jroesch commented on a change in pull request #4818: [REFACTOR][PY] Establish
tvm.runtime
URL: https://github.com/apache/incubator-tvm/pull/4818#discussion_r375559169
##########
File path: python/tvm/runtime/ndarray.py
##########
@@ -18,132 +18,36 @@
"""Runtime NDArray api"""
import ctypes
import numpy as np
-from .base import _LIB, check_call, c_array, string_types, _FFI_MODE, c_str
-from .runtime_ctypes import TVMType, TVMContext, TVMArray, TVMArrayHandle
-from .runtime_ctypes import TypeCode, tvm_shape_index_t
+import tvm._ffi
+
+from tvm._ffi.base import _LIB, check_call, c_array, string_types, _FFI_MODE
+from tvm._ffi.runtime_ctypes import DataType, TVMContext, TVMArray,
TVMArrayHandle
+from tvm._ffi.runtime_ctypes import TypeCode, tvm_shape_index_t
try:
# pylint: disable=wrong-import-position
if _FFI_MODE == "ctypes":
raise ImportError()
- from ._cy3.core import _set_class_ndarray, _make_array, _from_dlpack
- from ._cy3.core import NDArrayBase as _NDArrayBase
+ from tvm._ffi._cy3.core import _set_class_ndarray, _make_array,
_from_dlpack
+ from tvm._ffi._cy3.core import NDArrayBase
except (RuntimeError, ImportError):
# pylint: disable=wrong-import-position
- from ._ctypes.ndarray import _set_class_ndarray, _make_array, _from_dlpack
- from ._ctypes.ndarray import NDArrayBase as _NDArrayBase
-
-
-def context(dev_type, dev_id=0):
- """Construct a TVM context with given device type and id.
-
- Parameters
- ----------
- dev_type: int or str
- The device type mask or name of the device.
-
- dev_id : int, optional
- The integer device id
-
- Returns
- -------
- ctx: TVMContext
- The corresponding context.
-
- Examples
- --------
- Context can be used to create reflection of context by
- string representation of the device type.
-
- .. code-block:: python
-
- assert tvm.context("cpu", 1) == tvm.cpu(1)
- assert tvm.context("gpu", 0) == tvm.gpu(0)
- assert tvm.context("cuda", 0) == tvm.gpu(0)
- """
- if isinstance(dev_type, string_types):
- if '-device=micro_dev' in dev_type:
- dev_type = 'micro_dev'
- else:
- dev_type = dev_type.split()[0]
- if dev_type not in TVMContext.STR2MASK:
- raise ValueError("Unknown device type %s" % dev_type)
- dev_type = TVMContext.STR2MASK[dev_type]
- return TVMContext(dev_type, dev_id)
-
-
-def numpyasarray(np_data):
- """Return a TVMArray representation of a numpy array.
- """
- data = np_data
- assert data.flags['C_CONTIGUOUS']
- arr = TVMArray()
- shape = c_array(tvm_shape_index_t, data.shape)
- arr.data = data.ctypes.data_as(ctypes.c_void_p)
- arr.shape = shape
- arr.strides = None
- arr.dtype = TVMType(np.dtype(data.dtype).name)
- arr.ndim = data.ndim
- # CPU device
- arr.ctx = context(1, 0)
- return arr, shape
-
-
-def empty(shape, dtype="float32", ctx=context(1, 0)):
- """Create an empty array given shape and device
-
- Parameters
- ----------
- shape : tuple of int
- The shape of the array
-
- dtype : type or str
- The data type of the array.
-
- ctx : TVMContext
- The context of the array
-
- Returns
- -------
- arr : tvm.nd.NDArray
- The array tvm supported.
- """
- shape = c_array(tvm_shape_index_t, shape)
- ndim = ctypes.c_int(len(shape))
- handle = TVMArrayHandle()
- dtype = TVMType(dtype)
- check_call(_LIB.TVMArrayAlloc(
- shape, ndim,
- ctypes.c_int(dtype.type_code),
- ctypes.c_int(dtype.bits),
- ctypes.c_int(dtype.lanes),
- ctx.device_type,
- ctx.device_id,
- ctypes.byref(handle)))
- return _make_array(handle, False, False)
+ from tvm._ffi._ctypes.ndarray import _set_class_ndarray, _make_array,
_from_dlpack
+ from tvm._ffi._ctypes.ndarray import NDArrayBase
-def from_dlpack(dltensor):
- """Produce an array from a DLPack tensor without memory copy.
- Retreives the underlying DLPack tensor's pointer to create an array from
the
- data. Removes the original DLPack tensor's destructor as now the array is
- responsible for destruction.
+@tvm._ffi.register_object
+class NDArray(NDArrayBase):
+ """Lightweight NDArray class of TVM runtime.
Review comment:
I think the comments here could use some rewording.
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