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Backporting backward inference from 2.x apache#18348 and apache#18378
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Signed-off-by: Serge Panev <spanev@nvidia.com>
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@Kh4L
Kh4L committed Aug 10, 2020
1 parent 0b5b449 commit b4530ab
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Showing 9 changed files with 125 additions and 47 deletions.
41 changes: 28 additions & 13 deletions src/operator/contrib/batch_norm_relu.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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
Expand All @@ -84,14 +83,36 @@ static bool BatchNormWithReLUType(const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type, std::vector<int> *out_type) {
using namespace mshadow;
CHECK_GE(in_type->size(), 1U);
const int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
const size_t n_out = 4;
// For float16 input type beta, gamma, mean, and average are stored in float32.
// For other input types, these parameters have the same type as input
// NOTE: This requirement is from cuDNN (v. 4 and 5)
int dtype_param;
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
int dtype = (*in_type)[0];

if (type_is_none(dtype)) {
// Input type is undefined, we try backward inference
if (out_type->size() == 0 || type_is_none((*out_type)[0])) {
// Neither the input nor the output are defined,
// types cannot be infered for this op
return false;
} else {
// Input type is undefined but output type is: backward inference
dtype = (*out_type)[0];
(*in_type)[0] = dtype;
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
dtype_param = mshadow::DataType<AccRealX>::kFlag; });
}
} else {
// Input type is defined but output type is not: forward inference
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
dtype_param = mshadow::DataType<AccRealX>::kFlag; });
out_type->clear();
out_type->push_back(dtype);
for (size_t i = 1; i < n_out; ++i) {
out_type->push_back(dtype_param);
}
}
std::vector<std::string> args{"data", "gamma", "beta", "mean", "var"};
CHECK_LE(in_type->size(), args.size());
for (size_t i = 1; i < in_type->size(); ++i) {
Expand All @@ -101,12 +122,6 @@ static bool BatchNormWithReLUType(const nnvm::NodeAttrs& attrs,
UNIFORM_TYPE_CHECK((*in_type)[i], dtype_param, args[i]);
}
}
const size_t n_out = 4;
out_type->clear();
out_type->push_back(dtype);
for (size_t i = 1; i < n_out; ++i) {
out_type->push_back(dtype_param);
}
return true;
}

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40 changes: 27 additions & 13 deletions src/operator/nn/batch_norm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -365,6 +365,9 @@ 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
Expand All @@ -373,10 +376,6 @@ static bool BatchNormShape(const nnvm::NodeAttrs& attrs,

const int 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
Expand All @@ -394,14 +393,35 @@ static bool BatchNormType(const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type, std::vector<int> *out_type) {
using namespace mshadow;
CHECK_GE(in_type->size(), 1U);
const int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
const size_t n_out = 3;
// For float16 input type beta, gamma, mean, and average are stored in float32.
// For other input types, these parameters have the same type as input
// NOTE: This requirement is from cuDNN (v. 4 and 5)
int dtype_param;
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
int dtype = (*in_type)[0];
if (type_is_none(dtype)) {
// Input type is undefined, we try backward inference
if (out_type->size() == 0 || type_is_none((*out_type)[0])) {
// Neither the input nor the output are defined,
// types cannot be infered for this op
return false;
} else {
// Input type is undefined but output type is: backward inference
dtype = (*out_type)[0];
(*in_type)[0] = dtype;
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
dtype_param = mshadow::DataType<AccRealX>::kFlag; });
}
} else {
// Input type is defined but output type is not: forward inference
MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, {
dtype_param = mshadow::DataType<AccRealX>::kFlag; });
out_type->clear();
out_type->push_back(dtype);
for (size_t i = 1; i < n_out; ++i) {
out_type->push_back(dtype_param);
}
}
std::vector<std::string> args{"data", "gamma", "beta", "mean", "var"};
CHECK_LE(in_type->size(), args.size());
for (size_t i = 1; i < in_type->size(); ++i) {
Expand All @@ -411,12 +431,6 @@ static bool BatchNormType(const nnvm::NodeAttrs& attrs,
UNIFORM_TYPE_CHECK((*in_type)[i], dtype_param, args[i]);
}
}
const size_t n_out = 3;
out_type->clear();
out_type->push_back(dtype);
for (size_t i = 1; i < n_out; ++i) {
out_type->push_back(dtype_param);
}
return true;
}

Expand Down
13 changes: 10 additions & 3 deletions src/operator/nn/convolution.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -285,16 +285,23 @@ static bool ConvolutionType(const nnvm::NodeAttrs& attrs,
const ConvolutionParam& param_ = nnvm::get<ConvolutionParam>(attrs.parsed);
CHECK_GE(in_type->size(), 1U);
int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
if (type_is_none(dtype)) {
if (out_type->size() == 0 || type_is_none((*out_type)[0])) {
return false;
} else {
dtype = (*out_type)[0];
}
} else {
out_type->clear();
out_type->push_back(dtype);
}
for (size_t i = 0; i < in_type->size(); ++i) {
if ((*in_type)[i] == -1) {
(*in_type)[i] = dtype;
} else {
UNIFORM_TYPE_CHECK((*in_type)[i], dtype, ListArguments(param_)[i]);
}
}
out_type->clear();
out_type->push_back(dtype);
return true;
}

Expand Down
18 changes: 15 additions & 3 deletions src/operator/nn/deconvolution.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -332,16 +332,28 @@ static bool DeconvolutionType(const nnvm::NodeAttrs& attrs,
const DeconvolutionParam& param_ = nnvm::get<DeconvolutionParam>(attrs.parsed);
CHECK_GE(in_type->size(), 1U);
int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
if (type_is_none(dtype)) {
// Input type is undefined, we try backward inference
if (out_type->size() == 0 || type_is_none((*out_type)[0])) {
// Neither the input nor the output are defined,
// types cannot be infered for this op
return false;
} else {
// Input type is undefined but output type is: backward inference
dtype = (*out_type)[0];
}
} else {
// Input type is defined but output type is not: forward inference
out_type->clear();
out_type->push_back(dtype);
}
for (size_t i = 0; i < in_type->size(); ++i) {
if ((*in_type)[i] == -1) {
(*in_type)[i] = dtype;
} else {
UNIFORM_TYPE_CHECK((*in_type)[i], dtype, ListArguments(param_)[i]);
}
}
out_type->clear();
out_type->push_back(dtype);
return true;
}

Expand Down
8 changes: 4 additions & 4 deletions src/operator/nn/group_norm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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(num_groups));
in_shape->at(groupnorm::kBeta) = mxnet::TShape(Shape1(num_groups));

Expand Down
7 changes: 4 additions & 3 deletions src/operator/nn/layer_norm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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 int channelCount = dshape[axis];

if (!mxnet::ndim_is_known(dshape)) {
return false;
}
SHAPE_ASSIGN_CHECK(*in_shape,
layernorm::kGamma,
mxnet::TShape(Shape1(channelCount)));
Expand Down
7 changes: 5 additions & 2 deletions src/operator/nn/pooling.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -95,10 +95,14 @@ 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)"
Expand All @@ -114,7 +118,6 @@ 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;
Expand Down
18 changes: 15 additions & 3 deletions src/operator/softmax_output.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -66,16 +66,28 @@ static bool SoftmaxOutputType(const nnvm::NodeAttrs& attrs,
std::vector<int> *out_type) {
CHECK_EQ(in_type->size(), 2U);
int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
if (type_is_none(dtype)) {
// Input type is undefined, we try backward inference
if (out_type->size() == 0 || type_is_none((*out_type)[0])) {
// Neither the input nor the output are defined,
// types cannot be infered for this op
return false;
} else {
// Input type is undefined but output type is: backward inference
dtype = (*out_type)[0];
}
} else {
// Input type is defined but output type is not: forward inference
out_type->clear();
out_type->push_back(dtype);
}
for (size_t i = 0; i < in_type->size(); ++i) {
if ((*in_type)[i] == -1) {
(*in_type)[i] = dtype;
} else {
UNIFORM_TYPE_CHECK((*in_type)[i], dtype, ListArguments()[i]);
}
}
out_type->clear();
out_type->push_back(dtype);
return true;
}

Expand Down
20 changes: 17 additions & 3 deletions src/operator/tensor/matrix_op-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -455,9 +455,9 @@ 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);
Expand Down Expand Up @@ -506,7 +506,9 @@ 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;
}

Expand Down Expand Up @@ -1434,6 +1436,9 @@ 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 "
Expand Down Expand Up @@ -1727,6 +1732,9 @@ 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);
Expand Down Expand Up @@ -2395,6 +2403,9 @@ 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";
Expand Down Expand Up @@ -2559,6 +2570,9 @@ 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)
Expand Down

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