims.nbDims == 5);
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int32_t input_data_type = (inputs[0].type == nvinfer1::DataType::kHALF) ? 1 : 2;
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int32_t output_data_type = (outputs[0].type == nvinfer1::DataType::kHALF) ? 1 : 2;
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nvinfer1::Dims input_dims = inputs[0].dims;
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int32_t n = input_dims.d[0];
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int32_t c = input_dims.d[1];
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int32_t d = input_dims.d[2];
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int32_t h = input_dims.d[3];
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int32_t w = input_dims.d[4];
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InstanceNormFwdParams params{};
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// only these parameters are required for workspace computation
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params.nhw = d * h * w;
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params.c = c;
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params.n = n;
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// Reserve memory for the workspaces.
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size_t size_sums, size_counts, size_retired_ctas;
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instanceNormBufferSizesDispatch(
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mContext, params, size_sums, size_counts, size_retired_ctas, input_data_type, output_data_type);
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size_t size_nc = n * c * sizeof(float);
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size_nc = ((size_nc + 256 - 1) / 256) * 256;
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return size_sums + size_counts + size_retired_ctas + 4 * size_nc;
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}
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else
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{
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PLUGIN_ASSERT(0);
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}
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return 0;
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}
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int32_t InstanceNormalizationPlugin::enqueue(nvinfer1::PluginTensorDesc const* inputDesc,
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nvinfer1::PluginTensorDesc const* outputDesc, void const* const* inputs, void* const* outputs, void* workspace,
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cudaStream_t stream) noexcept
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{
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PLUGIN_VALIDATE(inputDesc != nullptr && outputDesc != nullptr && inputs != nullptr && outputs != nullptr && workspace != nullptr);
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nvinfer1::Dims input_dims = inputDesc[0].dims;
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// early return for empty tensor
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if (std::any_of(input_dims.d, input_dims.d + input_dims.nbDims, [](int32_t d) { return d == 0; }))
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{
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return 0;
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}
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auto const callRelu = [this, &stream](void* inOut, int32_t count, nvinfer1::DataType type) {
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if (mRelu > 0)
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{
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int32_t constexpr kBLOCK_SZ = 256;
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switch (type)
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{
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case nvinfer1::DataType::kFLOAT:
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in3dReluActivation<float, kBLOCK_SZ><<<divUp(count, kBLOCK_SZ), kBLOCK_SZ, 0, stream>>>(
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static_cast<float*>(inOut), static_cast<float*>(inOut), mAlpha, count);
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break;
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case nvinfer1::DataType::kHALF:
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in3dReluActivation<__half, kBLOCK_SZ><<<divUp(count, kBLOCK_SZ), kBLOCK_SZ, 0, stream>>>(
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static_cast<__half*>(inOut), static_cast<__half*>(inOut), mAlpha, count);
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break;
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default: PLUGIN_ASSERT(0);
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}
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}
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};
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if (input_dims.nbDims <= 4)
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{
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nvinfer1::Dims input_dims = inputDesc[0].dims;
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int32_t n = input_dims.d[0];
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int32_t c = input_dims.d[1];
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int32_t h = input_dims.d[2];
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int32_t w = input_dims.nbDims > 3 ? input_dims.d[3] : 1;
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size_t nchan_bytes = c * sizeof(float);
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float* _d_array = static_cast<float*>(workspace);
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float* d_scale = &_d_array[0];
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float* d_bias = &_d_array[n * c];
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for (int32_t i = 0; i < n; ++i)
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{
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PLUGIN_CUASSERT(
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cudaMemcpyAsync(d_scale + i * c, mDeviceScale, nchan_bytes, cudaMemcpyDeviceToDevice, stream));
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PLUGIN_CUASSERT(
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cudaMemcpyAsync(d_bias + i * c, mDeviceBias, nchan_bytes, cudaMemcpyDeviceToDevice, stream));
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}
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PLUGIN_CUDNNASSERT(
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mCudnnWrapper.cudnnSetTensor4dDescriptor(mBDescriptor, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, n * c, 1, 1));
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cudnnDataType_t cudnn_dtype{};
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PLUGIN_CUDNNASSERT(convertTrt2cudnnDtype(inputDesc[0].type, &cudnn_dtype));
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnSetTensor4dDescriptor(mXDescriptor, CUDNN_TENSOR_NCHW, cudnn_dtype, 1, n * c, h, w));
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnSetTensor4dDescriptor(mYDescriptor, CUDNN_TENSOR_NCHW, cudnn_dtype, 1, n * c, h, w));
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float alpha = 1;
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float beta = 0;
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void const* x_ptr = inputs[0];
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void* y_ptr = outputs[0];
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnSetStream(mCudnnHandle, stream));
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// Note: Use of CUDNN_BATCHNORM_SPATIAL_PERSISTENT can cause numerical
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// overflows (NaNs) for fp32 data in some circumstances. The lower-
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// performance CUDNN_BATCHNORM_SPATIAL should be used if this is not
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// acceptable.
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cudnnBatchNormMode_t cudnnBatchNormMode = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
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cudaStreamCaptureStatus streamStatus;
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PLUGIN_CHECK_CUDA(cudaStreamIsCapturing(stream, &streamStatus));
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if (streamStatus != cudaStreamCaptureStatusNone && mCudaDriverVersion < 11000)
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{
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gLogVerbose << "Using CUDNN_BATCHNORM_SPATIAL as a CUDA graph capture is in progress but the CUDA version "
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"may have issues with using CUDNN_BATCHNORM_SPATIAL_PERSISTENT"
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<< std::endl;
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cudnnBatchNormMode = CUDNN_BATCHNORM_SPATIAL;
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}
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PLUGIN_CUDNNASSERT(mCudnnWrapper.cudnnBatchNormalizationForwardTraining(mCudnnHandle, cudnnBatchNormMode,
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&alpha, &beta, mXDescriptor, x_ptr, mYDescriptor, y_ptr, mBDescriptor, d_scale, d_bias, 1., nullptr,
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nullptr, mEpsilon, nullptr, nullptr));
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callRelu(y_ptr, n * c * h * w, inputDesc[0].type);
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}
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else
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{
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if (inputDesc[0].format == nvinfer1::PluginFormat::kLINEAR)
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{
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnSetStream(mCudnnHandle, stream));
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nvinfer1::Dims input_dims = inputDesc[0].dims;
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int32_t n = input_dims.d[0];
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int32_t c = input_dims.d[1];
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int32_t d = input_dims.d[2];
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int32_t h = input_dims.d[3];
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int32_t w = input_dims.d[4];
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size_t nchan_bytes = c * sizeof(float);
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// Note: We repeat the data for each batch entry so that we can do the full
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// computation in a single CUDNN call in enqueue().
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float* _d_array = (float*) workspace;
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float* d_scale = &_d_array[0];
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float* d_bias = &_d_array[n * c];
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for (int32_t i = 0; i < n; ++i)
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{
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PLUGIN_CHECK_CUDA(
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cudaMemcpyAsync(d_scale + i * c, mDeviceScale, nchan_bytes, cudaMemcpyDeviceToDevice, stream));
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PLUGIN_CHECK_CUDA(
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cudaMemcpyAsync(d_bias + i * c, mDeviceBias, nchan_bytes, cudaMemcpyDeviceToDevice, stream));
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}
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int32_t nc_dimA[] = {1, n * c, 1, 1, 1};
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int32_t nc_strideA[] = {nc_dimA[1] * nc_dimA[2] * nc_dimA[3] * nc_dimA[4],
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nc_dimA[2] * nc_dimA[3] * nc_dimA[4], nc_dimA[3] * nc_dimA[4], nc_dimA[4], 1};
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int32_t img_dimA[] = {1, n * c, d, h, w};
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int32_t img_strideA[] = {img_dimA[1] * img_dimA[2] * img_dimA[3] * img_dimA[4],
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img_dimA[2] * img_dimA[3] * img_dimA[4], img_dimA[3] * img_dimA[4], img_dimA[4], 1};
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnSetTensorNdDescriptor(mBDescriptor, CUDNN_DATA_FLOAT, 5, nc_dimA, nc_strideA));
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cudnnDataType_t cudnn_dtype;
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PLUGIN_CHECK_CUDNN(convertTrt2cudnnDtype(inputDesc[0].type, &cudnn_dtype));
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnSetTensorNdDescriptor(mXDescriptor, cudnn_dtype, 5, img_dimA, img_strideA));
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnSetTensorNdDescriptor(mYDescriptor, cudnn_dtype, 5, img_dimA, img_strideA));
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float alpha = 1;
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float beta = 0;
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void const* x_ptr = inputs[0];
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void* y_ptr = outputs[0];
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// Note: Use of CUDNN_BATCHNORM_SPATIAL_PERSISTENT can cause numerical
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// overflows (NaNs) for fp32 data in some circumstances. The lower-
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// performance CUDNN_BATCHNORM_SPATIAL should be used if this is not
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// acceptable.
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cudnnBatchNormMode_t cudnnBatchNormMode = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
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cudaStreamCaptureStatus streamStatus;
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PLUGIN_CHECK_CUDA(cudaStreamIsCapturing(stream, &streamStatus));
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if (streamStatus != cudaStreamCaptureStatusNone && mCudaDriverVersion < 11000)
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{
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gLogVerbose
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<< "Using CUDNN_BATCHNORM_SPATIAL as a CUDA graph capture is in progress but the CUDA version "
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"may have issues with using CUDNN_BATCHNORM_SPATIAL_PERSISTENT"
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<< std::endl;
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cudnnBatchNormMode = CUDNN_BATCHNORM_SPATIAL;
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}
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PLUGIN_CHECK_CUDNN(mCudnnWrapper.cudnnBatchNormalizationForwardTraining(mCudnnHandle, cudnnBatchNormMode,
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&alpha, &beta, mXDescriptor, x_ptr, mYDescriptor, y_ptr, mBDescriptor, d_scale, d_bias, 1., nullptr,
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nullptr, mEpsilon, nullptr, nullptr));
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callRelu(y_ptr, n * c * d * h * w, inputDesc[0].type);
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}
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else if (inputDesc[0].format == nvinfer1::PluginFormat::kDHWC8
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|| inputDesc[0].format == nvinfer1::PluginFormat::kCDHW32)
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{
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int32_t input_data_type = (inputDesc[0].type == nvinfer1::DataType::kHALF) ? 1 : 2;
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int32_t output_data_type = (outputDesc[0].type == nvinfer1::DataType::kHALF) ? 1 : 2;
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nvinfer1::Dims input_dims = inputDesc[0].dims;
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int32_t n = input_dims.d[0];
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int32_t c = input_dims.d[1];
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int32_t d = input_dims.d[2];
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int32_t h = input_dims.d[3];
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int32_t w = input_dims.d[4];
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InstanceNormFwdParams params{};
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params.nhw = d * h * w;
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params.c = c;
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params.n = n;
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size_t size_sums, size_counts, size_retired_ctas;
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instanceNormBufferSizesDispatch(
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mContext, params, size_sums, size_counts, size_retired_ctas, input_data_type, output_data_type);
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size_t size_nc = n * c * sizeof(float);
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size_nc = ((size_nc + 256 - 1) / 256) * 256;
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char* d_buf = static_cast<char*>(workspace);
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params.gmem_sums = reinterpret_cast<GMEM_SUMS_TYPE*>(d_buf);
|
|
d_buf += size_sums;
|
|
params.gmem_counts = reinterpret_cast<int32_t*>(d_buf);
|
|
d_buf += size_counts;
|
|
params.gmem_retired_ctas = reinterpret_cast<int32_t*>(d_buf);
|
|
d_buf += size_retired_ctas;
|
|
params.gmem_running_mean = reinterpret_cast<float*>(d_buf);
|
|
d_buf += size_nc;
|
|
params.gmem_running_var = reinterpret_cast<float*>(d_buf);
|
|
d_buf += size_nc;
|
|
params.gmem_saved_mean = reinterpret_cast<float*>(d_buf);
|
|
d_buf += size_nc;
|
|
params.gmem_saved_var = reinterpret_cast<float*>(d_buf);
|
|
d_buf += size_nc;
|
|
|
|
params.gmem_src = inputs[0];
|
|
params.gmem_dst = outputs[0];
|
|
params.gmem_bias = mDeviceBias;
|
|
params.gmem_scale = mDeviceScale;
|
|
|
|
params.var_eps = mEpsilon;
|
|
params.exp_avg_factor = 1.F; //(float)exp_avg_factor;
|
|
params.use_relu = mRelu; // use_relu;
|
|
params.relu_alpha = mAlpha; // relu_alpha;
|
|
|
|
params.in_scale = inputDesc[0].scale;
|
|
PLUGIN_ASSERT(outputDesc[0].scale != 0.F);
|
|
params.out_scale = 1.F / outputDesc[0].scale;
|
|
|
|
instanceNormFwdDispatch(mContext, params, stream, input_data_type, output_data_type);
|
|
}
|
|
else
|
|
{
|
|
PLUGIN_FAIL("Unexpected input format");
|
|
}
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
size_t InstanceNormalizationPlugin::getSerializationSize() const noexcept
|
|
{
|
|
return (serialized_size(mEpsilon) + serialized_size(mNchan) + serialized_size(mHostScale)
|
|
+ serialized_size(mHostBias) + serialized_size(mRelu) + serialized_size(mAlpha));
|
|
}
|
|
|
|
void InstanceNormalizationPlugin::serialize(void* buffer) const noexcept
|
|
{
|
|
serialize_value(&buffer, mEpsilon);
|
|
serialize_value(&buffer, mNchan);
|
|
serialize_value(&buffer, mHostScale);
|
|
serialize_value(&buffer, mHostBias);
|
|
serialize_value(&buffer, mRelu);
|
|
serialize_value(&buffer, mAlpha);
|
|
}
|
|
|
|
bool InstanceNormalizationPlugin::supportsFormatCombination(
|
|
int32_t pos, nvinfer1::PluginTensorDesc const* inOut, int32_t nbInputs, int32_t nbOutputs) noexcept
|
|
{
|
|
PLUGIN_ASSERT(inOut && pos < (nbInputs + nbOutputs));
|
|
PLUGIN_ASSERT(pos == 0 || pos == 1);
|
|
|
|
// For 4-D or 3-D tensor (nbSpatialDims == 1 or 2), only FP32_Linear and FP16_Linear are supported.
|
|
// For 5-D tensor (nbSpatialDims == 3), FP32_Linear, FP16_Linear, FP16_DHWC8, and INT8_CDHW32 are supported.
|
|
// This is because we have special InstanceNorm3D kernels for vectorized formats from MLPerf-Inference.
|
|
|
|
int32_t const nbDims = inOut[pos].dims.nbDims;
|
|
PLUGIN_ASSERT(nbDims >= 3);
|
|
PLUGIN_ASSERT(nbDims <= 5);
|
|
bool const is3DInstanceNorm = (nbDims == 5);
|
|
|
|
bool const isFP32Linear
|
|
= (inOut[pos].type == nvinfer1::DataType::kFLOAT && inOut[pos].format == nvinfer1::PluginFormat::kLINEAR
|
|
&& inOut[pos].type == inOut[0].type && inOut[pos].format == inOut[0].format);
|
|
|
|
bool const isFP16Linear
|
|
= (inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == nvinfer1::PluginFormat::kLINEAR
|
|
&& inOut[pos].type == inOut[0].type && inOut[pos].format == inOut[0].format);
|
|
|
|
bool const isFP16DHWC8
|
|
= (inOut[pos].type == nvinfer1::DataType::kHALF && inOut[pos].format == nvinfer1::PluginFormat::kDHWC8
|
|
&& inOut[pos].type == inOut[0].type && inOut[pos].format == inOut[0].format);
|
|
|
|
bool const isINT8CDHW32
|
|
= (inOut[pos].type == nvinfer1::DataType::kINT8 && inOut[pos].format == nvinfer1::PluginFormat::kCDHW32
|
|
&& inOut[pos].type == inOut[0].type && inOut[pos].format == inOut[0].format);
|
|
|
|
bool const isFormatOK = isFP32Linear || isFP16Linear || (is3DInstanceNorm && (isFP16DHWC8 || isINT8CDHW32));
|
|
|
|
// Kernels for vectorized formats only support the case of C % spv == 0.
|
|
int32_t spv{1};
|
|
switch (inOut[pos].format)
|
|
{
|
|
case nvinfer1::PluginFormat::kDHWC8: spv = 8; break;
|
|
case nvinfer1::PluginFormat::kCDHW32: spv = 32; break;
|
|
default: break;
|
|
}
|
|
int32_t const isAlignmentOK = (inOut[pos].dims.d[1] % spv == 0);
|
|
|
|
return isFormatOK && isAlignmentOK;
|
|
}
|
|
|
|
char const* InstanceNormalizationPlugin::getPluginType() const noexcept
|
|
{
|
|
return gInstancePluginName;
|
|
}
|
|
|
|
char const* InstanceNormalizationPlugin::getPluginVersion() const noexcept
|
|
{
|
|
return gInstancePluginVersion;
|
|
}
|
|
|
|
char const* InstanceNormalizationPluginV2::getPluginVersion() const noexcept
|
|
{
|
|
return gInstancePluginVersionV2;
|
|
}
|
|
|
|
void InstanceNormalizationPlugin::destroy() noexcept
|
|
{
|
|
delete this;
|
|
}
|
|
|
|
template <class PluginType>
|
|
IPluginV2DynamicExt* InstanceNormalizationPlugin::cloneBase() const noexcept
|
|
{
|
|
try
|
|
{
|
|
auto plugin = std::make_unique<PluginType>(mEpsilon, mHostScale, mHostBias, mRelu, mAlpha);
|
|
plugin->setPluginNamespace(mPluginNamespace.c_str());
|
|
plugin->initialize();
|
|
return plugin.release();
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
IPluginV2DynamicExt* InstanceNormalizationPlugin::clone() const noexcept
|
|
{
|
|
return cloneBase<InstanceNormalizationPlugin>();
|
|
}
|
|
|
|
IPluginV2DynamicExt* InstanceNormalizationPluginV2::clone() const noexcept
|
|
{
|
|
return cloneBase<InstanceNormalizationPluginV2>();
|
|
}
|
|
|
|
// Set plugin namespace
|
|
void InstanceNormalizationPlugin::setPluginNamespace(char const* pluginNamespace) noexcept
|
|
{
|
|
mPluginNamespace = pluginNamespace;
|
|
}
|
|
|
|
char const* InstanceNormalizationPlugin::getPluginNamespace() const noexcept
|
|
{
|
|
return mPluginNamespace.c_str();
|
|
}
|
|
|
|
nvinfer1::DataType InstanceNormalizationPlugin::getOutputDataType(
|
|
int32_t index, nvinfer1::DataType const* inputTypes, int32_t nbInputs) const noexcept
|
|
{
|
|
PLUGIN_ASSERT(inputTypes && nbInputs > 0 && index == 0);
|
|
return inputTypes[0];
|
|
}
|
|
|
|
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
|
|
void InstanceNormalizationPlugin::attachToContext(
|
|
cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) noexcept
|
|
{
|
|
}
|
|
|
|
// Detach the plugin object from its execution context.
|
|
void InstanceNormalizationPlugin::detachFromContext() noexcept {}
|
|
|
|
void InstanceNormalizationPlugin::configurePlugin(nvinfer1::DynamicPluginTensorDesc const* in, int32_t nbInputs,
|
|
nvinfer1::DynamicPluginTensorDesc const* out, int32_t nbOutputs) noexcept
|
|
{
|
|
// Not support dynamic shape in C dimension
|
|
PLUGIN_ASSERT(nbInputs == 1 && in[0].desc.dims.d[1] != -1);
|
|
}
|
|
|
|
// InstanceNormalizationPluginCreator methods
|
|
InstanceNormalizationPluginCreator::InstanceNormalizationPluginCreator()
|
|
{
|
|
mPluginAttributes.clear();
|
|
mPluginAttributes.emplace_back(PluginField("epsilon", nullptr, PluginFieldType::kFLOAT32, 1));
|
|
mPluginAttributes.emplace_back(PluginField("scales", nullptr, PluginFieldType::kFLOAT32, 1));
|
|
mPluginAttributes.emplace_back(PluginField("bias", nullptr, PluginFieldType::kFLOAT32, 1));
|
|
mPluginAttributes.emplace_back(PluginField("relu", nullptr, PluginFieldType::kINT32, 1));
|
|
mPluginAttributes.emplace_back(PluginField("alpha", nullptr, PluginFieldType::kFLOAT32, 1));
|
|
|
|
mFC.nbFields = mPluginAttributes.size();
|
|
mFC.fields = mPluginAttributes.data();
|
|
}
|
|
|
|
char const* InstanceNormalizationPluginCreator::getPluginName() const noexcept
|
|
{
|
|
return gInstancePluginName;
|
|
}
|
|
|
|
char const* InstanceNormalizationPluginCreator::getPluginVersion() const noexcept
|
|
{
|
|
return gInstancePluginVersion;
|
|
}
|
|
|
|
char const* InstanceNormalizationPluginCreatorV2::getPluginVersion() const noexcept
|
|
{
|
|
return gInstancePluginVersionV2;
|
|
}
|
|
|
|
PluginFieldCollection const* InstanceNormalizationPluginCreator::getFieldNames() noexcept
|
|
{
|
|
return &mFC;
|
|
}
|
|
|
|
template <class PluginType>
|
|
IPluginV2DynamicExt* InstanceNormalizationPluginCreator::createPluginBase(
|
|
char const* name, nvinfer1::PluginFieldCollection const* fc) noexcept
|
|
{
|
|
try
|
|
{
|
|
using namespace std::string_view_literals;
|
|
std::vector<float> scaleValues;
|
|
std::vector<float> biasValues;
|
|
float epsilon{};
|
|
int32_t relu{};
|
|
float alpha{};
|
|
PluginField const* fields = fc->fields;
|
|
for (int32_t i = 0; i < fc->nbFields; ++i)
|
|
{
|
|
std::string_view const attrName = fields[i].name;
|
|
if (attrName == "epsilon"sv)
|
|
{
|
|
PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
|
|
epsilon = *(static_cast<float const*>(fields[i].data));
|
|
}
|
|
else if (attrName == "scales"sv)
|
|
{
|
|
PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
|
|
int32_t size = fields[i].length;
|
|
scaleValues.reserve(size);
|
|
auto const* w = static_cast<float const*>(fields[i].data);
|
|
for (int32_t j = 0; j < size; j++)
|
|
{
|
|
scaleValues.push_back(*w);
|
|
w++;
|
|
}
|
|
}
|
|
else if (attrName == "bias"sv)
|
|
{
|
|
PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
|
|
int32_t size = fields[i].length;
|
|
biasValues.reserve(size);
|
|
auto const* w = static_cast<float const*>(fields[i].data);
|
|
for (int32_t j = 0; j < size; j++)
|
|
{
|
|
biasValues.push_back(*w);
|
|
w++;
|
|
}
|
|
}
|
|
else if (attrName == "relu"sv)
|
|
{
|
|
PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kINT32);
|
|
relu = *(static_cast<int32_t const*>(fields[i].data));
|
|
}
|
|
else if (attrName == "alpha"sv)
|
|
{
|
|
PLUGIN_VALIDATE(fields[i].type == PluginFieldType::kFLOAT32);
|
|
alpha = *(static_cast<float const*>(fields[i].data));
|
|
}
|
|
}
|
|
|
|
Weights scaleWeights{DataType::kFLOAT, scaleValues.data(), (int64_t) scaleValues.size()};
|
|
Weights biasWeights{DataType::kFLOAT, biasValues.data(), (int64_t) biasValues.size()};
|
|
|
|
auto obj = std::make_unique<PluginType>(epsilon, scaleWeights, biasWeights, relu, alpha);
|
|
obj->setPluginNamespace(mNamespace.c_str());
|
|
obj->initialize();
|
|
return obj.release();
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
IPluginV2DynamicExt* InstanceNormalizationPluginCreator::createPlugin(
|
|
char const* name, nvinfer1::PluginFieldCollection const* fc) noexcept
|
|
{
|
|
return createPluginBase<InstanceNormalizationPlugin>(name, fc);
|
|
}
|
|
|
|
IPluginV2DynamicExt* InstanceNormalizationPluginCreatorV2::createPlugin(
|
|
char const* name, nvinfer1::PluginFieldCollection const* fc) noexcept
|
|
{
|
|
return createPluginBase<InstanceNormalizationPluginV2>(name, fc);
|
|
}
|
|
|
|
template <class PluginType>
|
|
IPluginV2DynamicExt* InstanceNormalizationPluginCreator::deserializePluginBase(
|
|
char const* name, void const* serialData, size_t serialLength) noexcept
|
|
{
|
|
try
|
|
{
|
|
auto obj = std::make_unique<PluginType>(serialData, serialLength);
|
|
obj->setPluginNamespace(mNamespace.c_str());
|
|
obj->initialize();
|
|
return obj.release();
|
|
}
|
|
catch (std::exception const& e)
|
|
{
|
|
caughtError(e);
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
IPluginV2DynamicExt* InstanceNormalizationPluginCreator::deserializePlugin(
|
|
char const* name, void const* serialData, size_t serialLength) noexcept
|
|
{
|
|
return deserializePluginBase<InstanceNormalizationPlugin>(name, serialData, serialLength);
|
|
}
|
|
|
|
IPluginV2DynamicExt* InstanceNormalizationPluginCreatorV2::deserializePlugin(
|
|
char const* name, void const* serialData, size_t serialLength) noexcept
|
|
{
|
|
return deserializePluginBase<InstanceNormalizationPluginV2>(name, serialData, serialLength);
|
|
}
|