Module furiosa.runtime.furiosa_sdk_runtime.tensor
Tensor object and its utilities
Expand source code
"""Tensor object and its utilities"""
import ctypes
from ctypes import c_void_p, POINTER, c_uint64, c_uint8, byref
from enum import IntEnum
from typing import Union
import numpy as np
from ._util import list_to_dict
from .errors import UnsupportedTensorType
from ._api import LIBNUX
class Axis(IntEnum):
"""Axis of Tensor"""
WIDTH = 0
HEIGHT = 1
CHANNEL = 2
BATCH = 3
WIDTH_OUTER = 4
HEIGHT_OUTER = 5
CHANNEL_OUTER = 6
BATCH_OUTER = 7
UNKNOWN = 8
@classmethod
def _names(cls):
return ["W", "H", "C", "N", "Wo", "Ho", "Co", "No", "?"]
def __repr__(self):
return self._names()[self]
class DataType(IntEnum):
"""Tensor data type"""
FLOAT32 = 0
UINT8 = 1
INT8 = 2
INT32 = 3
@classmethod
def _names(cls):
return ["float32", "uint8", "int8", "int32"]
@classmethod
def _numpy_dtypes(cls):
return [np.float32, np.uint8, np.int8, np.int32]
def __repr__(self) -> str:
return self._names()[self]
def numpy_dtype(self):
"""Return the numpy dtype corresponding to this DataType"""
return self._numpy_dtypes()[self]
class TensorDesc:
"""Tensor description including dimension, shape, and data type"""
ref = c_void_p(None)
def __init__(self, ref):
self.ref = ref
self._as_parameter_ = ref
def ndim(self) -> int:
"""Number of dimensions"""
return LIBNUX.nux_tensor_dim_num(self)
def dim(self, idx) -> int:
"""Size of i-th dimension"""
return LIBNUX.nux_tensor_dim(self, idx)
def shape(self) -> tuple:
"""tensor shape"""
dims = []
for i in range(self.ndim()):
dims.append(self.dim(i))
return tuple(dims)
def axis(self, idx) -> Axis:
"""Axis type of i-th dimension (e.g., width, height, channel)"""
return Axis(LIBNUX.nux_tensor_axis(self, idx))
def size(self) -> int:
"""Size in bytes"""
return LIBNUX.nux_tensor_size(self)
def length(self) -> int:
"""Number of all elements across all dimensions"""
return LIBNUX.nux_tensor_len(self)
def format(self) -> str:
"""Tensor memory layout (e.g., NHWC, NCHW)"""
tensor_format = str()
for idx in range(self.ndim()):
tensor_format += self.axis(idx).__repr__()
return tensor_format
def dtype(self) -> DataType:
"""Data type of tensor"""
return DataType(LIBNUX.nux_tensor_dtype(self))
def numpy_dtype(self):
"""Return numpy dtype"""
return self.dtype().numpy_dtype()
def __repr__(self) -> str:
return self.__class__.__name__ + \
': shape=' + str(self.shape()) + \
', dtype=' + self.dtype().__repr__() + \
', format=' + self.format() + \
', size=' + str(self.size()) + \
', len=' + str(self.length())
class Tensor:
"""A tensor which contains data and tensor description including shape"""
ref = c_void_p(None)
allocated: bool = False
desc = TensorDesc
def __init__(self, ref, desc: TensorDesc, allocated=False):
self.ref = ref
self.desc = desc
self.allocated = allocated
self._as_parameter_ = ref
def shape(self) -> tuple:
"""Return the tensor shape
Returns:
Tensor shape. An example shape is
```(1, 28, 28, 1)```.
"""
return self.desc.shape()
def dtype(self) -> DataType:
"""Data type of tensor"""
return self.desc.dtype()
def numpy_dtype(self):
"""Return numpy dtype"""
return self.desc.numpy_dtype()
def copy_from(self, data: Union[np.ndarray, np.generic]):
"""Copy the contents of Numpy ndarray to this tensor"""
if isinstance(data, (np.ndarray, np.generic)):
buf_ptr = data.ctypes.data_as(POINTER(c_uint8))
buf_size_in_bytes = data.nbytes
LIBNUX.tensor_fill_buffer(self, buf_ptr, buf_size_in_bytes)
else:
raise UnsupportedTensorType()
def numpy(self) -> np.ndarray:
"""Return numpy.ndarray converted from this tensor"""
itemsize = np.dtype(self.numpy_dtype()).itemsize
arr_size = np.prod(self.shape()[:]) * itemsize
buf_ptr = POINTER(c_uint8)()
buf_len = c_uint64(0)
LIBNUX.tensor_get_buffer(self, byref(buf_ptr), byref(buf_len))
buf_from_mem = ctypes.pythonapi.PyMemoryView_FromMemory
buf_from_mem.restype = ctypes.py_object
buf_from_mem.argtypes = (ctypes.c_void_p, ctypes.c_int, ctypes.c_int)
buffer = buf_from_mem(buf_ptr, arr_size, 0x100)
arr = np.ndarray(tuple(self.shape()[:]), self.numpy_dtype(), buffer, order="C")
return arr.copy()
def __repr__(self):
return '<' + self.__class__.__name__ + \
': shape=' + str(self.desc.shape()) + \
', dtype=' + str(self.desc.dtype()) + \
', numpy=' + str(self.numpy()) + '>'
def __del__(self):
if self.allocated and self.ref:
LIBNUX.nux_tensor_destroy(self.ref)
self.allocated = False
def __eq__(self, other):
if isinstance(other, Tensor):
return np.array_equal(self.numpy(), other.numpy())
return False
class TensorArray:
"""A list of tensors
It is used for input and output values of model inferences.
"""
ref = c_void_p(None)
should_drop: bool = False
descs: [TensorDesc]
len: int
def __init__(self, ref, descs: [TensorDesc], allocated=False):
self.ref = ref
self.descs = descs
self.should_drop = allocated
self._as_parameter_ = ref
self.len = LIBNUX.nux_tensor_array_len(self.ref)
def is_empty(self) -> bool:
"""True if it has no Tensor"""
return self.len == 0
def __len__(self):
return self.len
def __getitem__(self, key):
if isinstance(key, int):
while key < 0:
# Index is a negative, so addition will subtract.
key += len(self)
if key >= len(self):
raise IndexError("tensor index (%d) out of range [0, %d)" % (key, len(self)))
return Tensor(LIBNUX.nux_tensor_array_get(self, key),
desc=self.descs[key], allocated=False)
if isinstance(key, slice):
start, stop, step = key.indices(len(self))
return [self[i] for i in range(start, stop, step)]
raise TypeError
def __setitem__(self, key, data):
if isinstance(key, int):
while key < 0:
# Index is a negative, so addition will subtract.
key += len(self)
if key >= len(self):
raise IndexError("tensor index (%d) out of range [0, %d)" % (key, len(self)))
self[key].copy_from(data)
return
raise TypeError
def numpy(self) -> [np.ndarray]:
"""Convert TensorArray to a list of numpy.ndarray"""
array = []
for idx in range(self.len):
array.append(self[idx].numpy())
return array
def __del__(self):
if self.should_drop and self.ref:
LIBNUX.nux_tensor_array_destroy(self.ref)
self.should_drop = False
def __repr__(self):
return list_to_dict(self).__repr__()
def numpy_dtype(value):
"""Return numpy dtype from any eligible object of Nux"""
if isinstance(value, (np.ndarray, np.generic)):
return value.dtype()
if isinstance(value, Tensor):
return value.numpy_dtype()
if isinstance(value, TensorDesc):
return value.numpy_dtype()
if isinstance(value, DataType):
return value.numpy_dtype()
raise TypeError
Functions
def numpy_dtype(value)
-
Return numpy dtype from any eligible object of Nux
Expand source code
def numpy_dtype(value): """Return numpy dtype from any eligible object of Nux""" if isinstance(value, (np.ndarray, np.generic)): return value.dtype() if isinstance(value, Tensor): return value.numpy_dtype() if isinstance(value, TensorDesc): return value.numpy_dtype() if isinstance(value, DataType): return value.numpy_dtype() raise TypeError
Classes
class Axis (value, names=None, *, module=None, qualname=None, type=None, start=1)
-
Axis of Tensor
Expand source code
class Axis(IntEnum): """Axis of Tensor""" WIDTH = 0 HEIGHT = 1 CHANNEL = 2 BATCH = 3 WIDTH_OUTER = 4 HEIGHT_OUTER = 5 CHANNEL_OUTER = 6 BATCH_OUTER = 7 UNKNOWN = 8 @classmethod def _names(cls): return ["W", "H", "C", "N", "Wo", "Ho", "Co", "No", "?"] def __repr__(self): return self._names()[self]
Ancestors
- enum.IntEnum
- builtins.int
- enum.Enum
Class variables
var BATCH
var BATCH_OUTER
var CHANNEL
var CHANNEL_OUTER
var HEIGHT
var HEIGHT_OUTER
var UNKNOWN
var WIDTH
var WIDTH_OUTER
class DataType (value, names=None, *, module=None, qualname=None, type=None, start=1)
-
Tensor data type
Expand source code
class DataType(IntEnum): """Tensor data type""" FLOAT32 = 0 UINT8 = 1 INT8 = 2 INT32 = 3 @classmethod def _names(cls): return ["float32", "uint8", "int8", "int32"] @classmethod def _numpy_dtypes(cls): return [np.float32, np.uint8, np.int8, np.int32] def __repr__(self) -> str: return self._names()[self] def numpy_dtype(self): """Return the numpy dtype corresponding to this DataType""" return self._numpy_dtypes()[self]
Ancestors
- enum.IntEnum
- builtins.int
- enum.Enum
Class variables
var FLOAT32
var INT32
var INT8
var UINT8
Methods
def numpy_dtype(self)
-
Return the numpy dtype corresponding to this DataType
Expand source code
def numpy_dtype(self): """Return the numpy dtype corresponding to this DataType""" return self._numpy_dtypes()[self]
class Tensor (ref, desc: TensorDesc, allocated=False)
-
A tensor which contains data and tensor description including shape
Expand source code
class Tensor: """A tensor which contains data and tensor description including shape""" ref = c_void_p(None) allocated: bool = False desc = TensorDesc def __init__(self, ref, desc: TensorDesc, allocated=False): self.ref = ref self.desc = desc self.allocated = allocated self._as_parameter_ = ref def shape(self) -> tuple: """Return the tensor shape Returns: Tensor shape. An example shape is ```(1, 28, 28, 1)```. """ return self.desc.shape() def dtype(self) -> DataType: """Data type of tensor""" return self.desc.dtype() def numpy_dtype(self): """Return numpy dtype""" return self.desc.numpy_dtype() def copy_from(self, data: Union[np.ndarray, np.generic]): """Copy the contents of Numpy ndarray to this tensor""" if isinstance(data, (np.ndarray, np.generic)): buf_ptr = data.ctypes.data_as(POINTER(c_uint8)) buf_size_in_bytes = data.nbytes LIBNUX.tensor_fill_buffer(self, buf_ptr, buf_size_in_bytes) else: raise UnsupportedTensorType() def numpy(self) -> np.ndarray: """Return numpy.ndarray converted from this tensor""" itemsize = np.dtype(self.numpy_dtype()).itemsize arr_size = np.prod(self.shape()[:]) * itemsize buf_ptr = POINTER(c_uint8)() buf_len = c_uint64(0) LIBNUX.tensor_get_buffer(self, byref(buf_ptr), byref(buf_len)) buf_from_mem = ctypes.pythonapi.PyMemoryView_FromMemory buf_from_mem.restype = ctypes.py_object buf_from_mem.argtypes = (ctypes.c_void_p, ctypes.c_int, ctypes.c_int) buffer = buf_from_mem(buf_ptr, arr_size, 0x100) arr = np.ndarray(tuple(self.shape()[:]), self.numpy_dtype(), buffer, order="C") return arr.copy() def __repr__(self): return '<' + self.__class__.__name__ + \ ': shape=' + str(self.desc.shape()) + \ ', dtype=' + str(self.desc.dtype()) + \ ', numpy=' + str(self.numpy()) + '>' def __del__(self): if self.allocated and self.ref: LIBNUX.nux_tensor_destroy(self.ref) self.allocated = False def __eq__(self, other): if isinstance(other, Tensor): return np.array_equal(self.numpy(), other.numpy()) return False
Class variables
var allocated : bool
var desc
-
Tensor description including dimension, shape, and data type
var ref
Methods
def copy_from(self, data: Union[numpy.ndarray, numpy.generic])
-
Copy the contents of Numpy ndarray to this tensor
Expand source code
def copy_from(self, data: Union[np.ndarray, np.generic]): """Copy the contents of Numpy ndarray to this tensor""" if isinstance(data, (np.ndarray, np.generic)): buf_ptr = data.ctypes.data_as(POINTER(c_uint8)) buf_size_in_bytes = data.nbytes LIBNUX.tensor_fill_buffer(self, buf_ptr, buf_size_in_bytes) else: raise UnsupportedTensorType()
def dtype(self) ‑> DataType
-
Data type of tensor
Expand source code
def dtype(self) -> DataType: """Data type of tensor""" return self.desc.dtype()
def numpy(self) ‑> numpy.ndarray
-
Return numpy.ndarray converted from this tensor
Expand source code
def numpy(self) -> np.ndarray: """Return numpy.ndarray converted from this tensor""" itemsize = np.dtype(self.numpy_dtype()).itemsize arr_size = np.prod(self.shape()[:]) * itemsize buf_ptr = POINTER(c_uint8)() buf_len = c_uint64(0) LIBNUX.tensor_get_buffer(self, byref(buf_ptr), byref(buf_len)) buf_from_mem = ctypes.pythonapi.PyMemoryView_FromMemory buf_from_mem.restype = ctypes.py_object buf_from_mem.argtypes = (ctypes.c_void_p, ctypes.c_int, ctypes.c_int) buffer = buf_from_mem(buf_ptr, arr_size, 0x100) arr = np.ndarray(tuple(self.shape()[:]), self.numpy_dtype(), buffer, order="C") return arr.copy()
def numpy_dtype(self)
-
Return numpy dtype
Expand source code
def numpy_dtype(self): """Return numpy dtype""" return self.desc.numpy_dtype()
def shape(self) ‑> tuple
-
Return the tensor shape
Returns
Tensor shape. An example shape is
(1, 28, 28, 1)
.Expand source code
def shape(self) -> tuple: """Return the tensor shape Returns: Tensor shape. An example shape is ```(1, 28, 28, 1)```. """ return self.desc.shape()
class TensorArray (ref, descs: [
TensorDesc'>], allocated=False) -
A list of tensors
It is used for input and output values of model inferences.
Expand source code
class TensorArray: """A list of tensors It is used for input and output values of model inferences. """ ref = c_void_p(None) should_drop: bool = False descs: [TensorDesc] len: int def __init__(self, ref, descs: [TensorDesc], allocated=False): self.ref = ref self.descs = descs self.should_drop = allocated self._as_parameter_ = ref self.len = LIBNUX.nux_tensor_array_len(self.ref) def is_empty(self) -> bool: """True if it has no Tensor""" return self.len == 0 def __len__(self): return self.len def __getitem__(self, key): if isinstance(key, int): while key < 0: # Index is a negative, so addition will subtract. key += len(self) if key >= len(self): raise IndexError("tensor index (%d) out of range [0, %d)" % (key, len(self))) return Tensor(LIBNUX.nux_tensor_array_get(self, key), desc=self.descs[key], allocated=False) if isinstance(key, slice): start, stop, step = key.indices(len(self)) return [self[i] for i in range(start, stop, step)] raise TypeError def __setitem__(self, key, data): if isinstance(key, int): while key < 0: # Index is a negative, so addition will subtract. key += len(self) if key >= len(self): raise IndexError("tensor index (%d) out of range [0, %d)" % (key, len(self))) self[key].copy_from(data) return raise TypeError def numpy(self) -> [np.ndarray]: """Convert TensorArray to a list of numpy.ndarray""" array = [] for idx in range(self.len): array.append(self[idx].numpy()) return array def __del__(self): if self.should_drop and self.ref: LIBNUX.nux_tensor_array_destroy(self.ref) self.should_drop = False def __repr__(self): return list_to_dict(self).__repr__()
Class variables
var descs : [
TensorDesc'>] var len : int
var ref
var should_drop : bool
Methods
def is_empty(self) ‑> bool
-
True if it has no Tensor
Expand source code
def is_empty(self) -> bool: """True if it has no Tensor""" return self.len == 0
def numpy(self) ‑> [
] -
Convert TensorArray to a list of numpy.ndarray
Expand source code
def numpy(self) -> [np.ndarray]: """Convert TensorArray to a list of numpy.ndarray""" array = [] for idx in range(self.len): array.append(self[idx].numpy()) return array
class TensorDesc (ref)
-
Tensor description including dimension, shape, and data type
Expand source code
class TensorDesc: """Tensor description including dimension, shape, and data type""" ref = c_void_p(None) def __init__(self, ref): self.ref = ref self._as_parameter_ = ref def ndim(self) -> int: """Number of dimensions""" return LIBNUX.nux_tensor_dim_num(self) def dim(self, idx) -> int: """Size of i-th dimension""" return LIBNUX.nux_tensor_dim(self, idx) def shape(self) -> tuple: """tensor shape""" dims = [] for i in range(self.ndim()): dims.append(self.dim(i)) return tuple(dims) def axis(self, idx) -> Axis: """Axis type of i-th dimension (e.g., width, height, channel)""" return Axis(LIBNUX.nux_tensor_axis(self, idx)) def size(self) -> int: """Size in bytes""" return LIBNUX.nux_tensor_size(self) def length(self) -> int: """Number of all elements across all dimensions""" return LIBNUX.nux_tensor_len(self) def format(self) -> str: """Tensor memory layout (e.g., NHWC, NCHW)""" tensor_format = str() for idx in range(self.ndim()): tensor_format += self.axis(idx).__repr__() return tensor_format def dtype(self) -> DataType: """Data type of tensor""" return DataType(LIBNUX.nux_tensor_dtype(self)) def numpy_dtype(self): """Return numpy dtype""" return self.dtype().numpy_dtype() def __repr__(self) -> str: return self.__class__.__name__ + \ ': shape=' + str(self.shape()) + \ ', dtype=' + self.dtype().__repr__() + \ ', format=' + self.format() + \ ', size=' + str(self.size()) + \ ', len=' + str(self.length())
Class variables
var ref
Methods
def axis(self, idx) ‑> Axis
-
Axis type of i-th dimension (e.g., width, height, channel)
Expand source code
def axis(self, idx) -> Axis: """Axis type of i-th dimension (e.g., width, height, channel)""" return Axis(LIBNUX.nux_tensor_axis(self, idx))
def dim(self, idx) ‑> int
-
Size of i-th dimension
Expand source code
def dim(self, idx) -> int: """Size of i-th dimension""" return LIBNUX.nux_tensor_dim(self, idx)
def dtype(self) ‑> DataType
-
Data type of tensor
Expand source code
def dtype(self) -> DataType: """Data type of tensor""" return DataType(LIBNUX.nux_tensor_dtype(self))
def format(self) ‑> str
-
Tensor memory layout (e.g., NHWC, NCHW)
Expand source code
def format(self) -> str: """Tensor memory layout (e.g., NHWC, NCHW)""" tensor_format = str() for idx in range(self.ndim()): tensor_format += self.axis(idx).__repr__() return tensor_format
def length(self) ‑> int
-
Number of all elements across all dimensions
Expand source code
def length(self) -> int: """Number of all elements across all dimensions""" return LIBNUX.nux_tensor_len(self)
def ndim(self) ‑> int
-
Number of dimensions
Expand source code
def ndim(self) -> int: """Number of dimensions""" return LIBNUX.nux_tensor_dim_num(self)
def numpy_dtype(self)
-
Return numpy dtype
Expand source code
def numpy_dtype(self): """Return numpy dtype""" return self.dtype().numpy_dtype()
def shape(self) ‑> tuple
-
tensor shape
Expand source code
def shape(self) -> tuple: """tensor shape""" dims = [] for i in range(self.ndim()): dims.append(self.dim(i)) return tuple(dims)
def size(self) ‑> int
-
Size in bytes
Expand source code
def size(self) -> int: """Size in bytes""" return LIBNUX.nux_tensor_size(self)