# 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 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)