[GitHub] [incubator-tvm] icemelon9 commented on a change in pull request #5186: [Relay][Topi][AutoTVM] Winograd support for Conv3D

[GitBox <gi...@apache.org>]

icemelon9 commented on a change in pull request #5186: [Relay][Topi][AutoTVM] Winograd support for Conv3D
URL: https://github.com/apache/incubator-tvm/pull/5186#discussion_r403299155
 
 

 ##########
 File path: src/relay/op/nn/convolution.cc
 ##########
 @@ -662,96 +454,101 @@ RELAY_REGISTER_OP("nn.contrib_conv2d_winograd_without_weight_transform")
         ConvInferCorrectLayout<Conv2DWinogradAttrs>);
 
 // relay.nn.contrib_conv2d_winograd_weight_transform
-TVM_REGISTER_NODE_TYPE(Conv2DWinogradWeightTransformAttrs);
-
-bool Conv2DWinogradWeightTransformRel(const Array<Type>& types,
-                                      int num_inputs,
-                                      const Attrs& attrs,
-                                      const TypeReporter& reporter) {
-  CHECK_EQ(types.size(), 2);
-  const auto* data = types[0].as<TensorTypeNode>();
-  if (data == nullptr) return false;
-
-  const Conv2DWinogradWeightTransformAttrs* param = attrs.as<Conv2DWinogradWeightTransformAttrs>();
-  CHECK(param != nullptr);
-
-  CHECK_EQ(data->shape.size(), 4) << "Only support NCHW normal kernel layout";
-
-  // each pad width element should be a pair of positive integers
-  std::vector<IndexExpr> oshape {
-      param->tile_size + data->shape[2] - 1,
-      param->tile_size + data->shape[3] - 1,
-      data->shape[0],
-      data->shape[1],
-  };
-
-  reporter->Assign(types[1], TensorType(Array<IndexExpr>(oshape),
-                                                  data->dtype));
-  return true;
-}
-
-Expr MakeConv2DWinogradWeightTransform(Expr weight,
-                                       int tile_size) {
-  auto attrs = make_object<Conv2DWinogradWeightTransformAttrs>();
-  attrs->tile_size = tile_size;
-  static const Op& op = Op::Get("nn.contrib_conv2d_winograd_weight_transform");
-  return Call(op, {weight}, Attrs(attrs), {});
-}
-
+TVM_REGISTER_NODE_TYPE(ConvWinogradWeightTransformAttrs);
 
 TVM_REGISTER_GLOBAL("relay.op.nn._make.contrib_conv2d_winograd_weight_transform")
-.set_body_typed(MakeConv2DWinogradWeightTransform);
-
+.set_body_typed([](Expr weight,
+                   int tile_size) {
+  return MakeConvWinogradWeightTransform(
+    weight, tile_size, "nn.contrib_conv2d_winograd_weight_transform");
+});
 
 RELAY_REGISTER_OP("nn.contrib_conv2d_winograd_weight_transform")
-.describe(R"code(Weight transformation of winograd fast convolution algorithm.
+    .describe(R"code(Weight transformation of winograd fast convolution algorithm.
 
 Separate this into another operator in order to enable Precompute Pass to compute the
 weight transformation in advance.
 
 - **weight**: (channels, in_channels, kernel_size[0], kernel_size[1])
 )code" TVM_ADD_FILELINE)
-.set_attrs_type<Conv2DWinogradWeightTransformAttrs>()
-.set_num_inputs(1)
-.add_argument("weight", "Tensor", "The weight tensor.")
-.set_support_level(10)
-.add_type_rel("Conv2DWinogradWeightTransform", Conv2DWinogradWeightTransformRel);
+    .set_attrs_type<ConvWinogradWeightTransformAttrs>()
+    .set_num_inputs(1)
+    .add_argument("weight", "Tensor", "The weight tensor.")
+    .set_support_level(10)
+    .add_type_rel("Conv2DWinogradWeightTransform",
+                  Conv2DWinogradWeightTransformRel<ConvWinogradWeightTransformAttrs>);
+
+// relay.nn.contrib_conv3d_winograd_without_weight_transform
+TVM_REGISTER_NODE_TYPE(Conv3DWinogradAttrs);
+
+TVM_REGISTER_GLOBAL("relay.op.nn._make.contrib_conv3d_winograd_without_weight_transform")
+.set_body_typed([](Expr data,
+                   Expr weight,
+                   int tile_size,
+                   Array<IndexExpr> strides,
+                   Array<IndexExpr> padding,
+                   Array<IndexExpr> dilation,
+                   int groups,
+                   IndexExpr channels,
+                   Array<IndexExpr> kernel_size,
+                   std::string data_layout,
+                   std::string kernel_layout,
+                   std::string out_layout,
+                   DataType out_dtype) {
+  return MakeConvWinograd<Conv3DWinogradAttrs>(
+    data, weight, tile_size, strides, padding, dilation,
+    groups, channels, kernel_size, data_layout,
+    kernel_layout, out_layout, out_dtype, "nn.contrib_conv3d_winograd_without_weight_transform");
+});
+
+RELAY_REGISTER_OP("nn.contrib_conv3d_winograd_without_weight_transform")
+    .describe(R"code(Compute conv3d with winograd algorithm. Only supports NCDHW layout.
 
 Review comment:
   ditto

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