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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