chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
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+/*
+ * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * 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.
+ */
+#include "common/kernels/kernel.h"
+namespace nvinfer1
+{
+namespace plugin
+{
+template <unsigned nthdsPerCTA>
+__launch_bounds__(nthdsPerCTA) __global__ void softmaxKernel(const float* input, const int n, const int batch,
+    const int batchOffset, const int groups, const int groupOffset, const int stride, const float temp, float* output)
+{
+    int id = blockIdx.x * nthdsPerCTA + threadIdx.x;
+    if (id < batch * groups)
+    {
+        int b = id / groups;
+        int g = id % groups;
+        float sum = 0.;
+        // Initialze largest to be the smallest float number.
+        float largest = -3.402823466e+38;
+        int offset = b * batchOffset + g * groupOffset;
+        // Find the largest digits before softmax
+        for (int i = 0; i < n; ++i)
+        {
+            float val = input[i * stride + offset];
+            largest = (val > largest) ? val : largest;
+        }
+        // Softmax for a group of candidate classes
+        // Calculate exponentials
+        for (int i = 0; i < n; ++i)
+        {
+            /*
+             * Here we used a trick to prevent numeric overflow
+             * xm = max{x_1, x_2, ..., x_n}
+             * e^{x_1} / (e^{x_1} + e^{x_2} + e^{x_n}) = e^{x_1 - xm} / (e^{x_1 - xm} + e^{x_2 - xm} + e^{x_n - xm})
+             */
+            float e = exp(input[i * stride + offset] / temp - largest / temp);
+            sum += e;
+            output[i * stride + offset] = e;
+        }
+        // Normalize
+        for (int i = 0; i < n; ++i)
+            output[i * stride + offset] /= sum;
+    }
+}
+
+template <unsigned nthdsPerCTA>
+__launch_bounds__(nthdsPerCTA)
+    __global__ void activateKernel(float* data,
+                                   const int range)
+{
+    int i = blockIdx.x * nthdsPerCTA + threadIdx.x;
+    // Sigmoid function
+    if (i < range)
+        data[i] = 1. / (1. + exp(-data[i]));
+}
+
+pluginStatus_t regionGPU(
+    cudaStream_t stream,
+    const int batch,
+    const int C,
+    const int H,
+    const int W,
+    const int num,
+    const int coords,
+    const int classes,
+    const bool hasSoftmaxTree,
+    const nvinfer1::plugin::softmaxTree* smTree,
+    const float* input,
+    float* output)
+{
+    const int BS = 512;
+    const int GS1 = (2 * H * W + BS - 1) / BS;
+    const int GS2 = (H * W + BS - 1) / BS;
+    // Applying sigmoid activations
+    for (int b = 0; b < batch; ++b)
+    {
+        for (int n = 0; n < num; ++n)
+        {
+            // Apply sigmoid activation for the encoded center coordinates t_x, and t_y
+            int index = b * C * H * W + n * H * W * (coords + classes + 1);
+            activateKernel<BS><<<GS1, BS, 0, stream>>>(output + index, 2 * H * W);
+            /*
+             * Apply sigmoid for the encoded objectness t_o
+             * + 4 * H * W because we want to skip the first four coordinates t_x, t_y, t_w, t_h
+             * Chaning 4 * H * W to + coords * H * W will not make this function more general
+             * Since we already assumed the information layout in the channels of the input tensor
+             */
+            index = b * C * H * W + n * H * W * (coords + classes + 1) + 4 * H * W;
+            activateKernel<BS><<<GS2, BS, 0, stream>>>(output + index, H * W);
+        }
+    }
+    const int GS3 = (batch * num * H * W + BS - 1) / BS;
+    // Applying softmax activations
+    if (hasSoftmaxTree)
+    {
+        // Softmax for hierarchical classification
+        // The first 5 elements are t_x, t_y, t_w, t_h, t_o which we don't need to apply softmax activation
+        int count = 5;
+        // Only groups and groupSize information is useful for this plugin
+        // Applying softmax activation sequentially for each group of candidate classes
+        for (int i = 0; i < smTree->groups; ++i)
+        {
+            int groupSize = smTree->groupSize[i];
+            softmaxKernel<BS><<<GS3, BS, 0, stream>>>(input + count * H * W, groupSize, batch * num, (C * H * W / num), H * W, 1, H * W, 1., output + count * H * W);
+            count += groupSize;
+        }
+    }
+    else
+    {
+        // Softmax for non-hierarchical classificiation
+        softmaxKernel<BS><<<GS3, BS, 0, stream>>>(input + 5 * H * W, classes, batch * num, (C * H * W / num), H * W, 1, H * W, 1., output + 5 * H * W);
+    }
+
+    return STATUS_SUCCESS;
+}
+
+pluginStatus_t regionInference(cudaStream_t stream, const int batch, const int C, const int H, const int W,
+    const int num, const int coords, const int classes, const bool hasSoftmaxTree,
+    const nvinfer1::plugin::softmaxTree* smTree, const void* input, void* output)
+{
+    PLUGIN_CHECK(cudaMemcpyAsync(output, input, batch * C * H * W * sizeof(float), cudaMemcpyDeviceToDevice, stream));
+    return regionGPU(
+        stream, batch, C, H, W, num, coords, classes, hasSoftmaxTree, smTree, (const float*) input, (float*) output);
+}
+} // namespace plugin
+} // namespace nvinfer1