Merge branch 'general-conv' into 'master'

Support general conv for opencl.

See merge request !139

mace/core/runtime/opencl/opencl_runtime.cc

@@ -139,6 +139,7 @@ const std::map<std::string, std::string>
     OpenCLRuntime::program_map_ = {
   {"addn", "addn.cl"},
   {"batch_norm", "batch_norm.cl"},
+  {"conv_2d", "conv_2d.cl"},
   {"conv_2d_1x1", "conv_2d_1x1.cl"},
   {"conv_2d_3x3", "conv_2d_3x3.cl"},
   {"depthwise_conv_3x3", "depthwise_conv_3x3.cl"},

mace/kernels/opencl/cl/conv_2d.cl

+#include <common.h>
+
+__kernel void conv_2d(__read_only image2d_t input, /* [c%4 * w * c/4, h * b] */
+                      __read_only image2d_t filter, /* cout%4 * cin * kw * kh, cout/4 */
+#ifdef BIAS
+    __read_only image2d_t bias, /* cout%4 * cout/4 */
+#endif
+                      __write_only image2d_t output,
+                      __private const int in_height,
+                      __private const int in_width,
+                      __private const int in_ch_blks,
+                      __private const int out_height,
+                      __private const int out_width,
+                      __private const int filter_height,
+                      __private const int filter_width,
+                      __private const int padding_top,
+                      __private const int padding_left) {
+  const int out_ch_blk = get_global_id(0);
+  const int out_w_blk = get_global_id(1);
+  const int out_w_blks = get_global_size(1);
+  const int out_hb = get_global_id(2);
+  const int rounded_in_ch = in_ch_blks * 4;
+
+  const sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+#ifdef BIAS
+  DATA_TYPE4 out0 =
+     READ_IMAGET(bias, sampler, (int2)(out_ch_blk, 0));
+  DATA_TYPE4 out1 = out0;
+  DATA_TYPE4 out2 = out0;
+  DATA_TYPE4 out3 = out0;
+#else
+  DATA_TYPE4 out0 = 0;
+  DATA_TYPE4 out1 = 0;
+  DATA_TYPE4 out2 = 0;
+  DATA_TYPE4 out3 = 0;
+#endif
+
+#if STRIDE == 1
+  int in_width0 = out_w_blk - padding_left;
+  int in_width1 = in_width0 + out_w_blks;
+  int in_width2 = in_width1 + out_w_blks;
+  int in_width3 = in_width2 + out_w_blks;
+  const int height_idx = (out_hb % out_height) - padding_top;
+#else
+  int in_width0 = out_w_blk * 2 - padding_left;
+  int in_width1 = (out_w_blk + out_w_blks) * 2 - padding_left;
+  int in_width2 = (out_w_blk + 2 * out_w_blks) * 2 - padding_left;
+  int in_width3 = (out_w_blk + 3 * out_w_blks) * 2 - padding_left;
+  const int height_idx = (out_hb % out_height) * 2 - padding_top;
+#endif
+
+  const int batch_idx = (out_hb / out_height) * in_height;
+
+  DATA_TYPE4 in0, in1, in2, in3;
+  DATA_TYPE4 weights0, weights1, weights2, weights3;
+  int in_idx, in_width_idx;
+  // Unrolling this loop hurt perfmance
+  for (short in_ch_blk = 0; in_ch_blk < in_ch_blks; ++in_ch_blk) {
+    for (short hb_idx = 0; hb_idx < filter_height; ++hb_idx) {
+      for (short width_idx = 0; width_idx < filter_width; ++width_idx) {
+
+        in_idx = in_ch_blk * in_width;
+
+        int in_hb_value = height_idx + hb_idx;
+        in_hb_value = select(in_hb_value + batch_idx,
+                             -1,
+                             (in_hb_value < 0 || in_hb_value >= in_height));
+
+        int in_width_value;
+#define READ_INPUT(i)                                                                \
+        in_width_value = in_width##i + width_idx;                                    \
+        in_width_value = select(in_idx + in_width_value,                             \
+                                -1,                                                  \
+                                (in_width_value < 0 || in_width_value >= in_width)); \
+        in##i = READ_IMAGET(input, sampler, (int2)(in_width_value, in_hb_value));
+
+        READ_INPUT(0);
+        READ_INPUT(1);
+        READ_INPUT(2);
+        READ_INPUT(3);
+
+#undef READ_INPUT
+
+        int filter_idx = (in_ch_blk << 2) + (hb_idx * filter_width + width_idx) * rounded_in_ch;
+        weights0 = READ_IMAGET(filter, sampler, (int2)(filter_idx + 0, out_ch_blk));
+        weights1 = READ_IMAGET(filter, sampler, (int2)(filter_idx + 1, out_ch_blk));
+        weights2 = READ_IMAGET(filter, sampler, (int2)(filter_idx + 2, out_ch_blk));
+        weights3 = READ_IMAGET(filter, sampler, (int2)(filter_idx + 3, out_ch_blk));
+
+        // Will prefetch L2 improve performance? How to pretch image data?
+
+        // Interleaving load and mul does not improve performance as expected
+        out0 += in0.x * weights0;
+        out0 += in0.y * weights1;
+        out0 += in0.z * weights2;
+        out0 += in0.w * weights3;
+
+        out1 += in1.x * weights0;
+        out1 += in1.y * weights1;
+        out1 += in1.z * weights2;
+        out1 += in1.w * weights3;
+
+        out2 += in2.x * weights0;
+        out2 += in2.y * weights1;
+        out2 += in2.z * weights2;
+        out2 += in2.w * weights3;
+
+        out3 += in3.x * weights0;
+        out3 += in3.y * weights1;
+        out3 += in3.z * weights2;
+        out3 += in3.w * weights3;
+
+      }
+    }
+  }
+
+#ifdef FUSED_RELU
+  // TODO relux
+  out0 = fmax(out0, 0);
+  out1 = fmax(out1, 0);
+  out2 = fmax(out2, 0);
+  out3 = fmax(out3, 0);
+#endif
+
+  const int out_x_base = out_ch_blk * out_width;
+  int w = out_w_blk;
+  WRITE_IMAGET(output,
+               (int2)(out_x_base + w, out_hb),
+               out0);
+
+  w += out_w_blks;
+  if (w >= out_width) return;
+  WRITE_IMAGET(output,
+               (int2)(out_x_base + w, out_hb),
+               out1);
+
+  w += out_w_blks;
+  if (w >= out_width) return;
+  WRITE_IMAGET(output,
+               (int2)(out_x_base + w, out_hb),
+               out2);
+
+  w += out_w_blks;
+  if (w >= out_width) return;
+  WRITE_IMAGET(output,
+               (int2)(out_x_base + w, out_hb),
+               out3);
+
+}

mace/kernels/opencl/conv_2d_opencl.cc

@@ -28,6 +28,11 @@ extern void Conv2dOpenclK3x3S2(const Tensor *input, const Tensor *filter,
                                const int *padding, const DataType dt,
                                Tensor *output);
 
+extern void Conv2dOpencl(const Tensor *input, const Tensor *filter,
+                         const Tensor *bias, const bool fused_relu,
+                         const uint32_t stride, const int *padding,
+                         const DataType dt, Tensor *output);
+
 template<typename T>
 void Conv2dFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
                                                       const Tensor *filter,
@@ -47,17 +52,13 @@ void Conv2dFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
 
   index_t kernel_h = filter->dim(0);
   index_t kernel_w = filter->dim(1);
-  if (kernel_h != kernel_w || kernel_h > 5 || strides_[0] != strides_[1] ||
-      strides_[0] > 2 || dilations_[0] != 1 || dilations_[1] != 1 ||
-      selector[kernel_h - 1][strides_[0] - 1] == nullptr) {
+  if (!input->is_image() || strides_[0] != strides_[1] ||
+      strides_[0] > 2 || dilations_[0] != 1 || dilations_[1] != 1) {
     LOG(WARNING) << "OpenCL conv2d kernel with "
                  << "filter" << kernel_h << "x" << kernel_w << ","
                  << " stride " << strides_[0] << "x" << strides_[1]
                  << " is not implemented yet, using slow version";
-    // TODO(heliangliang) The CPU/NEON kernel should map the buffer
-    Conv2dFunctor<DeviceType::CPU, T>(strides_, paddings_, dilations_)(
-        input, filter, bias, output);
-    return;
+    MACE_NOT_IMPLEMENTED;
   }
 
   std::vector<index_t> output_shape(4);
@@ -66,16 +67,18 @@ void Conv2dFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
       input->shape().data(), filter->shape().data(), dilations_,
       strides_, paddings_, output_shape.data(), paddings.data());
 
-  if (input->is_image()) {
   std::vector<size_t> output_image_shape;
   CalImage2DShape(output_shape, BufferType::IN_OUT, output_image_shape);
   output->ResizeImage(output_shape, output_image_shape);
-  } else {
-    output->Resize(output_shape);
-  }
 
+  if (kernel_h == kernel_w && kernel_h <= 5 &&
+      selector[kernel_h - 1][strides_[0] - 1] != nullptr) {
     auto conv2d_func = selector[kernel_h - 1][strides_[0] - 1];
     conv2d_func(input, filter, bias, false, paddings.data(), DataTypeToEnum<T>::value, output);
+  } else {
+    Conv2dOpencl(input, filter, bias, false, strides_[0], paddings.data(), DataTypeToEnum<T>::value, output);
+  }
+
 }
 
 template

mace/kernels/opencl/conv_2d_opencl_general.cc

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#include "mace/core/common.h"
+#include "mace/core/runtime/opencl/opencl_runtime.h"
+#include "mace/kernels/conv_2d.h"
+#include "mace/kernels/opencl/helper.h"
+#include "mace/utils/utils.h"
+
+namespace mace {
+namespace kernels {
+
+void Conv2dOpencl(const Tensor *input, const Tensor *filter,
+                  const Tensor *bias, const bool fused_relu,
+                  const uint32_t stride, const int *padding,
+                  const DataType dt, Tensor *output) {
+  const index_t batch = output->dim(0);
+  const index_t height = output->dim(1);
+  const index_t width = output->dim(2);
+  const index_t channels = output->dim(3);
+  const index_t input_channels = input->dim(3);
+
+  const index_t channel_blocks = RoundUpDiv4(channels);
+  const index_t input_channel_blocks = RoundUpDiv4(input_channels);
+  const index_t width_blocks = RoundUpDiv4(width);
+
+  std::set<std::string> built_options;
+  built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(dt));
+  built_options.emplace("-DCMD_DATA_TYPE=" + DtToUpstreamCLCMDDt(dt));
+  built_options.emplace(bias != nullptr ? "-DBIAS" : "");
+  built_options.emplace("-DSTRIDE=" + ToString(stride));
+  if (fused_relu) {
+    built_options.emplace("-DFUSED_RELU");
+  }
+
+  auto runtime = OpenCLRuntime::Get();
+  auto program = runtime->program();
+
+  auto conv_2d_kernel = runtime->BuildKernel("conv_2d", "conv_2d", built_options);
+  const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(conv_2d_kernel);
+
+  uint32_t idx = 0;
+  conv_2d_kernel.setArg(idx++, *(static_cast<const cl::Image2D *>(input->buffer())));
+  conv_2d_kernel.setArg(idx++, *(static_cast<const cl::Image2D *>(filter->buffer())));
+  if (bias != nullptr) {
+    conv_2d_kernel.setArg(idx++, *(static_cast<const cl::Image2D *>(bias->buffer())));
+  }
+  conv_2d_kernel.setArg(idx++, *(static_cast<const cl::Image2D *>(output->buffer())));
+  conv_2d_kernel.setArg(idx++, static_cast<int>(input->dim(1)));
+  conv_2d_kernel.setArg(idx++, static_cast<int>(input->dim(2)));
+  conv_2d_kernel.setArg(idx++, static_cast<int>(input_channel_blocks));
+  conv_2d_kernel.setArg(idx++, static_cast<int>(height));
+  conv_2d_kernel.setArg(idx++, static_cast<int>(width));
+  conv_2d_kernel.setArg(idx++, static_cast<int>(filter->dim(0)));
+  conv_2d_kernel.setArg(idx++, static_cast<int>(filter->dim(1)));
+  conv_2d_kernel.setArg(idx++, padding[0] / 2);
+  conv_2d_kernel.setArg(idx++, padding[1] / 2);
+
+  auto command_queue = runtime->command_queue();
+  cl_int error;
+  error = command_queue.enqueueNDRangeKernel(
+      conv_2d_kernel, cl::NullRange,
+      cl::NDRange(static_cast<uint32_t>(channel_blocks), static_cast<uint32_t>(width_blocks),
+                  static_cast<uint32_t>(height * batch)),
+      cl::NDRange(16, 16, 4),
+      NULL, OpenCLRuntime::Get()->GetDefaultEvent());
+  MACE_CHECK(error == CL_SUCCESS, error);
+
+}
+
+}  // namespace kernels
+}  // namespace mace

mace/ops/buffer_to_image_test.cc

@@ -118,14 +118,13 @@ void TestDiffTypeBidirectionTransform(const int type, const std::vector<index_t>
       .Input("B2IOutput")
       .Output("I2BOutput")
       .AddIntArg("buffer_type", type)
-      .AddIntArg("T", DataTypeToEnum<T>::value)
       .Finalize(net.NewOperatorDef());
 
   // Run
   net.RunOp(D);
 
   // Check
-  ExpectTensorNear<float, T>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"), 1e-2);
+  ExpectTensorNear<float>(*net.GetOutput("Input"), *net.GetOutput("I2BOutput"), 1e-3);
 }
 
 TEST(BufferToImageTest, ArgFloatToHalfSmall) {

mace/ops/conv_2d_test.cc

@@ -558,18 +558,20 @@ TEST_F(Conv2dOpTest, OPENCLUnalignedConvNxNS12) {
 }
 
 template<DeviceType D>
-static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
+static void TestHalfComplexConvNxNS12(const std::vector<index_t> &input_shape,
+                                      const std::vector<index_t> &filter_shape) {
   testing::internal::LogToStderr();
-  auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
-                  Padding type) {
   srand(time(NULL));
 
+  auto func = [&](int stride_h, int stride_w, Padding padding) {
     // generate random input
     index_t batch = 3 + (rand() % 10);
-    index_t height = shape[0];
-    index_t width = shape[1];
-    index_t input_channels = shape[2] + (rand() % 10);
-    index_t output_channels = shape[3] + (rand() % 10);
+    index_t height = input_shape[0];
+    index_t width = input_shape[1];
+    index_t kernel_h = filter_shape[0];
+    index_t kernel_w = filter_shape[1];
+    index_t input_channels = filter_shape[2] + (rand() % 10);
+    index_t output_channels = filter_shape[3] + (rand() % 10);
     // Construct graph
     OpsTestNet net;
     OpDefBuilder("Conv2D", "Conv2dTest")
@@ -578,7 +580,7 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
         .Input("Bias")
         .Output("Output")
         .AddIntsArg("strides", {stride_h, stride_w})
-        .AddIntArg("padding", type)
+        .AddIntArg("padding", padding)
         .AddIntsArg("dilations", {1, 1})
         .Finalize(net.NewOperatorDef());
 
@@ -611,7 +613,7 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
         .Input("BiasImage")
         .Output("OutputImage")
         .AddIntsArg("strides", {stride_h, stride_w})
-        .AddIntArg("padding", type)
+        .AddIntArg("padding", padding)
         .AddIntsArg("dilations", {1, 1})
         .AddIntArg("T", static_cast<int>(DataType::DT_HALF))
         .Finalize(net.NewOperatorDef());
@@ -620,20 +622,46 @@ static void TestHalfComplexConvNxNS12(const std::vector<index_t> &shape) {
 
     ImageToBuffer<D, float>(net, "OutputImage", "OPENCLOutput", kernels::BufferType::IN_OUT);
 
-    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 0.2);
+    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 0.5);
   };
 
-  for (int kernel_size : {1, 3}) {
   for (int stride : {1, 2}) {
-      func(kernel_size, kernel_size, stride, stride, VALID);
-    }
+    func(stride, stride, VALID);
+    func(stride, stride, SAME);
   }
 }
 
-TEST_F(Conv2dOpTest, OPENCLHalfAlignedConvNxNS12) {
-  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32, 32, 64});
+TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv1x1S12) {
+  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32},
+                                                {1, 1, 32, 64});
+}
+
+TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv3x3S12) {
+  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32},
+                                                {3, 3, 32, 64});
+}
+
+TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv15x1S12) {
+  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32},
+                                                {15, 1, 256, 2});
+}
+
+TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv1x15S12) {
+  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32},
+                                                {1, 15, 256, 2});
+}
+
+TEST_F(Conv2dOpTest, OPENCLHalfAlignedConv7x75S12) {
+  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({32, 32},
+                                                {7, 7, 3, 64});
+}
+
+TEST_F(Conv2dOpTest, OPENCLHalfUnalignedConv1x1S12) {
+  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({107, 113},
+                                                {1, 1, 5, 7});
 }
 
-TEST_F(Conv2dOpTest, OPENCLHalfUnalignedConvNxNS12) {
-  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({107, 113, 5, 7});
+TEST_F(Conv2dOpTest, OPENCLHalfUnalignedConv3x3S12) {
+  TestHalfComplexConvNxNS12<DeviceType::OPENCL>({107, 113},
+                                                {3, 3, 5, 7});
 }