longformer / tvm /ndarray.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""TVM Runtime NDArray API.
tvm.ndarray provides a minimum runtime array API to test
the correctness of the program.
"""
# pylint: disable=invalid-name,unused-import
from __future__ import absolute_import as _abs
import numpy as _np
from ._ffi.ndarray import TVMContext, TVMType, NDArrayBase
from ._ffi.ndarray import context, empty, from_dlpack
from ._ffi.ndarray import _set_class_ndarray
from ._ffi.ndarray import register_extension, free_extension_handle
class NDArray(NDArrayBase):
"""Lightweight NDArray class of TVM runtime.
Strictly this is only an Array Container (a buffer object)
No arthimetic operations are defined.
All operations are performed by TVM functions.
The goal is not to re-build yet another array library.
Instead, this is a minimal data structure to demonstrate
how can we use TVM in existing project which might have their own array containers.
"""
def cpu(dev_id=0):
"""Construct a CPU device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(1, dev_id)
def gpu(dev_id=0):
"""Construct a CPU device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(2, dev_id)
def rocm(dev_id=0):
"""Construct a ROCM device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(10, dev_id)
def opencl(dev_id=0):
"""Construct a OpenCL device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(4, dev_id)
def metal(dev_id=0):
"""Construct a metal device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(8, dev_id)
def vpi(dev_id=0):
"""Construct a VPI simulated device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(9, dev_id)
def vulkan(dev_id=0):
"""Construct a Vulkan device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(7, dev_id)
def opengl(dev_id=0):
"""Construct a OpenGL device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(11, dev_id)
def ext_dev(dev_id=0):
"""Construct a extension device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
Note
----
This API is reserved for quick testing of new
device by plugin device API as ext_dev.
"""
return TVMContext(12, dev_id)
def micro_dev(dev_id=0):
"""Construct a micro device
Parameters
----------
dev_id : int, optional
The integer device id
Returns
-------
ctx : TVMContext
The created context
"""
return TVMContext(13, dev_id)
cl = opencl
mtl = metal
def array(arr, ctx=cpu(0)):
"""Create an array from source arr.
Parameters
----------
arr : numpy.ndarray
The array to be copied from
ctx : TVMContext, optional
The device context to create the array
Returns
-------
ret : NDArray
The created array
"""
if not isinstance(arr, (_np.ndarray, NDArray)):
arr = _np.array(arr)
return empty(arr.shape, arr.dtype, ctx).copyfrom(arr)
_set_class_ndarray(NDArray)