[OP] Topk and arange + Update submodules (apache#4565)

* initial add topk arange

fix arange

Put back ones and zeros

fix

fix default ctx

* fix lint

* Update Submodules

* Update MShadow

* fix warning

* fix pylint

* style fix

python/mxnet/ndarray.py

@@ -1049,9 +1049,11 @@ def zeros(shape, ctx=None, dtype=mx_real_t):
     out: Array
         The created NDArray.
     """
-    arr = empty(shape, ctx, dtype)
-    arr[:] = 0.0
-    return arr
+    if ctx is None:
+        ctx = Context.default_ctx
+    # pylint: disable= no-member, protected-access
+    return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype)
+    # pylint: enable= no-member, protected-access
 
 def ones(shape, ctx=None, dtype=mx_real_t):
     """Create a new NDArray filled with 1, with specified shape.
@@ -1068,9 +1070,11 @@ def ones(shape, ctx=None, dtype=mx_real_t):
     out: Array
         The created NDArray.
     """
-    arr = empty(shape, ctx, dtype)
-    arr[:] = 1.0
-    return arr
+    if ctx is None:
+        ctx = Context.default_ctx
+    # pylint: disable= no-member, protected-access
+    return _internal._ones(shape=shape, ctx=ctx, dtype=dtype)
+    # pylint: enable= no-member, protected-access
 
 def full(shape, val, ctx=None, dtype=mx_real_t):
     """Create a new NDArray filled with given value, with specified shape.
@@ -1174,6 +1178,39 @@ def concatenate(arrays, axis=0, always_copy=True):
 
     return ret
 
+# pylint: disable= no-member, protected-access, too-many-arguments
+def arange(start, stop=None, step=1.0, repeat=1, ctx=None, dtype=mx_real_t):
+    """Simlar function in the MXNet ndarray as numpy.arange
+        See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html.
+
+    Parameters
+    ----------
+    start : number, optional
+        Start of interval. The interval includes this value. The default start value is 0.
+    stop : number, optional
+        End of interval. The interval does not include this value.
+    step : number, optional
+        Spacing between values
+    repeat : number, optional
+        "The repeating time of all elements.
+        E.g repeat=3, the element a will be repeated three times --> a, a, a.
+    ctx : Context, optional
+        The context of the NDArray, default to current default context.
+    dtype : type, optional
+        The value type of the NDArray, default to np.float32
+
+    Returns
+    -------
+    out : NDArray
+        The created NDArray
+    """
+    if ctx is None:
+        ctx = Context.default_ctx
+    return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
+                             dtype=dtype, ctx=str(ctx))
+# pylint: enable= no-member, protected-access, too-many-arguments
+
+
 def load(fname):
     """Load ndarray from binary file.
 

python/mxnet/symbol.py

@@ -1186,3 +1186,68 @@ def hypot(left, right):
         return _numpy.hypot(left, right)
     else:
         raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right))))
+
+
+def zeros(shape, dtype=_numpy.float32):
+    """Create a Tensor filled with zeros, similar to numpy.zeros
+        See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html.
+
+    Parameters
+    ----------
+    shape :  int or sequence of ints
+        Shape of the new array.
+    dtype : type, optional
+        The value type of the NDArray, default to np.float32
+
+    Returns
+    -------
+    out : Symbol
+        The created Symbol
+    """
+    return _internal._zeros(shape=shape, dtype=dtype)
+
+
+def ones(shape, dtype=_numpy.float32):
+    """Create a Tensor filled with ones, similar to numpy.ones
+        See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.ones.html.
+
+    Parameters
+    ----------
+    shape :  int or sequence of ints
+        Shape of the new array.
+    dtype : type, optional
+        The value type of the NDArray, default to np.float32
+
+    Returns
+    -------
+    out : Symbol
+        The created Symbol
+    """
+    return _internal._ones(shape=shape, dtype=dtype)
+
+
+def arange(start, stop=None, step=1.0, repeat=1, name=None, dtype=_numpy.float32):
+    """Simlar function in the MXNet ndarray as numpy.arange
+        See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html.
+
+    Parameters
+    ----------
+    start : number
+        Start of interval. The interval includes this value. The default start value is 0.
+    stop : number, optional
+        End of interval. The interval does not include this value.
+    step : number, optional
+        Spacing between values
+    repeat : int, optional
+        "The repeating time of all elements.
+        E.g repeat=3, the element a will be repeated three times --> a, a, a.
+    dtype : type, optional
+        The value type of the NDArray, default to np.float32
+
+    Returns
+    -------
+    out : Symbol
+        The created Symbol
+    """
+    return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
+                             name=name, dtype=dtype)

src/operator/tensor/broadcast_reduce_op_index.cc

@@ -9,11 +9,19 @@ namespace mxnet {
 namespace op {
 MXNET_OPERATOR_REGISTER_REDUCE_AXIS(argmax)
 .MXNET_DESCRIBE("Compute argmax")
-.set_attr<FCompute>("FCompute<cpu>", SearchAxisCompute<cpu, mshadow::red::maximum>);
+.set_attr<FCompute>("FCompute<cpu>", SearchAxisCompute<cpu, mshadow::red::maximum>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    return MakeGradNode("_zeros", n, {}, {});
+});
 
 MXNET_OPERATOR_REGISTER_REDUCE_AXIS(argmin)
 .MXNET_DESCRIBE("Compute argmin")
-.set_attr<FCompute>("FCompute<cpu>", SearchAxisCompute<cpu, mshadow::red::minimum>);
+.set_attr<FCompute>("FCompute<cpu>", SearchAxisCompute<cpu, mshadow::red::minimum>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    return MakeGradNode("_zeros", n, {}, {});
+});
 
 // Legacy support
 NNVM_REGISTER_OP(argmax_channel)

src/operator/tensor/elemwise_unary_op.cc

@@ -26,6 +26,15 @@ NNVM_REGISTER_OP(_backward_copy)
   })
 .set_attr<FCompute>("FCompute<cpu>", IdentityCompute<cpu>);
 
+MXNET_OPERATOR_REGISTER_UNARY(BlockGrad)
+.MXNET_DESCRIBE("Get output from a symbol and pass 0 gradient back")
+.set_attr<FCompute>("FCompute<cpu>", IdentityCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient",
+  [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+    // pass back zero gradient
+    return MakeGradNode("_zeros", n, {}, {});
+});
+
 // identity output as first input, but attributes are constrainted to be like rhs
 NNVM_REGISTER_OP(_identity_with_attr_like_rhs)
 .set_num_inputs(2)

src/operator/tensor/elemwise_unary_op.cu

@@ -15,6 +15,9 @@ NNVM_REGISTER_OP(_copy)
 NNVM_REGISTER_OP(_backward_copy)
 .set_attr<FCompute>("FCompute<gpu>", IdentityCompute<gpu>);
 
+NNVM_REGISTER_OP(BlockGrad)
+.set_attr<FCompute>("FCompute<gpu>", IdentityCompute<gpu>);
+
 // identity output as first input, but attributes are constrainted to be like rhs
 NNVM_REGISTER_OP(_identity_with_attr_like_rhs)
 .set_attr<FCompute>("FCompute<gpu>", IdentityCompute<gpu>);