[examples/python] added framework code for shift-conv
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111
examples/python/tensorflow/shift.cpp
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111
examples/python/tensorflow/shift.cpp
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#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/shift.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 ShiftConvOp : public OpKernel {
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public:
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explicit ShiftConvOp(OpKernelConstruction* context) : OpKernel(context) {
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context->GetAttr("shift_h", &h_shift_h_);
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context->GetAttr("shift_w", &h_shift_w_);
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R_ = 3;
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S_ = 3;
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}
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void ComputeCommon(OpKernelContext* 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& tf_a = context->input(0);
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const Tensor& tf_b = context->input(1);
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// shapes for a
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int64_t Ca = tf_a.dim_size(0);
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int64_t H = tf_a.dim_size(1);
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int64_t W = tf_a.dim_size(2);
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int64_t B = tf_a.dim_size(3);
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// shapes for b
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int64_t Cb = tf_b.dim_size(0);
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int64_t F = tf_b.dim_size(1);
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// checks
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OP_REQUIRES(context, Ca == Cb, tensorflow::errors::InvalidArgument("operands must have the same number of channels"));
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int64_t C = Ca;
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// shapes for c
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Tensor* tf_c = nullptr;
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TensorShape out_shape({Ca, H, W, B});
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OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &tf_c));
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// return early if possible
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if (out_shape.num_elements() == 0)
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return;
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// initialize default compute device
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triton::jit jit(ctx);
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// matrix multiplication parameters
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triton::driver::cu_buffer da(ctx, (CUdeviceptr)tf_a.flat<float>().data(), false);
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triton::driver::cu_buffer db(ctx, (CUdeviceptr)tf_b.flat<float>().data(), false);
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triton::driver::cu_buffer dc(ctx, (CUdeviceptr)tf_c->flat<float>().data(), false);
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// shift configuration
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int32_t* shift_h_data = h_shift_h_.flat<int32_t>().data();
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int32_t* shift_w_data = h_shift_w_.flat<int32_t>().data();
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std::vector<int32_t> shift_h(shift_h_data, shift_h_data + C);
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std::vector<int32_t> shift_w(shift_w_data, shift_w_data + C);
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triton::dnn::shift shift(B, C, 1, H, W, 1, R_, S_, F, shift_h, shift_w, "fp32", "fp32", triton::dnn::shift::FPROP, false);
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// benchmark a given matrix multiplication kernel
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auto benchmark = [&](triton::driver::kernel* kernel,
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triton::jit::launch_information info) {
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// launch info
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unsigned TM = info.global_range_size[0];
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unsigned TN = info.global_range_size[1];
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unsigned nthreads = info.num_threads;
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shift.init(stream, (triton::driver::cu_module*)kernel->module());
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shift.enqueue(stream, kernel, &da, &db, &dc, TM, TN, nthreads);
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stream->synchronize();
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double ts = triton::tools::bench([&](){ shift.enqueue(stream, kernel, &da, &db, &dc, TM, TN, nthreads); },
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[&](){ stream->synchronize(); }, ctx->device());
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return shift.get_nflops() / ts * 1e-3;
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};
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std::ostringstream oss;
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shift.src(oss);
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std::string src = oss.str();
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triton::jit::tune_res_t best = jit.autotune("shift", src.c_str(), benchmark);
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}
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private:
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Tensor h_shift_h_;
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Tensor h_shift_w_;
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// triton::driver::buffer* d_shift_h_;
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// triton::driver::buffer* d_shift_w_;
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int R_;
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int S_;
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};
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REGISTER_KERNEL_BUILDER(Name("ShiftConv").Device(DEVICE_GPU), ShiftConvOp);
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REGISTER_OP("ShiftConv")
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.Input("a: float32")
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.Input("b: float32")
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.Attr("shift_h: tensor")
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.Attr("shift_w: tensor")
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.Output("c: float32")
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;
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