83 lines
2.9 KiB
C++
83 lines
2.9 KiB
C++
#include <iostream>
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#include "triton/driver/buffer.h"
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#include "triton/driver/backend.h"
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#include "triton/driver/stream.h"
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#include "triton/runtime/jit.h"
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#include "triton/tools/bench.hpp"
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#include "triton/dnn/conv.h"
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#define EIGEN_USE_GPU
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#include "tensorflow/core/framework/op.h"
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#include "tensorflow/core/framework/shape_inference.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/util/cuda_kernel_helper.h"
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#include "tensorflow/core/util/padding.h"
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#include "tensorflow/core/util/tensor_format.h"
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#include "tensorflow/core/framework/common_shape_fns.h"
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using namespace tensorflow;
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using GPUDevice = Eigen::GpuDevice;
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class Conv2dOp : public OpKernel {
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public:
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explicit Conv2dOp(OpKernelConstruction* context) : OpKernel(context) {
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}
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void Compute(OpKernelContext* context){
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// get device/stream
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GPUDevice device = context->eigen_device<GPUDevice>();
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triton::driver::cu_stream sstream(device.stream(), false);
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triton::driver::context* ctx = sstream.context();
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triton::driver::stream* stream = &sstream;
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// get inputs
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const Tensor& tfa = context->input(0);
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const Tensor& tfb = context->input(1);
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// get shapes
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int32_t B = tfa.dim_size(0);
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int32_t Ca = tfa.dim_size(1);
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int32_t D = 1;
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int32_t H = tfa.dim_size(2);
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int32_t W = tfa.dim_size(3);
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int32_t Cb = tfb.dim_size(0);
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int32_t T = 1;
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int32_t R = tfb.dim_size(1);
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int32_t S = tfb.dim_size(2);
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int32_t NF = tfb.dim_size(3);
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assert(Ca == Cb);
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int32_t C = Ca;
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int32_t stride_d = 1, stride_h = 1, stride_w = 1;
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int32_t pad_d = 0, pad_h = 0, pad_w = 0;
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bool has_bias = false;
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// wrap buffers
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triton::driver::cu_buffer a(ctx, tfa.tensor_data().size(), (CUdeviceptr)tfa.tensor_data().data(), false);
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triton::driver::cu_buffer b(ctx, tfb.tensor_data().size(), (CUdeviceptr)tfb.tensor_data().data(), false);
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triton::driver::buffer* bias = nullptr;
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// template
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triton::dnn::conv conv(B, C,
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D, H, W,
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T, R, S,
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NF,
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stride_d, stride_h, stride_w,
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pad_d, pad_h, pad_w,
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1, 1, 1,
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"half", "half",
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triton::dnn::conv::FPROP, has_bias);
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// allocate output
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auto c_shapes = conv.c_shapes();
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Tensor* tfc = nullptr;
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TensorShape out_shape({c_shapes[0], c_shapes[1], c_shapes[2], c_shapes[3]});
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OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &tfc));
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triton::driver::cu_buffer c(ctx, tfc->tensor_data().size(), (CUdeviceptr)tfc->tensor_data().data(), false);
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// enqueue
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conv.enqueue(stream, {&a, &b, &c, bias});
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}
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};
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REGISTER_KERNEL_BUILDER(Name("Conv2d").Device(DEVICE_GPU), Conv2dOp);
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REGISTER_OP("Conv2d")
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.Input("a: float16")
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.Input("b: float16")
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.Output("c: float32")
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;
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