Bring nnvm::Tuple to mxnet::Tuple

apache · Feb 26, 2019 · 51228b4 · 51228b4
1 parent f9c436b
commit 51228b4
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diff --git a/docs/architecture/overview.md b/docs/architecture/overview.md
@@ -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:
@@ -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:
 
@@ -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;
     };
@@ -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.
@@ -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);
     }
 ```
 

diff --git a/docs/faq/add_op_in_backend.md b/docs/faq/add_op_in_backend.md
@@ -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);
 
@@ -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

diff --git a/docs/faq/new_op.md b/docs/faq/new_op.md
@@ -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.

diff --git a/include/mxnet/base.h b/include/mxnet/base.h
@@ -36,6 +36,7 @@
 #include "nnvm/tuple.h"
 #include "nnvm/symbolic.h"
 #include "libinfo.h"
+#include "tuple.h"
 
 
 /*!
@@ -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;
 

diff --git a/include/mxnet/executor.h b/include/mxnet/executor.h
@@ -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,
@@ -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,