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[MXNET-1330] Bring nnvm::Tuple to mxnet::Tuple #14270

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22 changes: 11 additions & 11 deletions docs/architecture/overview.md
Original file line number Diff line number Diff line change
Expand Up @@ -301,9 +301,9 @@ The `OperatorProperty` interface consists of:
* **InferShape:**

```c++
virtual bool InferShape(std::vector<TShape> *in_shape,
std::vector<TShape> *out_shape,
std::vector<TShape> *aux_shape) const = 0;
virtual bool InferShape(mxnet::ShapeVector *in_shape,
mxnet::ShapeVector *out_shape,
mxnet::ShapeVector *aux_shape) const = 0;
```

This interface has two purposes:
Expand All @@ -322,9 +322,9 @@ MXNet defines two interfaces to achieve this:

```c++
virtual std::vector<ResourceRequest> ForwardResource(
const std::vector<TShape> &in_shape) const;
const mxnet::ShapeVector &in_shape) const;
virtual std::vector<ResourceRequest> BackwardResource(
const std::vector<TShape> &in_shape) const;
const mxnet::ShapeVector &in_shape) const;
```
The `ResourceRequest` structure (in `resource.h`) currently contains only a type flag:

Expand Down Expand Up @@ -473,7 +473,7 @@ To do so, you could define a `ConvolutionParam` structure, as follows:
```c++
#include <dmlc/parameter.h>
struct ConvolutionParam : public dmlc::Parameter<ConvolutionParam> {
TShape kernel, stride, pad;
mxnet::TShape kernel, stride, pad;
uint32_t num_filter, num_group, workspace;
bool no_bias;
};
Expand Down Expand Up @@ -582,10 +582,10 @@ must be provided before any calculation occurs.
let's check input data shape consistency and provide output shape.

```cpp
typedef TShape (*UnaryShapeFunction)(const TShape& src,
typedef mxnet::TShape (*UnaryShapeFunction)(const mxnet::TShape& src,
const EnvArguments& env);
typedef TShape (*BinaryShapeFunction)(const TShape& lhs,
const TShape& rhs,
typedef mxnet::TShape (*BinaryShapeFunction)(const mxnet::TShape& lhs,
const mxnet::TShape& rhs,
const EnvArguments& env);
```
You can use `mshadow::TShape` to check input data shape and designate output data shape.
Expand All @@ -611,9 +611,9 @@ In our smooth l1 loss example, it's okay to use the default behavior whereby the
Written explicitly, it is:

```cpp
inline TShape SmoothL1Shape_(const TShape& src,
inline mxnet::TShape SmoothL1Shape_(const mxnet::TShape& src,
const EnvArguments& env) {
return TShape(src);
return mxnet::TShape(src);
}
```

Expand Down
8 changes: 4 additions & 4 deletions docs/faq/add_op_in_backend.md
Original file line number Diff line number Diff line change
Expand Up @@ -175,8 +175,8 @@ element-wise multiplication and addition.
For our `quadratic` operator, shape inference possesses quite similar logic.
```cpp
inline bool QuadraticOpShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape>* in_attrs,
std::vector<TShape>* out_attrs) {
mxnet::ShapeVector* in_attrs,
mxnet::ShapeVector* out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);

Expand Down Expand Up @@ -216,8 +216,8 @@ The function `QuadraticOpShape` posted here is for the purpose of illustration o
```cpp
template<int n_in, int n_out>
inline bool ElemwiseShape(const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_attrs,
std::vector<TShape> *out_attrs);
mxnet::ShapeVector *in_attrs,
mxnet::ShapeVector *out_attrs);
```

The same logic goes for data type inference. We will leave the analysis of
Expand Down
2 changes: 1 addition & 1 deletion docs/faq/new_op.md
Original file line number Diff line number Diff line change
Expand Up @@ -258,7 +258,7 @@ can add argument descriptions in bulk with `.add_arguments(ActivationParam::__FI

#### FInferShape or TIsBackward (for Backward Only Ops)

Normally operators need to have `FInferShape` with prototype `bool(const nnvm::NodeAttrs& attrs, std::vector<TShape> *in_attrs, std::vector<TShape> *out_attrs)`. `FInferShape` fills unknown shapes (`shape.ndim() == 0`) in in_attrs/out_attrs based on known shapes in in_attrs/out_attrs. Use `ElemwiseShape<n_in, n_out>` for simple operators with uniform shapes.
Normally operators need to have `FInferShape` with prototype `bool(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector *in_attrs, mxnet::ShapeVector *out_attrs)`. `FInferShape` fills unknown shapes (`shape.ndim() == 0`) in in_attrs/out_attrs based on known shapes in in_attrs/out_attrs. Use `ElemwiseShape<n_in, n_out>` for simple operators with uniform shapes.

Operators that are only used for a backward pass can instead register `.set_attr<nnvm::TIsBackward>("TIsBackward", true)`
and their shapes with be copied from the corresponding forward operators.
Expand Down
3 changes: 1 addition & 2 deletions include/mxnet/base.h
Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,7 @@
#include "nnvm/tuple.h"
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#include "nnvm/symbolic.h"
#include "libinfo.h"
#include "tuple.h"


/*!
Expand Down Expand Up @@ -95,8 +96,6 @@ typedef mshadow::gpu gpu;
typedef mshadow::index_t index_t;
/*! \brief data type that will be used to store ndarray */
typedef mshadow::default_real_t real_t;
/*! \brief Shape data structure used to record shape information */
using TShape = nnvm::TShape;
/*! \brief operator structure from NNVM */
using Op = nnvm::Op;

Expand Down
4 changes: 2 additions & 2 deletions include/mxnet/executor.h
Original file line number Diff line number Diff line change
Expand Up @@ -121,7 +121,7 @@ class Executor {
const bool allow_up_sizing,
const Context& default_ctx,
const std::map<std::string, Context>& ctx_map,
const std::unordered_map<std::string, TShape>&
const std::unordered_map<std::string, mxnet::TShape>&
provided_arg_shapes,
std::vector<NDArray>* in_args,
std::vector<NDArray>* arg_grads,
Expand Down Expand Up @@ -155,7 +155,7 @@ class Executor {
const std::vector<Context>& in_arg_ctxes,
const std::vector<Context>& arg_grad_ctxes,
const std::vector<Context>& aux_state_ctxes,
const std::unordered_map<std::string, TShape>& arg_shape_map,
const std::unordered_map<std::string, mxnet::TShape>& arg_shape_map,
const std::unordered_map<std::string, int>& arg_dtype_map,
const std::unordered_map<std::string, int>& arg_stype_map,
const std::vector<OpReqType>& grad_req_types,
Expand Down
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