chore: import upstream snapshot with attribution

paddle/fluid/inference/tensorrt/plugin/common/bertCommon.h

@@ -0,0 +1,222 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+// SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION &
+// AFFILIATES. 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.
+
+#pragma once
+
+#include <cublas_v2.h>
+#include <cuda_fp16.h>
+#include <cuda_runtime_api.h>
+#include <algorithm>
+#include <cassert>
+#include <memory>
+#include <numeric>
+#include <stdexcept>
+#include <vector>
+#include "NvInfer.h"
+#include "NvInferRuntimeCommon.h"
+#include "paddle/fluid/inference/tensorrt/plugin/common/plugin.h"
+
+#define TRT_UNUSED (void)
+#define BERT_PRINT_DEBUG_MSG 0
+#if BERT_PRINT_DEBUG_MSG
+#define TRANSFORMER_DEBUG_MSG(msg) (gLogVerbose << (msg) << std::endl)
+#define BERT_DEBUG_VALUE(key, value) (gLogVerbose << key << value << std::endl)
+#else
+#define TRANSFORMER_DEBUG_MSG(msg) TRT_UNUSED(msg)
+#define BERT_DEBUG_VALUE(key, value) \
+  TRT_UNUSED(key);                   \
+  TRT_UNUSED(value)
+#endif
+using half = __half;
+constexpr uint32_t BDIM = 1;  // batch dimension
+constexpr uint32_t SDIM = 0;  // seq len dimension
+constexpr uint32_t HDIM = 2;  // hidden dimension
+constexpr int32_t kSM_53 = 53;
+constexpr int32_t kSM_70 = 70;
+constexpr int32_t kSM_72 = 72;
+constexpr int32_t kSM_75 = 75;
+constexpr int32_t kSM_80 = 80;
+constexpr int32_t kSM_86 = 86;
+constexpr int32_t kSM_87 = 87;
+constexpr size_t threadsPerCta128 = 2 * 2 * 32;
+constexpr size_t threadsPerCta384 = 1 * 8 * 32;
+
+// The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M
+// dimension: (s + 16*warps_m - 1) / (16*warps_m);
+constexpr size_t xmmasM128 = 4;
+constexpr size_t xmmasM384 = 24;
+
+// Packed mask size per batch. Layout is XMMAS_M * THREADS_PER_CTA.
+constexpr size_t unfusedMaskSize = 1;
+constexpr size_t packedMaskSize64 = xmmasM128 * threadsPerCta128;
+constexpr size_t packedMaskSize96 = xmmasM128 * threadsPerCta128;
+constexpr size_t packedMaskSize128 = xmmasM128 * threadsPerCta128;
+constexpr size_t packedMaskSize384 = xmmasM384 * threadsPerCta384;
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
+
+inline uint32_t getElementSize(nvinfer1::DataType t) noexcept {
+  switch (t) {
+    case nvinfer1::DataType::kINT32:
+      return 4;
+    case nvinfer1::DataType::kFLOAT:
+      return 4;
+    case nvinfer1::DataType::kHALF:
+      return 2;
+    case nvinfer1::DataType::kBOOL:
+    case nvinfer1::DataType::kINT8:
+      return 1;
+    default:
+      return 0;
+  }
+}
+
+inline int64_t getWeightsSize(const nvinfer1::Weights& w,
+                              nvinfer1::DataType type) {
+  return w.count * getElementSize(type);
+}
+
+template <typename T>
+inline void serFromDev(char** buffer, const T* data, size_t nbElem) {
+  const size_t len = sizeof(T) * nbElem;
+  cudaMemcpy(
+      *buffer, static_cast<const void*>(data), len, cudaMemcpyDeviceToHost);
+  *buffer += len;
+}
+
+template <typename T>
+struct CudaDeleter {
+  void operator()(T* buf) { cudaFree(buf); }
+};
+
+template <typename T>
+using cuda_unique_ptr = std::unique_ptr<T, CudaDeleter<T>>;
+
+template <typename T>
+using cuda_shared_ptr = std::shared_ptr<T>;
+
+template <typename T>
+void make_cuda_shared(cuda_shared_ptr<T>* ptr, void* cudaMem) {
+  ptr->reset(static_cast<T*>(cudaMem), CudaDeleter<T>());
+}
+
+struct WeightsWithOwnership : public nvinfer1::Weights {
+  WeightsWithOwnership() {
+    values = nullptr;
+    count = 0;
+  }
+  ~WeightsWithOwnership() { operator delete[](const_cast<void*>(values)); }
+
+  WeightsWithOwnership(const WeightsWithOwnership&) = delete;
+  WeightsWithOwnership operator=(const WeightsWithOwnership&) = delete;
+  WeightsWithOwnership(const WeightsWithOwnership&&) = delete;
+  WeightsWithOwnership operator=(const WeightsWithOwnership&&) = delete;
+
+  void convertAndCopy(const nvinfer1::Weights& src, nvinfer1::DataType type) {
+    this->type = type;
+    this->count = src.count;
+    if (type == nvinfer1::DataType::kFLOAT) {
+      auto destBuf = new float[src.count];
+      this->values = destBuf;
+      if (src.type == nvinfer1::DataType::kFLOAT) {
+        TRANSFORMER_DEBUG_MSG("Float Weights(Host) => Float Array(Host)");
+        std::copy_n(static_cast<const float*>(src.values), src.count, destBuf);
+      } else {
+        assert(src.type == nvinfer1::DataType::kHALF);
+        TRANSFORMER_DEBUG_MSG("Half Weights(Host) => Float Array(Host)");
+        const auto s = static_cast<const half*>(src.values);
+        auto d = static_cast<float*>(const_cast<void*>(this->values));
+        for (auto it = 0; it < src.count; it++) {
+          d[it] = __half2float(s[it]);
+        }
+      }
+    } else if (type == nvinfer1::DataType::kHALF) {
+      auto destBuf = new half[src.count];
+      this->values = destBuf;
+      if (src.type == nvinfer1::DataType::kHALF) {
+        TRANSFORMER_DEBUG_MSG("Half Weights(Host) => Half Array(Host)");
+        std::copy_n(static_cast<const half*>(src.values), src.count, destBuf);
+      } else {
+        assert(src.type == nvinfer1::DataType::kFLOAT);
+        TRANSFORMER_DEBUG_MSG("Float Weights(Host) => Half Array(Host)");
+        const auto s = static_cast<const float*>(src.values);
+        auto d = static_cast<half*>(const_cast<void*>(this->values));
+        for (auto it = 0; it < src.count; it++) {
+          d[it] = __float2half(s[it]);
+        }
+      }
+    } else {
+      throw std::runtime_error("Unsupported DataType specified for plugin.");
+    }
+  }
+
+  void convertAndCopy(const char** srcBuf,
+                      size_t count,
+                      nvinfer1::DataType type) noexcept {
+    this->type = type;
+    this->count = count;
+    const auto nbBytes = getWeightsSize(*this, type);
+    auto destBuf = new char[nbBytes];
+    this->values = destBuf;
+    std::copy_n(*srcBuf, nbBytes, destBuf);
+    *srcBuf += nbBytes;
+  }
+};
+
+template <typename T>
+inline void copyToDevice(WeightsWithOwnership* hostWeights,
+                         size_t nbBytes,
+                         cuda_unique_ptr<T>* cudaWeights) {
+  if (hostWeights->values) {
+    void* cudaMem{nullptr};
+    cudaMalloc(&cudaMem, nbBytes);
+    cudaMemcpy(cudaMem, hostWeights->values, nbBytes, cudaMemcpyHostToDevice);
+    cudaWeights->reset(static_cast<T*>(cudaMem));
+  }
+}
+
+inline nvinfer1::DataType fieldTypeToDataType(
+    const nvinfer1::PluginFieldType ftype) {
+  switch (ftype) {
+    case nvinfer1::PluginFieldType::kFLOAT32: {
+      TRANSFORMER_DEBUG_MSG("PluginFieldType is Float32");
+      return nvinfer1::DataType::kFLOAT;
+    }
+    case nvinfer1::PluginFieldType::kFLOAT16: {
+      TRANSFORMER_DEBUG_MSG("PluginFieldType is Float16");
+      return nvinfer1::DataType::kHALF;
+    }
+    case nvinfer1::PluginFieldType::kINT32: {
+      TRANSFORMER_DEBUG_MSG("PluginFieldType is Int32");
+      return nvinfer1::DataType::kINT32;
+    }
+    case nvinfer1::PluginFieldType::kINT8: {
+      TRANSFORMER_DEBUG_MSG("PluginFieldType is Int8");
+      return nvinfer1::DataType::kINT8;
+    }
+    default:
+      TRANSFORMER_DEBUG_MSG("PluginFieldType is Float32");
+      return nvinfer1::DataType::kFLOAT;
+  }
+}
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle