[Backend] Add TensorRT FP16 support for AdaptivePool2d

fastdeploy/runtime/backends/common/cuda/adaptive_pool2d_kernel.cu

@@ -17,8 +17,8 @@
 #include "adaptive_pool2d_kernel.h"
 
 namespace fastdeploy {
-
-__global__ void CudaCastKernel(const float* in, float* out, int edge,
+template <typename T1, typename T2>
+__global__ void CudaCastKernel(const T1* in, T2* out, int edge,
                                int out_bc_offset, int in_bc_offset, int ih,
                                int iw, int oh, int ow, bool is_avg) {
   int position = blockDim.x * blockIdx.x + threadIdx.x;
@@ -32,29 +32,37 @@ __global__ void CudaCastKernel(const float* in, float* out, int edge,
   int hend = ceilf(static_cast<float>((h + 1) * ih) / oh);
   int wstart = floorf(static_cast<float>(w * iw) / ow);
   int wend = ceilf(static_cast<float>((w + 1) * iw) / ow);
+  float ele_val = 0.0;
   if (is_avg) {
-    out[position] = 0.0;
+    ele_val = 0.0;
   } else {
-    out[position] = in[offset * in_bc_offset + hstart * iw + wstart];
+    ele_val =
+        static_cast<float>(in[offset * in_bc_offset + hstart * iw + wstart]);
   }
   for (int h = hstart; h < hend; ++h) {
     for (int w = wstart; w < wend; ++w) {
       int input_idx = h * iw + w;
       if (is_avg) {
-        out[position] = out[position] + in[offset * in_bc_offset + input_idx];
+        ele_val =
+            ele_val + static_cast<float>(in[offset * in_bc_offset + input_idx]);
       } else {
-        out[position] =
-            max(out[position], in[offset * in_bc_offset + input_idx]);
+        ele_val =
+            (ele_val >
+             static_cast<float>(in[offset * in_bc_offset + input_idx]))
+                ? ele_val
+                : static_cast<float>(in[offset * in_bc_offset + input_idx]);
       }
     }
   }
-  out[position] = out[position] / ((hend - hstart) * (wend - wstart));
+  out[position] = static_cast<T2>(
+      ele_val / static_cast<float>(((hend - hstart) * (wend - wstart))));
 }
 
 void CudaAdaptivePool(const std::vector<int64_t>& input_dims,
-                      const std::vector<int64_t>& output_dims, float* output,
-                      const float* input, void* compute_stream,
-                      const std::string& pooling_type) {
+                      const std::vector<int64_t>& output_dims, void* output,
+                      const void* input, void* compute_stream,
+                      const std::string& pooling_type, const std::string& dtype,
+                      const std::string& out_dtype) {
   auto casted_compute_stream = reinterpret_cast<cudaStream_t>(compute_stream);
   int out_bc_offset = output_dims[2] * output_dims[3];
   int in_bc_offset = input_dims[2] * input_dims[3];
@@ -65,9 +73,27 @@ void CudaAdaptivePool(const std::vector<int64_t>& input_dims,
   bool is_avg = pooling_type == "avg";
   int threads = 256;
   int blocks = ceil(jobs / static_cast<float>(threads));
-  CudaCastKernel<<<blocks, threads, 0, casted_compute_stream>>>(
-      input, output, jobs, out_bc_offset, in_bc_offset, int(input_dims[2]),
-      int(input_dims[3]), int(output_dims[2]), int(output_dims[3]), is_avg);
+  if (dtype == "float") {
+    CudaCastKernel<float, float><<<blocks, threads, 0, casted_compute_stream>>>(
+        static_cast<const float*>(input), static_cast<float*>(output), jobs,
+        out_bc_offset, in_bc_offset, int(input_dims[2]), int(input_dims[3]),
+        int(output_dims[2]), int(output_dims[3]), is_avg);
+  } else if (dtype == "half") {
+    if (out_dtype == "half") {
+      CudaCastKernel<half, half><<<blocks, threads, 0, casted_compute_stream>>>(
+          static_cast<const half*>(input), static_cast<half*>(output), jobs,
+          out_bc_offset, in_bc_offset, int(input_dims[2]), int(input_dims[3]),
+          int(output_dims[2]), int(output_dims[3]), is_avg);
+    }
+    if (out_dtype == "float") {
+      CudaCastKernel<half, float>
+          <<<blocks, threads, 0, casted_compute_stream>>>(
+              static_cast<const half*>(input), static_cast<float*>(output),
+              jobs, out_bc_offset, in_bc_offset, int(input_dims[2]),
+              int(input_dims[3]), int(output_dims[2]), int(output_dims[3]),
+              is_avg);
+    }
+  }
 }
 }  // namespace fastdeploy
 #endif

fastdeploy/runtime/backends/common/cuda/adaptive_pool2d_kernel.h

@@ -15,6 +15,7 @@
 
 #pragma once
 
+#include <cuda_fp16.h>
 #include <cstdint>
 #include <cuda.h>
 #include <cuda_runtime.h>
@@ -25,8 +26,10 @@
 namespace fastdeploy {
 
 void CudaAdaptivePool(const std::vector<int64_t>& input_dims,
-                      const std::vector<int64_t>& output_dims, float* output,
-                      const float* input, void* compute_stream,
-                      const std::string& pooling_type);
+                      const std::vector<int64_t>& output_dims, void* output,
+                      const void* input, void* compute_stream,
+                      const std::string& pooling_type,
+                      const std::string& dtype = "float",
+                      const std::string& out_dtype = "float");
 
 }  // namespace fastdeploy

fastdeploy/runtime/backends/tensorrt/ops/adaptive_pool2d.cc

@@ -63,20 +63,25 @@ int AdaptivePool2d::enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
                             const nvinfer1::PluginTensorDesc* outputDesc,
                             const void* const* inputs, void* const* outputs,
                             void* workspace, cudaStream_t stream) noexcept {
-  if (inputDesc[0].type != nvinfer1::DataType::kFLOAT) {
-    return -1;
-  }
-  auto const* data = static_cast<float const*>(inputs[0]);
-  auto* result = static_cast<float*>(outputs[0]);
   int nums = outputDesc[0].dims.d[0] * outputDesc[0].dims.d[1] *
              outputDesc[0].dims.d[2] * outputDesc[0].dims.d[3];
   std::vector<int64_t> input_size, output_size;
   for (int i = 0; i < 4; i++) {
     input_size.push_back(inputDesc[0].dims.d[i]);
     output_size.push_back(outputDesc[0].dims.d[i]);
   }
-  CudaAdaptivePool(input_size, output_size, result, data, stream,
-                   pooling_type_);
+  if (inputDesc[0].type == nvinfer1::DataType::kHALF) {
+    if (outputDesc[0].type == nvinfer1::DataType::kHALF) {
+      CudaAdaptivePool(input_size, output_size, outputs[0], inputs[0], stream,
+                       pooling_type_, "half", "half");
+    } else if (outputDesc[0].type == nvinfer1::DataType::kFLOAT) {
+      CudaAdaptivePool(input_size, output_size, outputs[0], inputs[0], stream,
+                       pooling_type_, "half", "float");
+    }
+  } else if (inputDesc[0].type == nvinfer1::DataType::kFLOAT) {
+    CudaAdaptivePool(input_size, output_size, outputs[0], inputs[0], stream,
+                     pooling_type_, "float", "float");
+  }
   return cudaPeekAtLastError();
 }
 
@@ -106,7 +111,12 @@ nvinfer1::DataType AdaptivePool2d::getOutputDataType(
 bool AdaptivePool2d::supportsFormatCombination(
     int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs,
     int nbOutputs) noexcept {
-  return (inOut[pos].format == nvinfer1::PluginFormat::kLINEAR);
+  if ((inOut[pos].format == nvinfer1::PluginFormat::kLINEAR) &&
+      (inOut[pos].type == nvinfer1::DataType::kFLOAT ||
+       inOut[pos].type == nvinfer1::DataType::kHALF)) {
+    return true;
+  }
+  return false;
 }
 
 int AdaptivePool2d::initialize() noexcept { return 0; }