Fix FInferShape for some ops to support partial type inference

src/operator/contrib/batch_norm_relu.cc

@@ -55,6 +55,9 @@ static bool BatchNormWithReLUShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_shape->size(), 5U) << "Input:[data, gamma, beta, MovingMean, MovingVar]";
   CHECK_EQ(out_shape->size(), 4U);
   const mxnet::TShape &dshape = in_shape->at(batchnormrelu::kData);
+  if (!mxnet::ndim_is_known(dshape)) {
+    return false;
+  }
 
   const size_t channelAxis = static_cast<size_t>(param.axis < 0
       ? static_cast<int>(dshape.ndim()) + param.axis
@@ -63,10 +66,6 @@ static bool BatchNormWithReLUShape(const nnvm::NodeAttrs& attrs,
 
   const int channelCount = dshape[channelAxis];
 
-  if (!mxnet::ndim_is_known(dshape)) {
-    return false;
-  }
-
   in_shape->at(batchnormrelu::kGamma) = mxnet::TShape(Shape1(channelCount));
   in_shape->at(batchnormrelu::kBeta) = mxnet::TShape(Shape1(channelCount));
   in_shape->at(batchnormrelu::kInMovingMean) = mxnet::TShape(Shape1(channelCount));  // kMovingMean

src/operator/nn/batch_norm.cc

@@ -323,6 +323,9 @@ static bool BatchNormShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_shape->size(), 5U) << "Input:[data, gamma, beta, MovingMean, MovingVar]";
   CHECK_EQ(out_shape->size(), 3U);
   const mxnet::TShape &dshape = in_shape->at(batchnorm::kData);
+  if (!mxnet::ndim_is_known(dshape)) {
+    return false;
+  }
 
   const size_t channelAxis = static_cast<size_t>(param.axis < 0
       ? static_cast<int>(dshape.ndim()) + param.axis
@@ -331,10 +334,6 @@ static bool BatchNormShape(const nnvm::NodeAttrs& attrs,
 
   const index_t channelCount = dshape[channelAxis];
 
-  if (!mxnet::ndim_is_known(dshape)) {
-    return false;
-  }
-
   in_shape->at(batchnorm::kGamma) = mxnet::TShape(Shape1(channelCount));
   in_shape->at(batchnorm::kBeta) = mxnet::TShape(Shape1(channelCount));
   in_shape->at(batchnorm::kInMovingMean) = mxnet::TShape(Shape1(channelCount));  // kMovingMean

src/operator/nn/group_norm.cc

@@ -39,14 +39,14 @@ static bool GroupNormShape(const nnvm::NodeAttrs& attrs,
   using namespace mshadow;
   CHECK_EQ(in_shape->size(), 3U) << "Input:[data, gamma, beta]";
   const mxnet::TShape &dshape = in_shape->at(groupnorm::kData);
-  CHECK_GE(dshape.ndim(), 3U);
-  const int num_groups = param.num_groups;
-  CHECK_EQ(dshape[1] % num_groups, 0) << "# of channels must be divisible by # of groups";
-
   if (!mxnet::ndim_is_known(dshape)) {
     return false;
   }
 
+  CHECK_GE(dshape.ndim(), 3U);
+  const int num_groups = param.num_groups;
+  CHECK_EQ(dshape[1] % num_groups, 0) << "# of channels must be divisible by # of groups";
+
   in_shape->at(groupnorm::kGamma) = mxnet::TShape(Shape1(dshape[1]));
   in_shape->at(groupnorm::kBeta) = mxnet::TShape(Shape1(dshape[1]));
 

src/operator/nn/layer_norm.cc

@@ -43,15 +43,16 @@ static bool LayerNormShape(const nnvm::NodeAttrs& attrs,
   using namespace mshadow;
   CHECK_EQ(in_shape->size(), 3U) << "Input:[data, gamma, beta]";
   const mxnet::TShape &dshape = in_shape->at(layernorm::kData);
+  if (!mxnet::ndim_is_known(dshape)) {
+    return false;
+  }
+
   int axis = GetRealAxis(param.axis, dshape.ndim());
   CHECK(axis >= 0 && axis < dshape.ndim())
     << "Channel axis out of range: axis=" << param.axis;
 
   const index_t channelCount = dshape[axis];
 
-  if (!mxnet::ndim_is_known(dshape)) {
-    return false;
-  }
   SHAPE_ASSIGN_CHECK(*in_shape,
                      layernorm::kGamma,
                      mxnet::TShape(Shape1(channelCount)));

src/operator/nn/pooling.cc

@@ -95,10 +95,15 @@ static bool PoolingShape(const nnvm::NodeAttrs &attrs,
                          mxnet::ShapeVector *out_shape) {
   const PoolingParam &param = nnvm::get<PoolingParam>(attrs.parsed);
   CHECK_EQ(in_shape->size(), 1U);
+  const mxnet::TShape &dshape = (*in_shape)[0];
+  if (!mxnet::ndim_is_known(dshape)) {
+    return false;
+  }
+
   if (param.pool_type == pool_enum::kLpPooling) {
     CHECK(param.p_value.has_value());
   }
-  const mxnet::TShape &dshape = (*in_shape)[0];
+
   if (param.pooling_convention == pool_enum::kSame) {
     CHECK_EQ(dshape.ndim(), 3U)
       << "Pooling: Input data should be 3D in (batch, channel, x)"
@@ -114,7 +119,7 @@ static bool PoolingShape(const nnvm::NodeAttrs &attrs,
       << "Pooling: Input data should be  3D in (batch, channel, x)"
       << " Or 4D in (batch, channel, y, x) "
       << " Or 5D in (batch, channel, d, y, x)";
-  if (!mxnet::ndim_is_known(dshape)) return false;
+
   int layout = param.GetLayout(dshape.ndim());
   if (param.global_pool) {
     mxnet::TShape oshape = dshape;

src/operator/tensor/matrix_op-inl.h

@@ -456,9 +456,9 @@ inline bool TransposeShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(out_attrs->size(), 1U);
   mxnet::TShape& shp = (*in_attrs)[0];
   mxnet::TShape& out_shp = (*out_attrs)[0];
-  CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
-  if (shp.ndim() == -1 && out_shp.ndim() == -1)
+  if (!mxnet::ndim_is_known(shp) && !mxnet::ndim_is_known(out_shp))
     return false;  // none of the shapes is known
+  CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
   if (out_shp.ndim() >= 0 && shp.ndim() >= 0)
     CHECK_EQ(out_shp.ndim(), shp.ndim());
   mxnet::TShape get(std::max(shp.ndim(), out_shp.ndim()), -1);
@@ -513,12 +513,12 @@ inline bool ExpandDimShape(const nnvm::NodeAttrs& attrs,
   const ExpandDimParam& param = nnvm::get<ExpandDimParam>(attrs.parsed);
   CHECK_EQ(in_attrs->size(), 1U);
   CHECK_EQ(out_attrs->size(), 1U);
-  if (!mxnet::ndim_is_known(in_attrs->at(0)) && !mxnet::ndim_is_known(out_attrs->at(0))) {
+  mxnet::TShape& ishape = (*in_attrs)[0];
+  mxnet::TShape& oshape = (*out_attrs)[0];
+  if (!mxnet::ndim_is_known(ishape) && !mxnet::ndim_is_known(oshape)) {
     return false;
   }
 
-  mxnet::TShape& ishape = (*in_attrs)[0];
-  mxnet::TShape& oshape = (*out_attrs)[0];
   int indim = ishape.ndim();
   bool unknown_ishape = false;
   if (-1 == indim) {
@@ -1441,6 +1441,9 @@ inline bool SliceLikeShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(out_attrs->size(), 1U);
   mxnet::TShape& ishape = (*in_attrs)[0];
   mxnet::TShape& from_shape = (*in_attrs)[1];
+  if (!mxnet::ndim_is_known(ishape) || !mxnet::ndim_is_known(from_shape)) {
+    return false;
+  }
   if (param.axes.ndim() == 0) {
     CHECK_EQ(ishape.ndim(), from_shape.ndim())
       << "By default slice_axis performs slice on all axes, but ndim mismatch "
@@ -1749,6 +1752,9 @@ inline bool RepeatOpShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(in_attrs->size(), 1U);
   CHECK_EQ(out_attrs->size(), 1U);
   const mxnet::TShape& ishape = (*in_attrs)[0];
+  if (!mxnet::ndim_is_known(ishape)) {
+    return false;
+  }
   int repeats = 0;
   dmlc::optional<int> axisOpt;
   GetRepeatParams(param, ishape, &repeats, &axisOpt);
@@ -2427,6 +2433,9 @@ inline bool DepthToSpaceOpShape(const nnvm::NodeAttrs& attrs,
   mxnet::TShape expected_out(4, -1);
 
   mxnet::TShape& in_shape = in_attrs->at(0);
+  if (!mxnet::ndim_is_known(in_shape)) {
+    return false;
+  }
   int block = param.block_size;
   CHECK_NE(block, 0) << "block_size must be a positive integer value";
   CHECK_NE(in_shape[1], 0) << "Depth dimension:1 cannot be 0";
@@ -2591,6 +2600,9 @@ inline bool SpaceToDepthOpShape(const nnvm::NodeAttrs& attrs,
   mxnet::TShape expected_out(in_attrs->at(0).ndim(), -1);
 
   mxnet::TShape& in_shape = in_attrs->at(0);
+  if (!mxnet::ndim_is_known(in_shape)) {
+    return false;
+  }
   int block = param.block_size;
   CHECK_NE(block, 0) << "block_size must be a positive integer value";
   CHECK_NE(in_shape[0], 0)