[Bugfix] Conv1Dtranspose default kernel layout should be IOW (#14482)

* fix conv1Dtranspose kernel layout

* fix conv1Dtranspose type checker

* fix mxnet layout

Author: rebel-jangys

include/tvm/relay/attrs/nn.h

@@ -671,10 +671,10 @@ struct Conv1DTransposeAttrs : public tvm::AttrsNode<Conv1DTransposeAttrs> {
             "dimensions respectively. Convolution is applied on the"
             "'W' dimension.");
     TVM_ATTR_FIELD(kernel_layout)
-        .set_default("OIW")
+        .set_default("IOW")
         .describe(
-            "Dimension ordering of data and weight. Can be 'OIW', 'OIW16o16i', etc."
-            "'O', 'I', 'W' stands for num_filter, input_channel, and width"
+            "Dimension ordering of data and weight. Can be 'IOW', 'IOW16o16i', etc."
+            "'I', 'O', 'W' stands for input_channel, num_filter and width"
             "dimensions respectively.");
     TVM_ATTR_FIELD(out_layout)
         .set_default("")

python/tvm/relay/frontend/keras.py

@@ -282,6 +282,8 @@ def _convert_dense(
 
 
 def _convert_convolution1d(inexpr, keras_layer, etab, data_layout, input_shape=None):
+    is_deconv = type(keras_layer).__name__ == "Conv1DTranspose"
+
     if input_shape is None:
         input_shape = keras_layer.input_shape
     _check_data_format(keras_layer)
@@ -290,19 +292,21 @@ def _convert_convolution1d(inexpr, keras_layer, etab, data_layout, input_shape=N
 
     if data_layout == "NWC":
         kernel_layout = "WIO"
+        if is_deconv:
+            kernel_layout = "WOI"
     else:
         kernel_layout = "OIW"
+        if is_deconv:
+            kernel_layout = "IOW"
         msg = (
             "Kernel layout with {} is not supported for operator Convolution1D "
             "in frontend Keras."
         )
         raise tvm.error.OpAttributeUnImplemented(msg.format(data_layout))
 
-    is_deconv = type(keras_layer).__name__ == "Conv1DTranspose"
-
     if is_deconv:
-        if kernel_layout == "OIW":
-            weight = weight.transpose([2, 0, 1])
+        if kernel_layout == "IOW":
+            weight = weight.transpose([2, 1, 0])
         kernel_w, n_filters, _ = weight.shape
     else:
         kernel_w, _, n_filters = weight.shape

python/tvm/relay/frontend/mxnet.py

@@ -304,7 +304,7 @@ def _mx_conv1d_transpose(inputs, attrs):
     if data_layout != "NCW":
         raise tvm.error.OpAttributeInvalid('Only "NCW" data layout is supported for 1D Convolution')
     channel_axis = 1
-    kernel_layout = "OIW"
+    kernel_layout = "IOW"
     new_attrs = {}
     new_attrs["channels"] = attrs.get_int("num_filter")
     new_attrs["kernel_size"] = attrs.get_int_tuple("kernel")

python/tvm/relay/frontend/pytorch.py

@@ -1263,6 +1263,9 @@ def convolution(self, inputs, input_types):
         else:
             data_layout = "NCW"
             kernel_layout = "OIW"
+            if use_transpose:
+                # Transposed convolutions have IOW layout.
+                kernel_layout = "IOW"
 
         # Conv1d does not currently support grouped convolution so we convert it to conv2d
         is_grouped_conv1d = False

python/tvm/relay/op/nn/nn.py

@@ -604,7 +604,7 @@ def conv1d_transpose(
     channels=None,
     kernel_size=None,
     data_layout="NCW",
-    kernel_layout="OIW",
+    kernel_layout="IOW",
     out_layout="",
     output_padding=(0,),
     out_dtype="",

src/relay/op/nn/convolution.cc

@@ -926,7 +926,7 @@ bool Conv1DTransposeRel(const Array<Type>& types, int num_inputs, const Attrs& a
   if (data == nullptr) return false;
 
   static const Layout kNCW("NCW");
-  static const Layout kOIW("OIW");
+  static const Layout kIOW("IOW");
 
   const Conv1DTransposeAttrs* param = attrs.as<Conv1DTransposeAttrs>();
   ICHECK(param != nullptr);
@@ -938,9 +938,9 @@ bool Conv1DTransposeRel(const Array<Type>& types, int num_inputs, const Attrs& a
       << "Conv only support input layouts that are convertible from NCW."
       << " But got " << in_layout;
 
-  const auto trans_kernel_layout = tir::BijectiveLayout(kernel_layout, kOIW);
+  const auto trans_kernel_layout = tir::BijectiveLayout(kernel_layout, kIOW);
   ICHECK(trans_kernel_layout.defined())
-      << "Conv only support kernel layouts that are convertible from OIW."
+      << "Conv only support kernel layouts that are convertible from IOW."
       << " But got " << kernel_layout;
 
   Layout out_layout(param->out_layout == "" ? param->data_layout : param->out_layout);
@@ -979,16 +979,18 @@ bool Conv1DTransposeRel(const Array<Type>& types, int num_inputs, const Attrs& a
       ICHECK_EQ(param->kernel_size.size(), 1);
       // check the size
       ICHECK(reporter->AssertEQ(param->kernel_size[0], wshape[2]))
-          << "Conv1D: shape of weight is inconsistent with kernel_size, "
+          << "Conv1DTraspose: shape of weight is inconsistent with kernel_size, "
           << " kernel_size=" << param->kernel_size << " wshape=" << Array<IndexExpr>(wshape);
     }
     if (param->channels.defined()) {
-      ICHECK(reporter->AssertEQ(param->channels, wshape[1]))
-          << "Conv1D: shape of weight is inconsistent with channels, "
-          << " channels=" << param->channels << " wshape=" << Array<IndexExpr>(wshape);
+      ICHECK(reporter->AssertEQ(indexdiv(param->channels, param->groups), wshape[1]))
+          << "Conv1DTraspose: shape of weight is inconsistent with channels, "
+          << " out_channels // groups != weight.shape[1] "
+          << " out_channels=" << param->channels << " groups=" << param->groups
+          << " wshape=" << Array<IndexExpr>(wshape);
     }
     if (!dshape_ncw[1].as<tir::AnyNode>() && !wshape[0].as<tir::AnyNode>()) {
-      ICHECK(reporter->AssertEQ(indexdiv(dshape_ncw[1], param->groups), wshape[0]));
+      ICHECK(reporter->AssertEQ(dshape_ncw[1], wshape[0]));
     }
     channels = wshape[1];
     dilated_ksize_x = 1 + (wshape[2] - 1) * param->dilation[0];