Comparison ops implemented using mshadow

CMakeLists.txt

@@ -58,6 +58,11 @@ message(STATUS "CMAKE_HOST_SYSTEM_PROCESSOR ${CMAKE_HOST_SYSTEM_PROCESSOR}")
 message(STATUS "CMAKE_SYSTEM_PROCESSOR ${CMAKE_SYSTEM_PROCESSOR}")
 
 message(STATUS "CMAKE_SYSTEM_NAME ${CMAKE_SYSTEM_NAME}")
+
+if(USE_TVM_OP)
+  add_definitions(-DMXNET_USE_TVM_OP=1)
+endif()
+
 if(USE_CUDA AND NOT USE_OLDCMAKECUDA)
   message(STATUS "CMake version '${CMAKE_VERSION}' using generator '${CMAKE_GENERATOR}'")
   if(
@@ -743,7 +748,6 @@ if(USE_DIST_KVSTORE)
 endif()
 
 if(USE_TVM_OP)
-  add_definitions(-DMXNET_USE_TVM_OP=1)
   list(APPEND mxnet_LINKER_LIBS ${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/libtvm_runtime.so)
   include(cmake/BuildTVM.cmake)
   add_subdirectory("3rdparty/tvm")

python/mxnet/_numpy_op_doc.py

@@ -653,3 +653,54 @@ def _np_trace(a, offset=0, axis1=0, axis2=1, out=None):
     (2, 3)
     """
     pass
+
+
+def _np_squeeze(a, axis=None, out=None):
+    """
+    Remove single-dimensional entries from the shape of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : None or int or tuple of ints, optional
+        Selects a subset of the single-dimensional entries in the
+        shape. If an axis is selected with shape entry greater than
+        one, an error is raised.
+    out : ndarray, optional
+        Array into which the output is placed. It must have the same size
+        and dtype as the input array.
+
+    Returns
+    -------
+    squeezed : ndarray
+        The input array, but with all or a subset of the
+        dimensions of length 1 removed. It always returns a copy of `a`.
+
+    Raises
+    ------
+    MXNetError
+        If `axis` is not `None`, and an axis being squeezed is not of length 1
+
+    See Also
+    --------
+    expand_dims : The inverse operation, adding singleton dimensions
+    reshape : Insert, remove, and combine dimensions, and resize existing ones
+
+    Examples
+    --------
+    >>> x = np.array([[[0], [1], [2]]])
+    >>> x.shape
+    (1, 3, 1)
+    >>> np.squeeze(x).shape
+    (3,)
+    >>> np.squeeze(x, axis=0).shape
+    (3, 1)
+    >>> np.squeeze(x, axis=1).shape
+    Traceback (most recent call last):
+    ...
+    mxnet.base.MXNetError: cannot select an axis to squeeze out which has size=3 not equal to one
+    >>> np.squeeze(x, axis=2).shape
+    (1, 3)
+    """
+    pass

python/mxnet/ndarray/numpy/_op.py

@@ -2110,11 +2110,11 @@ def logical_not(x, out=None, **kwargs):
     --------
     >>> x= np.array([True, False, 0, 1])
     >>> np.logical_not(x)
-    array([0., 1., 1., 0.])
+    array([False,  True,  True, False])
 
     >>> x = np.arange(5)
     >>> np.logical_not(x<3)
-    array([0., 0., 0., 1., 1.])
+    array([False, False, False,  True,  True])
     """
     return _unary_func_helper(x, _npi.logical_not, _np.logical_not, out=out, **kwargs)
 

python/mxnet/numpy/multiarray.py

@@ -302,7 +302,7 @@ def __getitem__(self, key):
             except Exception as err:
                 raise TypeError('{}'.format(str(err)))
         if isinstance(key, _np.ndarray) and key.dtype == _np.bool_:
-            key = array(key, dtype='bool')
+            key = array(key, dtype='bool', ctx=self.ctx)
         if isinstance(key, ndarray) and key.dtype == _np.bool_:  # boolean indexing
             key_shape = key.shape
             key_ndim = len(key_shape)
@@ -364,6 +364,8 @@ def __setitem__(self, key, value):
         """
         if isinstance(value, NDArray) and not isinstance(value, ndarray):
             raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray')
+
+        # handle basic and advanced indexing
         if self.ndim == 0:
             if not isinstance(key, tuple) or len(key) != 0:
                 raise IndexError('scalar tensor can only accept `()` as index')
@@ -753,7 +755,7 @@ def detach(self):
         check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
         return _np_ndarray_cls(hdl)
 
-    def astype(self, dtype, *args, **kwargs):  # pylint: disable=arguments-differ,unused-argument
+    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ,unused-argument
         """
         Copy of the array, cast to a specified type.
 
@@ -1237,7 +1239,14 @@ def tile(self, *args, **kwargs):
 
     def transpose(self, *axes):  # pylint: disable=arguments-differ
         """Permute the dimensions of an array."""
-        return _mx_np_op.transpose(self, axes=axes if len(axes) != 0 else None)
+        if len(axes) == 0:
+            axes = None
+        elif len(axes) == 1:
+            if isinstance(axes[0], (tuple, list)):
+                axes = axes[0]
+            elif axes[0] is None:
+                axes = None
+        return _mx_np_op.transpose(self, axes=axes)
 
     def flip(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`flip`.
@@ -3401,11 +3410,11 @@ def logical_not(x, out=None, **kwargs):
     --------
     >>> x= np.array([True, False, 0, 1])
     >>> np.logical_not(x)
-    array([0., 1., 1., 0.])
+    array([False,  True,  True, False])
 
     >>> x = np.arange(5)
     >>> np.logical_not(x<3)
-    array([0., 0., 0., 1., 1.])
+    array([False, False, False,  True,  True])
     """
     return _mx_nd_np.logical_not(x, out=out, **kwargs)
 

python/mxnet/numpy_extension/__init__.py

@@ -25,7 +25,7 @@
 from . import _register
 from ._op import *  # pylint: disable=wildcard-import
 from ..context import *  # pylint: disable=wildcard-import
-from ..util import is_np_shape, is_np_array, set_np, reset_np
+from ..util import is_np_shape, is_np_array, set_np, reset_np, get_cuda_compute_capability
 from ..ndarray import waitall
 from .utils import *  # pylint: disable=wildcard-import
 from . import random  # pylint: disable=wildcard-import

python/mxnet/symbol/numpy/_symbol.py

@@ -23,7 +23,7 @@
 import numpy as _np
 from . import _op as _mx_np_op
 from ...base import _LIB, SymbolHandle, numeric_types, mx_uint
-from ...util import check_call, set_module
+from ...util import check_call, set_module, _sanity_check_params
 from ...context import current_context
 from ..symbol import Symbol
 from .._internal import _set_np_symbol_class
@@ -181,8 +181,29 @@ def T(self):
         return self.transpose()
     # pylint: enable= invalid-name, undefined-variable
 
-    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ
-        raise NotImplementedError
+    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ,unused-argument
+        """
+        Copy of the array, cast to a specified type.
+
+        Parameters
+        ----------
+        dtype : str or dtype
+            Typecode or data-type to which the array is cast.
+        copy : bool, optional
+            Default `True`. By default, astype always returns a newly
+            allocated ndarray on the same context. If this is set to
+            `False`, and the dtype requested is the same as the ndarray's
+            dtype, the ndarray is returned instead of a copy.
+
+        Returns
+        -------
+        arr_t : ndarray
+            Unless `copy` is False and the other conditions for returning the input
+            array are satisfied (see description for `copy` input parameter), `arr_t`
+            is a new array of the same shape as the input array with `dtype`.
+        """
+        _sanity_check_params('astype', ['order', 'casting', 'subok'], kwargs)
+        return _npi.cast(self, dtype=dtype)
 
     def dot(self, b, out=None):
         """Dot product of two arrays.
@@ -438,7 +459,14 @@ def transpose(self, *axes):  # pylint: disable=arguments-differ
         """The arguments are the same as for :py:func:`transpose`, with
         this array as data.
         """
-        return _mx_np_op.transpose(self, axes=axes if len(axes) != 0 else None)
+        if len(axes) == 0:
+            axes = None
+        elif len(axes) == 1:
+            if isinstance(axes[0], (tuple, list)):
+                axes = axes[0]
+            elif axes[0] is None:
+                axes = None
+        return _mx_np_op.transpose(self, axes=axes)
 
     def flip(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`flip`.

python/mxnet/test_utils.py

@@ -51,6 +51,7 @@
 from .symbol.numpy import _Symbol as np_symbol
 from .util import use_np  # pylint: disable=unused-import
 from .runtime import Features
+from .numpy_extension import get_cuda_compute_capability
 
 
 def default_context():
@@ -2235,10 +2236,34 @@ def has_tvm_ops():
     """Returns True if MXNet is compiled with TVM generated operators. If current ctx
     is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported."""
     built_with_tvm_op = _features.is_enabled("TVM_OP")
-    if current_context().device_type == 'gpu':
+    ctx = current_context()
+    if ctx.device_type == 'gpu':
         try:
-            import tvm
-        except ImportError:
+            cc = get_cuda_compute_capability(ctx)
+        except:  # pylint: disable=bare-except
+            print('Failed to get CUDA compute capability for context {}. The operators '
+                  'built with USE_TVM_OP=1 will not be run in unit tests.'.format(ctx))
             return False
-        return built_with_tvm_op and (int("".join(tvm.nd.gpu(0).compute_version.split('.'))) >= 53)
+        print('Cuda arch compute capability: sm_{}'.format(str(cc)))
+        return built_with_tvm_op and cc >= 53
     return built_with_tvm_op
+
+
+def is_op_runnable():
+    """Returns True for all CPU tests. Returns True for GPU tests that are either of the following.
+    1. Built with USE_TVM_OP=0.
+    2. Built with USE_TVM_OP=1, but with compute capability >= 53."""
+    ctx = current_context()
+    if ctx.device_type == 'gpu':
+        if not _features.is_enabled("TVM_OP"):
+            return True
+        else:
+            try:
+                cc = get_cuda_compute_capability(ctx)
+            except:  # pylint: disable=bare-except
+                print('Failed to get CUDA compute capability for context {}. The operators '
+                      'built with USE_TVM_OP=1 will not be run in unit tests.'.format(ctx))
+                return False
+            print('Cuda arch compute capability: sm_{}'.format(str(cc)))
+            return cc >= 53
+    return True

python/mxnet/util.py

@@ -602,3 +602,64 @@ def set_np(shape=True, array=True):
 def reset_np():
     """Deactivate NumPy shape and array semantics at the same time."""
     set_np(shape=False, array=False)
+
+
+_CUDA_SUCCESS = 0
+
+
+def get_cuda_compute_capability(ctx):
+    """Returns the cuda compute capability of the input `ctx`.
+
+    Parameters
+    ----------
+    ctx : Context
+        GPU context whose corresponding cuda compute capability is to be retrieved.
+
+    Returns
+    -------
+    cuda_compute_capability : int
+        CUDA compute capability. For example, it returns 70 for CUDA arch equal to `sm_70`.
+
+    References
+    ----------
+    https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549#file-cuda_check-py
+    """
+    if ctx.device_type != 'gpu':
+        raise ValueError('Expecting a gpu context to get cuda compute capability, '
+                         'while received ctx {}'.format(str(ctx)))
+
+    libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')
+    for libname in libnames:
+        try:
+            cuda = ctypes.CDLL(libname)
+        except OSError:
+            continue
+        else:
+            break
+    else:
+        raise OSError("could not load any of: " + ' '.join(libnames))
+
+    # Some constants taken from cuda.h
+
+    cc_major = ctypes.c_int()
+    cc_minor = ctypes.c_int()
+    device = ctypes.c_int()
+    error_str = ctypes.c_char_p()
+
+    ret = cuda.cuInit(0)
+    if ret != _CUDA_SUCCESS:
+        cuda.cuGetErrorString(ret, ctypes.byref(error_str))
+        raise RuntimeError('cuInit failed with erro code {}: {}'
+                           .format(ret, error_str.value.decode()))
+
+    ret = cuda.cuDeviceGet(ctypes.byref(device), ctx.device_id)
+    if ret != _CUDA_SUCCESS:
+        cuda.cuGetErrorString(ret, ctypes.byref(error_str))
+        raise RuntimeError('cuDeviceGet failed with error code {}: {}'
+                           .format(ret, error_str.value.decode()))
+    ret = cuda.cuDeviceComputeCapability(ctypes.byref(cc_major), ctypes.byref(cc_minor), device)
+    if ret != _CUDA_SUCCESS:
+        cuda.cuGetErrorString(ret, ctypes.byref(error_str))
+        raise RuntimeError('cuDeviceComputeCapability failed with error code {}: {}'
+                           .format(ret, error_str.value.decode()))
+    return cc_major.value * 10 + cc_minor.value

src/common/utils.h

@@ -823,6 +823,21 @@ static inline std::string GetOutputName(const nnvm::NodeEntry& e) {
   return sym.ListOutputNames()[0];
 }
 
+inline mxnet::TShape CanonicalizeAxes(const mxnet::TShape& src) {
+  // convert negative axes to positive values
+  const int ndim = src.ndim();
+  mxnet::TShape axes = src;
+  for (int i = 0; i < ndim; ++i) {
+    if (axes[i] < 0) {
+      axes[i] += ndim;
+    }
+    CHECK(axes[i] >= 0 && axes[i] < ndim) << "axes[" << i << "]="
+                                          << axes[i] << " exceeds the range ["
+                                          << 0 << ", " << ndim << ")";
+  }
+  return axes;
+}
+
 }  // namespace common
 }  // namespace mxnet
 #endif  // MXNET_COMMON_UTILS_H_

src/ndarray/ndarray_function.cc

@@ -36,23 +36,14 @@ template<>
 void Copy<cpu, cpu>(const TBlob &from, TBlob *to,
                     Context from_ctx, Context to_ctx,
                     RunContext ctx) {
-  if (from.type_flag_ == mshadow::kBool || to->type_flag_ == mshadow::kBool) {
-    CHECK_EQ(from.type_flag_, to->type_flag_) << "Only supports copying data between"
-                                                 " two boolean tensors.";
-    const index_t size = from.Size();
-    CHECK_EQ(size, to->Size()) << "copying size mismatch, from: " << size * sizeof(bool)
-                               << " bytes, to: " << to->Size() * sizeof(bool) << " bytes.";
-    common::ParallelCopy(to->dptr<bool>(), from.dptr<bool>(), size);
-    return;
-  }
-  MSHADOW_TYPE_SWITCH(to->type_flag_, DType, {
+  MSHADOW_TYPE_SWITCH_WITH_BOOL(to->type_flag_, DType, {
     if (to->type_flag_ == from.type_flag_) {
       const index_t size = static_cast<index_t>(from.Size());
       CHECK_EQ(size, to->Size()) << "copying size mismatch, from: " << size * sizeof(DType)
                << " bytes, to: " << to->Size() * sizeof(DType) << " bytes.";
       common::ParallelCopy(to->dptr<DType>(), from.dptr<DType>(), size);
     } else {
-      MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
+      MSHADOW_TYPE_SWITCH_WITH_BOOL(from.type_flag_, SrcDType, {
           to->FlatTo1D<cpu, DType>() =
               mshadow::expr::tcast<DType>(from.FlatTo1D<cpu, SrcDType>());
       })

src/ndarray/ndarray_function.cu

@@ -76,10 +76,6 @@ void Copy<gpu, gpu>(const TBlob &from, TBlob *to,
                       from.FlatTo1D<gpu, DType>(s),
                       s);
       } else {
-        CHECK_NE(from.type_flag_, mshadow::kBool)
-            << "Copying boolean ndarray across devices is not supported";
-        CHECK_NE(to->type_flag_, mshadow::kBool)
-            << "Copying boolean ndarray across devices is not supported";
         MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
           to->FlatTo1D<gpu, DType>(s) =
             mshadow::expr::tcast<DType>(from.FlatTo1D<gpu, SrcDType>(s));

src/operator/leaky_relu-inl.h

@@ -134,7 +134,7 @@ class LeakyReLUOp : public Operator {
             mshadow::Shape<NDim> oshape = new_oshape.get<NDim>();
             mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>());
             mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>());
-            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType,
+            mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, DType, DType,
                                                                mshadow_op::xelu>, xpu>::
             template LaunchEx(s, new_oshape.Size(), req[leakyrelu::kOut], lstride, rstride, oshape,
             in_data[leakyrelu::kData].dptr<DType>(), in_data[leakyrelu::kGamma].dptr<DType>(),