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Fix infer shape partial after unknown shape changed to -1 #14869

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May 21, 2019
Merged

Fix infer shape partial after unknown shape changed to -1 #14869

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64d36af
change check and shape_is_known
roywei May 3, 2019
cd010f6
rever some changes
roywei May 10, 2019
35c64f7
revert
roywei May 10, 2019
09da737
revert
roywei May 10, 2019
b1ae6f3
revert
roywei May 10, 2019
6023a12
add test
roywei May 10, 2019
f5092e4
add more tests
roywei May 13, 2019
c0ca8c9
update test dot
roywei May 14, 2019
c773ebc
fix test
roywei May 14, 2019
57e4207
update reduce axes
roywei May 14, 2019
fdf2108
fix lint
roywei May 14, 2019
f1bd014
update check
roywei May 16, 2019
616b9d6
fix lint
roywei May 16, 2019
bcaa9b3
address comments
roywei May 20, 2019
04002ba
remove invalid test case
roywei May 20, 2019
9ab7000
run all tests
roywei May 21, 2019
c9c2abf
update test case
roywei May 21, 2019
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2 changes: 1 addition & 1 deletion src/operator/instance_norm-inl.h
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Expand Up @@ -113,7 +113,7 @@ class InstanceNormOp : public Operator {
CHECK_EQ(in_data.size(), 3U);
CHECK_EQ(out_data.size(), 3U);

CHECK_GE(in_data[instance_norm::kData].ndim(), 3U)
CHECK_GE(in_data[instance_norm::kData].ndim(), 3)
<< "InstanceNorm only supports input tensors of rank > 2.";

Stream<xpu> *s = ctx.get_stream<xpu>();
Expand Down
12 changes: 6 additions & 6 deletions src/operator/l2_normalization-inl.h
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Expand Up @@ -102,7 +102,7 @@ class L2NormalizationOp : public Operator {
norm = F<mxnet::op::mshadow_op::square_root>(norm);
out = data / broadcast<0>(norm, out.shape_);
} else if (param_.mode == l2_normalization::kChannel) {
CHECK_GE(orig_shape.ndim(), 3U);
CHECK_GE(orig_shape.ndim(), 3);
Shape<3> dshape = Shape3(orig_shape[0], orig_shape[1],
orig_shape.ProdShape(2, orig_shape.ndim()));
Tensor<xpu, 3, DType> data = in_data[l2_normalization::kData]
Expand All @@ -120,7 +120,7 @@ class L2NormalizationOp : public Operator {
norm = F<mxnet::op::mshadow_op::square_root>(norm);
out = data / broadcast_with_axis(norm, 0, orig_shape[1]);
} else if (param_.mode == l2_normalization::kSpatial) {
CHECK_GE(orig_shape.ndim(), 3U);
CHECK_GE(orig_shape.ndim(), 3);
Shape<3> dshape = Shape3(orig_shape[0], orig_shape[1],
orig_shape.ProdShape(2, orig_shape.ndim()));
Tensor<xpu, 3, DType> data = in_data[l2_normalization::kData]
Expand Down Expand Up @@ -174,7 +174,7 @@ class L2NormalizationOp : public Operator {
(grad_out - data * broadcast<0>(temp, data.shape_)) /
broadcast<0>(norm, data.shape_));
} else if (param_.mode == l2_normalization::kChannel) {
CHECK_GE(orig_shape.ndim(), 3U);
CHECK_GE(orig_shape.ndim(), 3);
Shape<3> dshape = Shape3(orig_shape[0], orig_shape[1],
orig_shape.ProdShape(2, orig_shape.ndim()));
Tensor<xpu, 3, DType> data = out_data[l2_normalization::kOut]
Expand All @@ -193,7 +193,7 @@ class L2NormalizationOp : public Operator {
(grad_out - data * broadcast_with_axis(temp, 0, orig_shape[1])) /
broadcast_with_axis(norm, 0, orig_shape[1]));
} else if (param_.mode == l2_normalization::kSpatial) {
CHECK_GE(orig_shape.ndim(), 3U);
CHECK_GE(orig_shape.ndim(), 3);
Shape<3> dshape = Shape3(orig_shape[0], orig_shape[1],
orig_shape.ProdShape(2, orig_shape.ndim()));
Tensor<xpu, 3, DType> data = out_data[l2_normalization::kOut]
Expand Down Expand Up @@ -273,12 +273,12 @@ class L2NormalizationProp : public OperatorProperty {
if (param_.mode == l2_normalization::kInstance) {
out_shape->push_back(Shape1(dshape[0]));
} else if (param_.mode == l2_normalization::kChannel) {
CHECK_GE(dshape.ndim(), 3U) << "At lease 3 dimensions required in channel mode";
CHECK_GE(dshape.ndim(), 3) << "At lease 3 dimensions required in channel mode";
mxnet::TShape norm_shape = dshape;
norm_shape[1] = 1;
out_shape->push_back(norm_shape);
} else if (param_.mode == l2_normalization::kSpatial) {
CHECK_GE(dshape.ndim(), 3U) << "At lease 3 dimensions required in spatial mode";
CHECK_GE(dshape.ndim(), 3) << "At lease 3 dimensions required in spatial mode";
out_shape->push_back(Shape2(dshape[0], dshape[1]));
} else {
return false;
Expand Down
4 changes: 2 additions & 2 deletions src/operator/l2_normalization.cc
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Expand Up @@ -70,7 +70,7 @@ class L2NormalizationOpCPU : public L2NormalizationOp<cpu, DType> {
}
}
} else if (this->param_.mode == l2_normalization::kChannel) {
CHECK_GE(orig_shape.ndim(), 3U);
CHECK_GE(orig_shape.ndim(), 3);
Shape<3> dshape = Shape3(orig_shape[0], orig_shape[1],
orig_shape.ProdShape(2, orig_shape.ndim()));
Tensor<cpu, 3, DType> data = in_data[l2_normalization::kData]
Expand All @@ -94,7 +94,7 @@ class L2NormalizationOpCPU : public L2NormalizationOp<cpu, DType> {
}
}
} else if (this->param_.mode == l2_normalization::kSpatial) {
CHECK_GE(orig_shape.ndim(), 3U);
CHECK_GE(orig_shape.ndim(), 3);
Shape<3> dshape = Shape3(orig_shape[0], orig_shape[1],
orig_shape.ProdShape(2, orig_shape.ndim()));
Tensor<cpu, 3, DType> data = in_data[l2_normalization::kData]
Expand Down
36 changes: 18 additions & 18 deletions src/operator/nn/mkldnn/mkldnn_convolution.cc
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Expand Up @@ -63,15 +63,15 @@ mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(const MKLDNNConvFullP
mkldnn::memory::dims strides(param.conv_param.kernel.ndim());
mkldnn::memory::dims padding(param.conv_param.kernel.ndim());
if (param.conv_param.kernel.ndim() == 1) {
CHECK_GE(param.conv_param.stride.ndim(), 1U);
CHECK_GE(param.conv_param.pad.ndim(), 1U);
CHECK_GE(param.conv_param.dilate.ndim(), 1U);
CHECK_GE(param.conv_param.stride.ndim(), 1);
CHECK_GE(param.conv_param.pad.ndim(), 1);
CHECK_GE(param.conv_param.dilate.ndim(), 1);
strides[0] = param.conv_param.stride[0];
padding[0] = param.conv_param.pad[0];
} else if (param.conv_param.kernel.ndim() == 2) {
CHECK_GE(param.conv_param.stride.ndim(), 2U);
CHECK_GE(param.conv_param.pad.ndim(), 2U);
CHECK_GE(param.conv_param.dilate.ndim(), 2U);
CHECK_GE(param.conv_param.stride.ndim(), 2);
CHECK_GE(param.conv_param.pad.ndim(), 2);
CHECK_GE(param.conv_param.dilate.ndim(), 2);
strides[0] = param.conv_param.stride[0];
strides[1] = param.conv_param.stride[1];
padding[0] = param.conv_param.pad[0];
Expand Down Expand Up @@ -169,15 +169,15 @@ static mkldnn::convolution_backward_data::primitive_desc GetConvBwdData(
mkldnn::memory::dims strides(param.kernel.ndim());
mkldnn::memory::dims padding(param.kernel.ndim());
if (param.kernel.ndim() == 1) {
CHECK_GE(param.stride.ndim(), 1U);
CHECK_GE(param.pad.ndim(), 1U);
CHECK_GE(param.dilate.ndim(), 1U);
CHECK_GE(param.stride.ndim(), 1);
CHECK_GE(param.pad.ndim(), 1);
CHECK_GE(param.dilate.ndim(), 1);
strides[0] = param.stride[0];
padding[0] = param.pad[0];
} else if (param.kernel.ndim() == 2) {
CHECK_GE(param.stride.ndim(), 2U);
CHECK_GE(param.pad.ndim(), 2U);
CHECK_GE(param.dilate.ndim(), 2U);
CHECK_GE(param.stride.ndim(), 2);
CHECK_GE(param.pad.ndim(), 2);
CHECK_GE(param.dilate.ndim(), 2);
strides[0] = param.stride[0];
strides[1] = param.stride[1];
padding[0] = param.pad[0];
Expand Down Expand Up @@ -237,15 +237,15 @@ static mkldnn::convolution_backward_weights::primitive_desc GetConvBwdWeights(
mkldnn::memory::dims strides(param.kernel.ndim());
mkldnn::memory::dims padding(param.kernel.ndim());
if (param.kernel.ndim() == 1) {
CHECK_GE(param.stride.ndim(), 1U);
CHECK_GE(param.pad.ndim(), 1U);
CHECK_GE(param.dilate.ndim(), 1U);
CHECK_GE(param.stride.ndim(), 1);
CHECK_GE(param.pad.ndim(), 1);
CHECK_GE(param.dilate.ndim(), 1);
strides[0] = param.stride[0];
padding[0] = param.pad[0];
} else if (param.kernel.ndim() == 2) {
CHECK_GE(param.stride.ndim(), 2U);
CHECK_GE(param.pad.ndim(), 2U);
CHECK_GE(param.dilate.ndim(), 2U);
CHECK_GE(param.stride.ndim(), 2);
CHECK_GE(param.pad.ndim(), 2);
CHECK_GE(param.dilate.ndim(), 2);
strides[0] = param.stride[0];
strides[1] = param.stride[1];
padding[0] = param.pad[0];
Expand Down
18 changes: 9 additions & 9 deletions src/operator/nn/mkldnn/mkldnn_deconvolution.cc
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Expand Up @@ -90,9 +90,9 @@ static mkldnn::convolution_backward_data::primitive_desc GetDeconvFwdImpl(
auto weight_md = GetWeightDesc(weights, param.num_group);
auto out_md = GetMemDesc(output);
auto engine = CpuEngine::Get()->get_engine();
CHECK_GE(param.stride.ndim(), 2U);
CHECK_GE(param.pad.ndim(), 2U);
CHECK_GE(param.dilate.ndim(), 2U);
CHECK_GE(param.stride.ndim(), 2);
CHECK_GE(param.pad.ndim(), 2);
CHECK_GE(param.dilate.ndim(), 2);
mkldnn::memory::dims strides{0, 0};
strides[0] = param.stride[0];
strides[1] = param.stride[1];
Expand Down Expand Up @@ -128,9 +128,9 @@ static mkldnn::convolution_forward::primitive_desc GetDeconvBwdDataImpl(
auto weight_md = GetWeightDesc(weights, param.num_group);
auto out_md = GetMemDesc(output);
auto engine = CpuEngine::Get()->get_engine();
CHECK_GE(param.stride.ndim(), 2U);
CHECK_GE(param.pad.ndim(), 2U);
CHECK_GE(param.dilate.ndim(), 2U);
CHECK_GE(param.stride.ndim(), 2);
CHECK_GE(param.pad.ndim(), 2);
CHECK_GE(param.dilate.ndim(), 2);
mkldnn::memory::dims strides{0, 0};
strides[0] = param.stride[0];
strides[1] = param.stride[1];
Expand All @@ -153,9 +153,9 @@ GetDeconvBwdWeightsImpl(
auto weight_md = GetWeightDesc(weights, param.num_group);
auto out_md = GetMemDesc(output);
auto engine = CpuEngine::Get()->get_engine();
CHECK_GE(param.stride.ndim(), 2U);
CHECK_GE(param.pad.ndim(), 2U);
CHECK_GE(param.dilate.ndim(), 2U);
CHECK_GE(param.stride.ndim(), 2);
CHECK_GE(param.pad.ndim(), 2);
CHECK_GE(param.dilate.ndim(), 2);
mkldnn::memory::dims strides{0, 0};
strides[0] = param.stride[0];
strides[1] = param.stride[1];
Expand Down
4 changes: 2 additions & 2 deletions src/operator/nn/pooling.cc
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Expand Up @@ -106,11 +106,11 @@ static bool PoolingShape(const nnvm::NodeAttrs &attrs,
CHECK(param.pad[0] == 0 && param.pad[1] == 0 && param.pad[2] == 0)
<< "Same pooling convention disables the use of pad parameter.";
}
CHECK_GE(dshape.ndim(), 3U)
CHECK_GE(dshape.ndim(), 3)
<< "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)";
CHECK_LE(dshape.ndim(), 5U)
CHECK_LE(dshape.ndim(), 5)
<< "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)";
Expand Down
4 changes: 2 additions & 2 deletions src/operator/pooling_v1-inl.h
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Expand Up @@ -243,9 +243,9 @@ class PoolingV1Prop : public OperatorProperty {
mxnet::ShapeVector *aux_shape) const override {
CHECK_EQ(in_shape->size(), 1U);
const mxnet::TShape &dshape = (*in_shape)[0];
CHECK_GE(dshape.ndim(), 4U) << "Pooling: Input data should be 4D in (batch, channel, y, x) "
CHECK_GE(dshape.ndim(), 4) << "Pooling: Input data should be 4D in (batch, channel, y, x) "
<< "Or 5D in (batch, channel, d, y, x)";
CHECK_LE(dshape.ndim(), 5U) << "Pooling: Input data should be 4D in (batch, channel, y, x) "
CHECK_LE(dshape.ndim(), 5) << "Pooling: Input data should be 4D in (batch, channel, y, x) "
<< "Or 5D in (batch, channel, d, y, x)";
mxnet::TShape oshape = dshape;
if (dshape.ndim() == -1) return false;
Expand Down
8 changes: 4 additions & 4 deletions src/operator/tensor/broadcast_reduce_op.h
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Expand Up @@ -304,12 +304,12 @@ inline bool ReduceAxesShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector *out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
if (!shape_is_known((*in_attrs)[0])) return false;
if (!ndim_is_known((*in_attrs)[0])) return false;
const ReduceAxesParam& param = nnvm::get<ReduceAxesParam>(attrs.parsed);
SHAPE_ASSIGN_CHECK(*out_attrs, 0,
ReduceAxesShapeImpl((*in_attrs)[0], param.axis,
param.keepdims, param.exclude));
return true;
return shape_is_known((*out_attrs)[0]);
}

inline bool ReduceMinMaxAxesShape(const nnvm::NodeAttrs& attrs,
Expand Down Expand Up @@ -1373,7 +1373,7 @@ inline bool PickOpShape(const nnvm::NodeAttrs& attrs,
CHECK_EQ(in_attrs->size(), 2);
CHECK_EQ(out_attrs->size(), 1);
const mxnet::TShape& ishape = (*in_attrs)[0];
if (ishape.ndim() == 0) return false;
if (!ndim_is_known(ishape)) return false;
const PickParam& param = nnvm::get<PickParam>(attrs.parsed);
if (!param.axis) LOG(FATAL)
<< "axis=None is not supported by pick yet. Must specify an axis.";
Expand All @@ -1387,7 +1387,7 @@ inline bool PickOpShape(const nnvm::NodeAttrs& attrs,
SHAPE_ASSIGN_CHECK(*in_attrs, 1,
ReduceAxisShapeImpl(ishape, param.axis, false));
}
return true;
return shape_is_known((*out_attrs)[0]);
}

inline bool PickOpType(const nnvm::NodeAttrs& attrs,
Expand Down
1 change: 1 addition & 0 deletions src/operator/tensor/control_flow_op.h
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Expand Up @@ -175,6 +175,7 @@ inline bool WhereOpShape(const nnvm::NodeAttrs& attrs,
CHECK_EQ(in_attrs->size(), 3U)
<< "where operator takes 3 arguments (" << in_attrs->size() << " given)";
CHECK_EQ(out_attrs->size(), 1U);
if (!mxnet::shape_is_known((*in_attrs)[0])) return false;

mxnet::TShape tshape((*in_attrs)[1]);
if (!shape_assign(&tshape, (*in_attrs)[2])) return false;
Expand Down
15 changes: 13 additions & 2 deletions src/operator/tensor/dot-inl.h
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Expand Up @@ -1207,6 +1207,9 @@ inline bool DotShape(const nnvm::NodeAttrs& attrs,
CHECK_EQ(out_attrs->size(), 1U);
mxnet::TShape& lshape = (*in_attrs)[0];
mxnet::TShape& rshape = (*in_attrs)[1];
if (!ndim_is_known(lshape) || !ndim_is_known(rshape)) return false;
CHECK_GT(lshape.ndim(), 0) << "scalar tensor is not supported by this operator.";
CHECK_GT(rshape.ndim(), 0) << "scalar tensor is not supported by this operator.";
if (lshape.ndim() == 1 && rshape.ndim() == 1) {
CHECK(!param.transpose_a && !param.transpose_b) << "Cannot transpose vectors";
CHECK_EQ(lshape[0], rshape[0]) << "dot shape error: " << lshape << " X " << rshape;
Expand Down Expand Up @@ -1243,7 +1246,8 @@ inline bool DotShape(const nnvm::NodeAttrs& attrs,
mxnet::TShape oshape(buf.begin(), buf.end());
SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
}
return true;
// return true if output shape is fully inferred
return shape_is_known((*out_attrs)[0]);
}

template<typename xpu>
Expand Down Expand Up @@ -1479,7 +1483,13 @@ inline bool BatchDotShape(const nnvm::NodeAttrs& attrs,
const DotParam& param = nnvm::get<DotParam>(attrs.parsed);
mxnet::TShape& lshape = (*in_attrs)[0];
mxnet::TShape& rshape = (*in_attrs)[1];
// return false if lhs and rhs both have fully unknown shape
if (!ndim_is_known(lshape) || !ndim_is_known(rshape)) return false;
if (lshape.ndim() == 3 && rshape.ndim() == 3) {
// only partially infer shape if last dim of lhs and second dim of rhs is known
bool last_dim_known = dim_size_is_known(lshape, 2);
bool second_dim_known = dim_size_is_known(rshape, 1);
if ( !last_dim_known || !second_dim_known) return false;
CHECK(lshape[0] == rshape[0])
<< "batch_dot shape error(batch_size must be equal): " << lshape << " X " << rshape
<< " trans_a=" << param.transpose_a << " trans_b=" << param.transpose_b;
Expand All @@ -1495,7 +1505,8 @@ inline bool BatchDotShape(const nnvm::NodeAttrs& attrs,
LOG(FATAL) << "batch_dot currently only support 3D*3D array"
<< lshape << " v.s. " << rshape;
}
return true;
// return true if output shape is fully inferred
return shape_is_known((*out_attrs)[0]);
}

} // namespace op
Expand Down
4 changes: 2 additions & 2 deletions src/operator/tensor/elemwise_binary_broadcast_op.h
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Expand Up @@ -452,7 +452,7 @@ void BinaryBroadcastComputeSparseEx(const nnvm::NodeAttrs& attrs,
CHECK_EQ(inputs.size(), 2U);
CHECK_EQ(outputs.size(), 1U);
CHECK_EQ(req.size(), 1U);
CHECK_LE(inputs[1].shape().ndim(), 2U)
CHECK_LE(inputs[1].shape().ndim(), 2)
<< "input dense matrix should have less than or equal to 2 dimensions";
if (req[0] == kNullOp) return;
const NDArray& lhs = inputs[0];
Expand Down Expand Up @@ -488,7 +488,7 @@ void BinaryBroadcastComputeDenseEx(const nnvm::NodeAttrs& attrs,
CHECK_EQ(inputs.size(), 2U);
CHECK_EQ(outputs.size(), 1U);
CHECK_EQ(req.size(), 1U);
CHECK_LE(inputs[1].shape().ndim(), 2U)
CHECK_LE(inputs[1].shape().ndim(), 2)
<< "input dense matrix should have less than or equal to 2 dimensions";
if (req[0] == kNullOp) return;
const NDArray& lhs = inputs[0];
Expand Down
4 changes: 2 additions & 2 deletions src/operator/tensor/indexing_op.h
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Expand Up @@ -145,7 +145,7 @@ inline bool EmbeddingOpShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector *out_attrs) {
using namespace mshadow;
const mxnet::TShape &dshape = (*in_attrs)[embedding::kData];
if (!shape_is_known(dshape)) return false;
if (!ndim_is_known(dshape)) return false;
const ParamType& param = nnvm::get<ParamType>(attrs.parsed);
SHAPE_ASSIGN_CHECK(*in_attrs, embedding::kWeight, Shape2(param.input_dim,
param.output_dim));
Expand Down Expand Up @@ -1075,7 +1075,7 @@ inline bool BatchTakeOpShape(const nnvm::NodeAttrs& attrs,
SHAPE_ASSIGN_CHECK(*in_attrs, 1, (*out_attrs)[0]);
}
if ((*in_attrs)[0].ndim() == 0) return false;
CHECK_GE((*in_attrs)[0].ndim(), 2U) << "Data array must have at least 2 dimensional";
CHECK_GE((*in_attrs)[0].ndim(), 2) << "Data array must have at least 2 dimensional";
if ((*out_attrs)[0].ndim() == 0) return false;
CHECK_EQ((*in_attrs)[0].Size()/(*in_attrs)[0][(*in_attrs)[0].ndim()-1],
(*out_attrs)[0].Size())
Expand Down
2 changes: 1 addition & 1 deletion src/operator/tensor/matrix_op-inl.h
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Expand Up @@ -344,7 +344,7 @@ inline bool TransposeShape(const nnvm::NodeAttrs& attrs,
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
mxnet::TShape& shp = (*in_attrs)[0];
CHECK_LE(shp.ndim(), 6U) << "Transpose support at most 6 dimensions";
CHECK_LE(shp.ndim(), 6) << "Transpose support at most 6 dimensions";
mxnet::TShape ret(shp.ndim(), -1);
if (param.axes.ndim() == 0) {
for (int i = 0; i < shp.ndim(); ++i) {
Expand Down
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