megenginelite.tensor 源代码

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an

from ctypes import *

import numpy as np

from .base import _Ctensor, _lib, _LiteCObjBase
from .struct import LiteDataType, LiteDeviceType, LiteIOType, Structure


_lite_type_to_nptypes = {
    LiteDataType.LITE_INT: np.int32,
    LiteDataType.LITE_FLOAT: np.float32,
    LiteDataType.LITE_UINT8: np.uint8,
    LiteDataType.LITE_INT8: np.int8,
    LiteDataType.LITE_INT16: np.int16,
    LiteDataType.LITE_UINT16: np.uint16,
    LiteDataType.LITE_HALF: np.float16,

_nptype_to_lite_type = {val: key for key, val in _lite_type_to_nptypes.items()}

_str_nptypes_to_lite_nptypes = {
    np.dtype("int32"): LiteDataType.LITE_INT,
    np.dtype("float32"): LiteDataType.LITE_FLOAT,
    np.dtype("uint8"): LiteDataType.LITE_UINT8,
    np.dtype("int8"): LiteDataType.LITE_INT8,
    np.dtype("int16"): LiteDataType.LITE_INT16,
    np.dtype("uint16"): LiteDataType.LITE_UINT16,
    np.dtype("float16"): LiteDataType.LITE_HALF,

ctype_to_lite_dtypes = {
    c_int: LiteDataType.LITE_INT,
    c_uint: LiteDataType.LITE_INT,
    c_float: LiteDataType.LITE_FLOAT,
    c_ubyte: LiteDataType.LITE_UINT8,
    c_byte: LiteDataType.LITE_INT8,
    c_short: LiteDataType.LITE_INT16,
    c_ushort: LiteDataType.LITE_UINT16,

_lite_dtypes_to_ctype = {
    LiteDataType.LITE_INT: c_int,
    LiteDataType.LITE_FLOAT: c_float,
    LiteDataType.LITE_UINT8: c_ubyte,
    LiteDataType.LITE_INT8: c_byte,
    LiteDataType.LITE_INT16: c_short,
    LiteDataType.LITE_UINT16: c_ushort,

[文档]class LiteLayout(Structure): """ the simple layout description """ _fields_ = [ ("_shapes", c_size_t * MAX_DIM), ("ndim", c_size_t), ("data_type", c_int), ] def __init__(self, shape=None, dtype=None): if shape: shape = list(shape) assert len(shape) <= MAX_DIM, "Layout max dim is 7." self._shapes = (c_size_t * MAX_DIM)(*shape) self.ndim = len(shape) else: self._shapes = (c_size_t * MAX_DIM)() self.ndim = 0 if not dtype: self.data_type = LiteDataType.LITE_FLOAT elif isinstance(dtype, LiteDataType): self.data_type = dtype elif type(dtype) == str: self.data_type = _str_nptypes_to_lite_nptypes[np.dtype(dtype)] elif isinstance(dtype, np.dtype): ctype = np.ctypeslib.as_ctypes_type(dtype) self.data_type = ctype_to_lite_dtypes[ctype] elif isinstance(dtype, type): self.data_type = _nptype_to_lite_type[dtype] else: raise RuntimeError("unkonw data type") @property def dtype(self): return _lite_type_to_nptypes[LiteDataType(self.data_type)] @property def shapes(self): return list(self._shapes)[0 : self.ndim] @shapes.setter def shapes(self, shape): shape = list(shape) assert len(shape) <= MAX_DIM, "Layout max dim is 7." self._shapes = (c_size_t * MAX_DIM)(*shape) self.ndim = len(shape) def __repr__(self): data = { "shapes": self.shapes, "ndim": self.ndim, "data_type": _lite_type_to_nptypes[LiteDataType(self.data_type)], } return data.__repr__()
class _LiteTensorDesc(Structure): """ warpper of the MegEngine Tensor :is_pinned_host: when set, the storage memory of the tensor is pinned memory, this is used to Optimize the H2D or D2H memory copy, if the device or layout is not set, when copy form other device(CUDA) tensor, this tensor will be automatically set to pinned tensor """ _fields_ = [ ("is_pinned_host", c_int), ("layout", LiteLayout), ("device_type", c_int), ("device_id", c_int), ] def __init__(self): self.layout = LiteLayout() self.device_type = LiteDeviceType.LITE_CPU self.is_pinned_host = False self.device_id = 0 def __repr__(self): data = { "is_pinned_host": self.is_pinned_host, "layout": LiteLayout(self.layout), "device_type": LiteDeviceType(self.device_type.value), "device_id": self.device_id, } return data.__repr__() class _TensorAPI(_LiteCObjBase): """ get the api from the lib """ _api_ = [ ("LITE_make_tensor", [_LiteTensorDesc, POINTER(_Ctensor)]), ("LITE_set_tensor_layout", [_Ctensor, LiteLayout]), ("LITE_reset_tensor_memory", [_Ctensor, c_void_p, c_size_t]), ("LITE_reset_tensor", [_Ctensor, LiteLayout, c_void_p]), ("LITE_tensor_reshape", [_Ctensor, POINTER(c_int), c_int]), ( "LITE_tensor_slice", [ _Ctensor, POINTER(c_size_t), POINTER(c_size_t), POINTER(c_size_t), c_size_t, POINTER(_Ctensor), ], ), ( "LITE_tensor_concat", [POINTER(_Ctensor), c_int, c_int, c_int, c_int, POINTER(_Ctensor),], ), ("LITE_tensor_fill_zero", [_Ctensor]), ("LITE_tensor_copy", [_Ctensor, _Ctensor]), ("LITE_tensor_share_memory_with", [_Ctensor, _Ctensor]), ("LITE_get_tensor_memory", [_Ctensor, POINTER(c_void_p)]), ("LITE_get_tensor_total_size_in_byte", [_Ctensor, POINTER(c_size_t)]), ("LITE_get_tensor_layout", [_Ctensor, POINTER(LiteLayout)]), ("LITE_get_tensor_device_type", [_Ctensor, POINTER(c_int)]), ("LITE_get_tensor_device_id", [_Ctensor, POINTER(c_int)]), ("LITE_destroy_tensor", [_Ctensor]), ("LITE_is_pinned_host", [_Ctensor, POINTER(c_int)]), ]
[文档]class LiteTensor(object): """ the tensor to hold a block of data """ _api = _TensorAPI()._lib def __init__( self, layout=None, device_type=LiteDeviceType.LITE_CPU, device_id=0, is_pinned_host=False, shapes=None, dtype=None, ): """ create a Tensor with layout, device, is_pinned_host or shapes, dtype, device_type, device_id, is_pinned_host param """ self._tensor = _Ctensor() self._layout = LiteLayout() if layout is not None: self._layout = layout elif shapes is not None: shapes = list(shapes) self._layout = LiteLayout(shapes, dtype) self._device_type = device_type self._device_id = device_id self._is_pinned_host = is_pinned_host tensor_desc = _LiteTensorDesc() tensor_desc.layout = self._layout tensor_desc.device_type = device_type tensor_desc.device_id = device_id tensor_desc.is_pinned_host = is_pinned_host self._api.LITE_make_tensor(tensor_desc, byref(self._tensor)) self.update() def __del__(self): self._api.LITE_destroy_tensor(self._tensor)
[文档] def fill_zero(self): """ fill the buffer memory with zero """ self._api.LITE_tensor_fill_zero(self._tensor) self.update()
[文档] def share_memory_with(self, src_tensor): """ share the same memory with the src_tensor, the self memory will be freed """ assert isinstance(src_tensor, LiteTensor) self._api.LITE_tensor_share_memory_with(self._tensor, src_tensor._tensor) self.update()
@property def layout(self): self._api.LITE_get_tensor_layout(self._tensor, byref(self._layout)) return self._layout @layout.setter def layout(self, layout): if isinstance(layout, LiteLayout): self._layout = layout elif isinstance(layout, list): self._layout.shapes = layout self._api.LITE_set_tensor_layout(self._tensor, self._layout) @property def is_pinned_host(self): """ whether the tensor is pinned tensor """ pinned = c_int() self._api.LITE_is_pinned_host(self._tensor, byref(pinned)) self._is_pinned_host = pinned return bool(self._is_pinned_host) @property def device_type(self): """ get device of the tensor """ device_type = c_int() self._api.LITE_get_tensor_device_type(self._tensor, byref(device_type)) self._device_type = device_type return LiteDeviceType(device_type.value) @property def device_id(self): """ get device id of the tensor """ device_id = c_int() self._api.LITE_get_tensor_device_id(self._tensor, byref(device_id)) self._device_id = device_id.value return device_id.value @property def is_continue(self): """ whether the tensor memory is continue """ is_continue = c_int() self._api.LITE_is_memory_continue(self._tensor, byref(is_continue)) return bool(is_continue.value) @property def nbytes(self): """ get the length of the meomry in byte """ length = c_size_t() self._api.LITE_get_tensor_total_size_in_byte(self._tensor, byref(length)) return length.value
[文档] def update(self): """ update the member from C, this will auto used after slice, share """ pinned = c_int() self._api.LITE_is_pinned_host(self._tensor, byref(pinned)) self._is_pinned_host = pinned device_type = c_int() self._api.LITE_get_tensor_device_type(self._tensor, byref(device_type)) self._device_type = device_type self._api.LITE_get_tensor_layout(self._tensor, byref(self._layout)) c_types = _lite_dtypes_to_ctype[self._layout.data_type] self.np_array_type = np.ctypeslib._ctype_ndarray( c_types, list(self._layout.shapes)[0 : self._layout.ndim] )
[文档] def copy_from(self, src_tensor): """ copy memory form the src_tensor """ assert isinstance(src_tensor, LiteTensor) self._api.LITE_tensor_copy(self._tensor, src_tensor._tensor) self.update()
[文档] def reshape(self, shape): """ reshape the tensor with data not change, only change the shape :param shape: int arrary of dst_shape """ shape = list(shape) length = len(shape) c_shape = (c_int * length)(*shape) self._api.LITE_tensor_reshape(self._tensor, c_shape, length) self.update()
[文档] def slice(self, start, end, step=None): """ slice the tensor with gaven start, end, step :param start: silce begin index of each dim :param end: silce end index of each dim :param step: silce step of each dim """ start = list(start) end = list(end) length = len(start) assert length == len(end), "slice with different length of start and end." if step: assert length == len(step), "slice with different length of start and step." step = list(step) else: step = [1 for i in range(length)] c_start = (c_size_t * length)(*start) c_end = (c_size_t * length)(*end) c_step = (c_size_t * length)(*step) slice_tensor = LiteTensor() self._api.LITE_tensor_slice( self._tensor, c_start, c_end, c_step, length, byref(slice_tensor._tensor) ) slice_tensor.update() return slice_tensor
[文档] def get_ctypes_memory(self): """ get the memory of the tensor, return c_void_p of the tensor memory """ mem = c_void_p() self._api.LITE_get_tensor_memory(self._tensor, byref(mem)) return mem
[文档] def set_data_by_share(self, data, length=0, layout=None): """ share the data to the tensor param data: the data will shared to the tensor, it should be a numpy.ndarray or ctypes data """ if isinstance(data, np.ndarray): assert ( self.is_continue ), "set_data_by_share can only apply in continue tensor." assert ( self.is_pinned_host or self.device_type == LiteDeviceType.LITE_CPU ), "set_data_by_share can only apply in cpu tensor or pinned tensor." c_type = _lite_dtypes_to_ctype[LiteDataType(self._layout.data_type)] if self.nbytes != data.nbytes: self.layout = LiteLayout(data.shape, ctype_to_lite_dtypes[c_type]) self._shared_data = data data = data.ctypes.data_as(POINTER(c_type)) if layout is not None: self.layout = layout else: assert length == 0 or length == self.nbytes, "the data length is not match." self._api.LITE_reset_tensor_memory(self._tensor, data, self.nbytes)
[文档] def set_data_by_copy(self, data, data_length=0, layout=None): """ copy the data to the tensor param data: the data to copy to tensor, it should be list, numpy.ndarraya or ctypes with length """ if layout is not None: self.layout = layout assert self.is_continue, "set_data_by_copy can only apply in continue tensor." assert ( self.is_pinned_host or self.device_type == LiteDeviceType.LITE_CPU ), "set_data_by_copy can only apply in cpu tensor or pinned tensor." c_type = _lite_dtypes_to_ctype[LiteDataType(self._layout.data_type)] tensor_memory = c_void_p() if type(data) == list: length = len(data) self._api.LITE_get_tensor_memory(self._tensor, byref(tensor_memory)) tensor_length = self.nbytes assert ( length * sizeof(c_type) <= tensor_length ), "the length of input data to set to the tensor is too large." arr = (c_type * length)(*data) memmove(tensor_memory, arr, sizeof(c_type) * length) elif type(data) == np.ndarray: if self.nbytes != data.nbytes: self.layout = LiteLayout(data.shape, data.dtype) arr = data.ctypes.data_as(POINTER(c_type)) self._api.LITE_get_tensor_memory(self._tensor, byref(tensor_memory)) assert self.nbytes == data.nbytes memmove(tensor_memory, arr, self.nbytes) else: assert ( data_length == self.nbytes or layout is not None ), "when input data is ctypes, the length of input data or layout must set" self._api.LITE_get_tensor_memory(self._tensor, byref(tensor_memory)) memmove(tensor_memory, data, data_length)
[文档] def get_data_by_share(self): """ get the data in the tensor, add share the data with a new numpy, and return the numpy arrray, be careful, the data in numpy is valid before the tensor memory is write again, such as LiteNetwok forward next time. """ buffer = c_void_p() self._api.LITE_get_tensor_memory(self._tensor, byref(buffer)) buffer = self.np_array_type.from_address(buffer.value) return np.ctypeslib.as_array(buffer)
[文档] def to_numpy(self): """ get the buffer of the tensor """ self.update() if self.nbytes <= 0: np_type = _lite_type_to_nptypes[LiteDataType(self._layout.data_type)] return np.array([], dtype=np_type) if self.is_continue and ( self.is_pinned_host or self.device_type == LiteDeviceType.LITE_CPU ): ptr = c_void_p() self._api.LITE_get_tensor_memory(self._tensor, byref(ptr)) np_type = _lite_type_to_nptypes[LiteDataType(self._layout.data_type)] shape = [self._layout.shapes[i] for i in range(self._layout.ndim)] np_arr = np.zeros(shape, np_type) if np_arr.nbytes: memmove(np_arr.ctypes.data_as(c_void_p), ptr, np_arr.nbytes) return np_arr else: tmp_tensor = LiteTensor(self.layout) tmp_tensor.copy_from(self) return tmp_tensor.to_numpy()
def __repr__(self): self.update() data = { "layout": self._layout, "device_type": LiteDeviceType(self._device_type.value), "device_id": int(self.device_id), "is_pinned_host": bool(self._is_pinned_host), } return data.__repr__()
def LiteTensorConcat( tensors, dim, device_type=LiteDeviceType.LITE_DEVICE_DEFAULT, device_id=-1 ): """ concat tensor in input dim to one tensor dim : the dim to act concat device_type: the result tensor device type device_id: the result tensor device id """ api = _TensorAPI()._lib length = len(tensors) c_tensors = [t._tensor for t in tensors] c_tensors = (_Ctensor * length)(*c_tensors) result_tensor = LiteTensor() api.LITE_tensor_concat( cast(byref(c_tensors), POINTER(c_void_p)), length, dim, device_type, device_id, byref(result_tensor._tensor), ) result_tensor.update() return result_tensor def lite_dtype_2_numpy(dtype): """ convert lite dtype to corresponding numpy dtype """ assert isinstance( dtype, LiteDataType ), "input must be LiteDataType when using lite_dtype_2_numpy." return _lite_type_to_nptypes[dtype]