# 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""" from __future__ import absolute_import import sys 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_EXCEPT = RuntimeError if _FFI_MODE == "cython" else ImportError try: # pylint: disable=wrong-import-position if _FFI_MODE == "ctypes": raise ImportError() if sys.version_info >= (3, 0): from ._cy3.core import _set_class_ndarray, _make_array, _from_dlpack from ._cy3.core import NDArrayBase as _NDArrayBase from ._cy3.core import _reg_extension, _reg_ndarray else: from ._cy2.core import _set_class_ndarray, _make_array, _from_dlpack from ._cy2.core import NDArrayBase as _NDArrayBase from ._cy2.core import _reg_extension, _reg_ndarray except IMPORT_EXCEPT: # pylint: disable=wrong-import-position from ._ctypes.ndarray import _set_class_ndarray, _make_array, _from_dlpack from ._ctypes.ndarray import NDArrayBase as _NDArrayBase from ._ctypes.ndarray import _reg_extension, _reg_ndarray 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): 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) 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) class NDArrayBase(_NDArrayBase): """A simple Device/CPU Array object in runtime.""" @property def shape(self): """Shape of this array""" return tuple(self.handle.contents.shape[i] for i in range(self.handle.contents.ndim)) @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 = TVMType(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.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 = TVMType(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, TVMContext): target = empty(self.shape, self.dtype, target) if isinstance(target, NDArrayBase): check_call(_LIB.TVMArrayCopyFromTo( self.handle, target.handle, None)) else: raise ValueError("Unsupported target type %s" % str(type(target))) return target def free_extension_handle(handle, type_code): """Free c++ extension type handle Parameters ---------- handle : ctypes.c_void_p The handle to the extension type. type_code : int The tyoe code """ check_call(_LIB.TVMExtTypeFree(handle, ctypes.c_int(type_code))) def register_extension(cls, fcreate=None): """Register a extension class to TVM. After the class is registered, the class will be able to directly pass as Function argument generated by TVM. Parameters ---------- cls : class The class object to be registered as extension. fcreate : function, optional The creation function to create a class object given handle value. Note ---- The registered class is requires one property: _tvm_handle. If the registered class is a subclass of NDArray, it is required to have a class attribute _array_type_code. Otherwise, it is required to have a class attribute _tvm_tcode. - ```_tvm_handle``` returns integer represents the address of the handle. - ```_tvm_tcode``` or ```_array_type_code``` gives integer represents type code of the class. Returns ------- cls : class The class being registered. Example ------- The following code registers user defined class MyTensor to be DLTensor compatible. .. code-block:: python @tvm.register_extension class MyTensor(object): _tvm_tcode = tvm.TypeCode.ARRAY_HANDLE def __init__(self): self.handle = _LIB.NewDLTensor() @property def _tvm_handle(self): return self.handle.value """ if issubclass(cls, _NDArrayBase): assert fcreate is not None assert hasattr(cls, "_array_type_code") _reg_ndarray(cls, fcreate) else: assert hasattr(cls, "_tvm_tcode") if fcreate and cls._tvm_tcode < TypeCode.EXT_BEGIN: raise ValueError("Cannot register create when extension tcode is same as buildin") _reg_extension(cls, fcreate) return cls