[examples/python] added framework code for shift-conv

examples/python/tensorflow/CMakeLists.txt

@@ -5,7 +5,7 @@ if(${TensorFlow_FOUND})
   include_directories("${CUDA_HOME}/include")
   link_directories(${TF_LIB})
   add_definitions(-D_GLIBCXX_USE_CXX11_ABI=${TF_ABI})
-  add_library(tf_blocksparse SHARED dot.cpp conv2d.cpp)
+  add_library(tf_blocksparse SHARED dot.cpp conv2d.cpp shift.cpp)
   target_link_libraries(tf_blocksparse tensorflow_framework triton)
   configure_file(${CMAKE_CURRENT_SOURCE_DIR}/run.py
                   ${CMAKE_CURRENT_BINARY_DIR}/run.py

examples/python/tensorflow/run.py

@@ -28,13 +28,13 @@ def run_dot():
     print("dif: %f" % np.max(dif))
 
 def run_conv():
-    BS, C, H, W = 16, 32, 32, 32
+    B, C, H, W = 16, 32, 32, 32
     R, S, NF = 3, 3, 32
-    a = tf.placeholder(tf.float32, shape=[BS, C, H, W])
+    a = tf.placeholder(tf.float32, shape=[B, C, H, W])
     b = tf.placeholder(tf.float32, shape=[C, R, S, NF])
     c = module.conv2d(a, b)
     # Reference
-    ha = np.random.rand(BS, C, H, W)
+    ha = np.random.rand(B, C, H, W)
     hb = np.random.rand(C, R, S, NF)
     # Run
     sess = tf.InteractiveSession()
@@ -42,4 +42,23 @@ def run_conv():
     result = sess.run([c], feed_dict = {a: ha,
                                         b: hb})[0]
 
-run_conv()
+def run_shift():
+    B, C, H, W = 16, 32, 32, 32
+    R, S, F = 3, 3, 32
+    a = tf.placeholder(tf.float32, shape=[C, H, W, B])
+    b = tf.placeholder(tf.float32, shape=[C, F])
+    shift_h = tf.zeros(C, tf.int32)
+    shift_w = tf.zeros(C, tf.int32)
+    hshift_h = np.zeros(C, np.int32)
+    hshift_w = np.zeros(C, np.int32)
+    c = module.shift_conv(a, b, shift_h=tf.make_tensor_proto(hshift_h), shift_w=tf.make_tensor_proto(hshift_w))
+    # Reference
+    ha = np.random.rand(C, H, W, B)
+    hb = np.random.rand(C, F)
+    # Run
+    sess = tf.InteractiveSession()
+    sess.run(tf.global_variables_initializer())
+    result = sess.run([c], feed_dict = {a: ha,
+                                        b: hb})[0]
+
+run_shift()

examples/python/tensorflow/shift.cpp

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