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| #include <cuda_runtime.h>
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| #include <cuda_fp16.h>
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| #include "grid_stride_range.hpp"
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| #include "execution.hpp"
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| #include "vector_traits.hpp"
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| #include "../cuda4dnn/csl/stream.hpp"
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| #include "../cuda4dnn/csl/span.hpp"
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| using namespace cv::dnn::cuda4dnn::csl;
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| using namespace cv::dnn::cuda4dnn::csl::device;
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| namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
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| namespace raw {
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| template <std::size_t N>
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| __global__ void fp32_to_fp16(Span<__half> output, View<float> input) {
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| using output_vector_type = get_vector_type_t<__half, N>;
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| using input_vector_type = get_vector_type_t<float, N>;
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| auto output_vPtr = output_vector_type::get_pointer(output.data());
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| auto input_vPtr = input_vector_type::get_pointer(input.data());
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| for (auto i : grid_stride_range(output.size() / output_vector_type::size())) {
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| input_vector_type in_vec;
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| v_load(in_vec, input_vPtr[i]);
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| output_vector_type out_vec;
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| for (int j = 0; j < output_vector_type::size(); j++)
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| out_vec.data[j] = __float2half(in_vec.data[j]);
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| v_store(output_vPtr[i], out_vec);
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| }
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| }
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| template <std::size_t N>
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| __global__ void fp16_to_fp32(Span<float> output, View<__half> input) {
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| using output_vector_type = get_vector_type_t<float, N>;
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| using input_vector_type = get_vector_type_t<__half, N>;
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| auto output_vPtr = output_vector_type::get_pointer(output.data());
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| auto input_vPtr = input_vector_type::get_pointer(input.data());
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| for (auto i : grid_stride_range(output.size() / output_vector_type::size())) {
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| input_vector_type in_vec;
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| v_load(in_vec, input_vPtr[i]);
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| output_vector_type out_vec;
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| for (int j = 0; j < output_vector_type::size(); j++)
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| out_vec.data[j] = __half2float(in_vec.data[j]);
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| v_store(output_vPtr[i], out_vec);
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| }
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| }
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| }
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| template <std::size_t N> static
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| void launch_vectorized_fp32_to_fp16(const Stream& stream, Span<__half> output, View<float> input) {
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| CV_Assert(is_fully_aligned<__half>(output, N));
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| CV_Assert(is_fully_aligned<float>(input, N));
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| auto kernel = raw::fp32_to_fp16<N>;
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| auto policy = make_policy(kernel, output.size() / N, 0, stream);
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| launch_kernel(kernel, policy, output, input);
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| }
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| void fp32_to_fp16(const Stream& stream, Span<__half> output, View<float> input) {
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| if (is_fully_aligned<__half>(output, 4) && is_fully_aligned<float>(input, 4)) {
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| launch_vectorized_fp32_to_fp16<4>(stream, output, input);
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| } else if (is_fully_aligned<__half>(output, 2) && is_fully_aligned<float>(input, 2)) {
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| launch_vectorized_fp32_to_fp16<2>(stream, output, input);
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| } else {
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| launch_vectorized_fp32_to_fp16<1>(stream, output, input);
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| }
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| }
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| template <std::size_t N> static
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| void launch_vectorized_fp16_to_fp32(const Stream& stream, Span<float> output, View<__half> input) {
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| CV_Assert(is_fully_aligned<float>(output, N));
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| CV_Assert(is_fully_aligned<__half>(input, N));
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| auto kernel = raw::fp16_to_fp32<N>;
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| auto policy = make_policy(kernel, output.size() / N, 0, stream);
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| launch_kernel(kernel, policy, output, input);
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| }
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| void fp16_to_fp32(const Stream& stream, Span<float> output, View<__half> input) {
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| if (is_fully_aligned<float>(output, 4) && is_fully_aligned<__half>(input, 4)) {
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| launch_vectorized_fp16_to_fp32<4>(stream, output, input);
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| } else if (is_fully_aligned<float>(output, 2) && is_fully_aligned<__half>(input, 2)) {
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| launch_vectorized_fp16_to_fp32<2>(stream, output, input);
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| } else {
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| launch_vectorized_fp16_to_fp32<1>(stream, output, input);
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| }
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| }
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| }}}}
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