Tianqi Chen
3cc4971990
The _type_child_slots can be used to enable quick type checking optimization by checking the whether the type index is within the bound. This PR enables these static slots: - Introduce a static assert to avoid the scenario when a developer forget to _type_child_slots when the field is set for the type's parent. - Revamp and assign static type index to common runtime objects - Add a DumpTypeTable call to allow developer monitor the current situation of type table and offers suggestions for the slots(ideally the slots equals the number of children so there is no overflow.
507 lines
13 KiB
Python
507 lines
13 KiB
Python
# 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.
|
|
# pylint: disable=invalid-name, unused-import
|
|
"""Runtime NDArray API"""
|
|
import ctypes
|
|
import numpy as np
|
|
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 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 tvm._ffi._ctypes.ndarray import _set_class_ndarray, _make_array, _from_dlpack
|
|
from tvm._ffi._ctypes.ndarray import NDArrayBase
|
|
|
|
|
|
@tvm._ffi.register_object("runtime.NDArray")
|
|
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.
|
|
"""
|
|
|
|
@property
|
|
def dtype(self):
|
|
"""Type of this array"""
|
|
return str(self.handle.contents.dtype)
|
|
|
|
@property
|
|
def ctx(self):
|
|
"""context of this array"""
|
|
return self.handle.contents.ctx
|
|
|
|
@property
|
|
def context(self):
|
|
"""context of this array"""
|
|
return self.ctx
|
|
|
|
def __hash__(self):
|
|
return ctypes.cast(self.handle, ctypes.c_void_p).value
|
|
|
|
def __eq__(self, other):
|
|
return self.same_as(other)
|
|
|
|
def __ne__(self, other):
|
|
return not self.__eq__(other)
|
|
|
|
def same_as(self, other):
|
|
"""Check object identity equality
|
|
|
|
Parameters
|
|
----------
|
|
other : object
|
|
The other object to compare to
|
|
|
|
Returns
|
|
-------
|
|
same : bool
|
|
Whether other is same as self.
|
|
"""
|
|
if not isinstance(other, NDArrayBase):
|
|
return False
|
|
return self.__hash__() == other.__hash__()
|
|
|
|
def __setitem__(self, in_slice, value):
|
|
"""Set ndarray value"""
|
|
if (not isinstance(in_slice, slice) or
|
|
in_slice.start is not None
|
|
or in_slice.stop is not None):
|
|
raise ValueError('Array only support set from numpy array')
|
|
if isinstance(value, NDArrayBase):
|
|
if value.handle is not self.handle:
|
|
value.copyto(self)
|
|
elif isinstance(value, (np.ndarray, np.generic)):
|
|
self.copyfrom(value)
|
|
else:
|
|
raise TypeError('type %s not supported' % str(type(value)))
|
|
|
|
def copyfrom(self, source_array):
|
|
"""Peform an synchronize copy from the array.
|
|
|
|
Parameters
|
|
----------
|
|
source_array : array_like
|
|
The data source we should like to copy from.
|
|
|
|
Returns
|
|
-------
|
|
arr : NDArray
|
|
Reference to self.
|
|
"""
|
|
if isinstance(source_array, NDArrayBase):
|
|
source_array.copyto(self)
|
|
return self
|
|
|
|
if not isinstance(source_array, np.ndarray):
|
|
try:
|
|
source_array = np.array(source_array, dtype=self.dtype)
|
|
except:
|
|
raise TypeError('array must be an array_like data,' +
|
|
'type %s is not supported' % str(type(source_array)))
|
|
|
|
t = DataType(self.dtype)
|
|
shape, dtype = self.shape, self.dtype
|
|
if t.lanes > 1:
|
|
shape = shape + (t.lanes,)
|
|
t.lanes = 1
|
|
dtype = str(t)
|
|
|
|
if source_array.shape != shape:
|
|
raise ValueError("array shape do not match the shape of NDArray {0} vs {1}".format(
|
|
source_array.shape, shape))
|
|
source_array = np.ascontiguousarray(source_array, dtype=dtype)
|
|
assert source_array.flags['C_CONTIGUOUS']
|
|
data = source_array.ctypes.data_as(ctypes.c_void_p)
|
|
nbytes = ctypes.c_size_t(source_array.size * source_array.dtype.itemsize)
|
|
check_call(_LIB.TVMArrayCopyFromBytes(self.handle, data, nbytes))
|
|
return self
|
|
|
|
def __repr__(self):
|
|
res = "<tvm.nd.NDArray shape={0}, {1}>\n".format(self.shape, self.context)
|
|
res += self.asnumpy().__repr__()
|
|
return res
|
|
|
|
def __str__(self):
|
|
return str(self.asnumpy())
|
|
|
|
def asnumpy(self):
|
|
"""Convert this array to numpy array
|
|
|
|
Returns
|
|
-------
|
|
np_arr : numpy.ndarray
|
|
The corresponding numpy array.
|
|
"""
|
|
t = DataType(self.dtype)
|
|
shape, dtype = self.shape, self.dtype
|
|
if t.lanes > 1:
|
|
shape = shape + (t.lanes,)
|
|
t.lanes = 1
|
|
dtype = str(t)
|
|
np_arr = np.empty(shape, dtype=dtype)
|
|
assert np_arr.flags['C_CONTIGUOUS']
|
|
data = np_arr.ctypes.data_as(ctypes.c_void_p)
|
|
nbytes = ctypes.c_size_t(np_arr.size * np_arr.dtype.itemsize)
|
|
check_call(_LIB.TVMArrayCopyToBytes(self.handle, data, nbytes))
|
|
return np_arr
|
|
|
|
def copyto(self, target):
|
|
"""Copy array to target
|
|
|
|
Parameters
|
|
----------
|
|
target : NDArray
|
|
The target array to be copied, must have same shape as this array.
|
|
"""
|
|
if isinstance(target, NDArrayBase):
|
|
return self._copyto(target)
|
|
if isinstance(target, TVMContext):
|
|
res = empty(self.shape, self.dtype, target)
|
|
return self._copyto(res)
|
|
raise ValueError("Unsupported target type %s" % str(type(target)))
|
|
|
|
|
|
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: tvm.runtime.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 = DataType(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 = DataType(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)
|
|
|
|
|
|
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.
|
|
|
|
Parameters
|
|
----------
|
|
dltensor : DLPack tensor
|
|
Input DLManagedTensor, can only be consumed once.
|
|
|
|
Returns
|
|
-------
|
|
arr: tvm.nd.NDArray
|
|
The array view of the tensor data.
|
|
"""
|
|
return _from_dlpack(dltensor)
|
|
|
|
|
|
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 GPU 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)
|
|
|
|
|
|
def hexagon(dev_id=0):
|
|
"""Construct a Hexagon device
|
|
|
|
Parameters
|
|
----------
|
|
dev_id : int, optional
|
|
The integer device id
|
|
|
|
Returns
|
|
-------
|
|
ctx : TVMContext
|
|
The created context
|
|
"""
|
|
return TVMContext(14, 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)
|
|
|
|
# Register back to FFI
|
|
_set_class_ndarray(NDArray)
|