Tianqi Chen
346 lines
10 KiB
Python
346 lines
10 KiB
Python
# pylint: disable=invalid-name, unused-import
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"""Runtime NDArray api"""
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from __future__ import absolute_import
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import sys
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import ctypes
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import numpy as np
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from .base import _LIB, check_call, c_array, string_types, _FFI_MODE, c_str
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from .runtime_ctypes import TVMType, TVMContext, TVMArray, TVMArrayHandle
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from .runtime_ctypes import TypeCode, tvm_shape_index_t
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IMPORT_EXCEPT = RuntimeError if _FFI_MODE == "cython" else ImportError
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try:
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# pylint: disable=wrong-import-position
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if _FFI_MODE == "ctypes":
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raise ImportError()
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if sys.version_info >= (3, 0):
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from ._cy3.core import _set_class_ndarray, _reg_extension, _make_array, _from_dlpack
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from ._cy3.core import NDArrayBase as _NDArrayBase
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else:
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from ._cy2.core import _set_class_ndarray, _reg_extension, _make_array, _from_dlpack
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from ._cy2.core import NDArrayBase as _NDArrayBase
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except IMPORT_EXCEPT:
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# pylint: disable=wrong-import-position
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from ._ctypes.ndarray import _set_class_ndarray, _reg_extension, _make_array, _from_dlpack
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from ._ctypes.ndarray import NDArrayBase as _NDArrayBase
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def context(dev_type, dev_id=0):
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"""Construct a TVM context with given device type and id.
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Parameters
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----------
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dev_type: int or str
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The device type mask or name of the device.
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dev_id : int, optional
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The integer device id
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Returns
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-------
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ctx: TVMContext
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The corresponding context.
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Examples
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--------
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Context can be used to create reflection of context by
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string representation of the device type.
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.. code-block:: python
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assert tvm.context("cpu", 1) == tvm.cpu(1)
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assert tvm.context("gpu", 0) == tvm.gpu(0)
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assert tvm.context("cuda", 0) == tvm.gpu(0)
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"""
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if isinstance(dev_type, string_types):
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dev_type = dev_type.split()[0]
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if dev_type not in TVMContext.STR2MASK:
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raise ValueError("Unknown device type %s" % dev_type)
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dev_type = TVMContext.STR2MASK[dev_type]
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return TVMContext(dev_type, dev_id)
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def numpyasarray(np_data):
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"""Return a TVMArray representation of a numpy array.
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"""
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data = np_data
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assert data.flags['C_CONTIGUOUS']
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arr = TVMArray()
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shape = c_array(tvm_shape_index_t, data.shape)
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arr.data = data.ctypes.data_as(ctypes.c_void_p)
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arr.shape = shape
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arr.strides = None
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arr.dtype = TVMType(np.dtype(data.dtype).name)
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arr.ndim = data.ndim
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# CPU device
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arr.ctx = context(1, 0)
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return arr, shape
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def empty(shape, dtype="float32", ctx=context(1, 0)):
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"""Create an empty array given shape and device
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Parameters
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----------
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shape : tuple of int
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The shape of the array
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dtype : type or str
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The data type of the array.
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ctx : TVMContext
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The context of the array
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Returns
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-------
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arr : tvm.nd.NDArray
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The array tvm supported.
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"""
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shape = c_array(tvm_shape_index_t, shape)
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ndim = ctypes.c_int(len(shape))
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handle = TVMArrayHandle()
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dtype = TVMType(dtype)
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check_call(_LIB.TVMArrayAlloc(
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shape, ndim,
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ctypes.c_int(dtype.type_code),
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ctypes.c_int(dtype.bits),
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ctypes.c_int(dtype.lanes),
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ctx.device_type,
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ctx.device_id,
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ctypes.byref(handle)))
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return _make_array(handle, False)
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def from_dlpack(dltensor):
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"""Produce an array from a DLPack tensor without memory copy.
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Retreives the underlying DLPack tensor's pointer to create an array from the
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data. Removes the original DLPack tensor's destructor as now the array is
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responsible for destruction.
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Parameters
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----------
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dltensor : DLPack tensor
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Input DLManagedTensor, can only be consumed once.
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Returns
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-------
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arr: tvm.nd.NDArray
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The array view of the tensor data.
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"""
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return _from_dlpack(dltensor)
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class NDArrayBase(_NDArrayBase):
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"""A simple Device/CPU Array object in runtime."""
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@property
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def shape(self):
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"""Shape of this array"""
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return tuple(self.handle.contents.shape[i] for i in range(self.handle.contents.ndim))
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@property
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def dtype(self):
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"""Type of this array"""
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return str(self.handle.contents.dtype)
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@property
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def ctx(self):
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"""context of this array"""
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return self.handle.contents.ctx
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@property
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def context(self):
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"""context of this array"""
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return self.ctx
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def __hash__(self):
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return ctypes.cast(self.handle, ctypes.c_void_p).value
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def __eq__(self, other):
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return self.same_as(other)
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def __ne__(self, other):
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return not self.__eq__(other)
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def same_as(self, other):
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"""Check object identity equality
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Parameters
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----------
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other : object
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The other object to compare to
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Returns
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-------
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same : bool
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Whether other is same as self.
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"""
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if not isinstance(other, NDArrayBase):
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return False
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return self.__hash__() == other.__hash__()
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def __setitem__(self, in_slice, value):
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"""Set ndarray value"""
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if (not isinstance(in_slice, slice) or
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in_slice.start is not None
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or in_slice.stop is not None):
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raise ValueError('Array only support set from numpy array')
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if isinstance(value, NDArrayBase):
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if value.handle is not self.handle:
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value.copyto(self)
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elif isinstance(value, (np.ndarray, np.generic)):
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self.copyfrom(value)
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else:
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raise TypeError('type %s not supported' % str(type(value)))
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def copyfrom(self, source_array):
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"""Peform an synchronize copy from the array.
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Parameters
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----------
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source_array : array_like
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The data source we should like to copy from.
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Returns
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-------
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arr : NDArray
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Reference to self.
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"""
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if isinstance(source_array, NDArrayBase):
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source_array.copyto(self)
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return self
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if not isinstance(source_array, np.ndarray):
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try:
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source_array = np.array(source_array, dtype=self.dtype)
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except:
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raise TypeError('array must be an array_like data,' +
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'type %s is not supported' % str(type(source_array)))
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t = TVMType(self.dtype)
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shape, dtype = self.shape, self.dtype
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if t.lanes > 1:
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shape = shape + (t.lanes,)
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t.lanes = 1
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dtype = str(t)
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if source_array.shape != shape:
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raise ValueError("array shape do not match the shape of NDArray {0} vs {1}".format(
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source_array.shape, shape))
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source_array = np.ascontiguousarray(source_array, dtype=dtype)
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assert source_array.flags['C_CONTIGUOUS']
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data = source_array.ctypes.data_as(ctypes.c_void_p)
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nbytes = ctypes.c_size_t(source_array.size * source_array.dtype.itemsize)
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check_call(_LIB.TVMArrayCopyFromBytes(self.handle, data, nbytes))
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return self
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def __repr__(self):
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res = "<tvm.NDArray shape={0}, {1}>\n".format(self.shape, self.context)
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res += self.asnumpy().__repr__()
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return res
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def __str__(self):
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return str(self.asnumpy())
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def asnumpy(self):
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"""Convert this array to numpy array
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Returns
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-------
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np_arr : numpy.ndarray
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The corresponding numpy array.
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"""
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t = TVMType(self.dtype)
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shape, dtype = self.shape, self.dtype
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if t.lanes > 1:
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shape = shape + (t.lanes,)
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t.lanes = 1
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dtype = str(t)
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np_arr = np.empty(shape, dtype=dtype)
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assert np_arr.flags['C_CONTIGUOUS']
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data = np_arr.ctypes.data_as(ctypes.c_void_p)
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nbytes = ctypes.c_size_t(np_arr.size * np_arr.dtype.itemsize)
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check_call(_LIB.TVMArrayCopyToBytes(self.handle, data, nbytes))
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return np_arr
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def copyto(self, target):
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"""Copy array to target
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Parameters
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----------
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target : NDArray
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The target array to be copied, must have same shape as this array.
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"""
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if isinstance(target, TVMContext):
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target = empty(self.shape, self.dtype, target)
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if isinstance(target, NDArrayBase):
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check_call(_LIB.TVMArrayCopyFromTo(
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self.handle, target.handle, None))
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else:
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raise ValueError("Unsupported target type %s" % str(type(target)))
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return target
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def free_extension_handle(handle, type_code):
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"""Free c++ extension type handle
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Parameters
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----------
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handle : ctypes.c_void_p
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The handle to the extension type.
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type_code : int
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The tyoe code
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"""
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check_call(_LIB.TVMExtTypeFree(handle, ctypes.c_int(type_code)))
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def register_extension(cls, fcreate=None):
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"""Register a extension class to TVM.
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After the class is registered, the class will be able
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to directly pass as Function argument generated by TVM.
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Parameters
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----------
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cls : class
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The class object to be registered as extension.
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Note
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----
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The registered class is requires one property: _tvm_handle and a class attribute _tvm_tcode.
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- ```_tvm_handle``` returns integer represents the address of the handle.
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- ```_tvm_tcode``` gives integer represents type code of the class.
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Returns
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-------
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cls : class
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The class being registered.
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fcreate : function, optional
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The creation function to create a class object given handle value.
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Example
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-------
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The following code registers user defined class
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MyTensor to be DLTensor compatible.
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.. code-block:: python
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@tvm.register_extension
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class MyTensor(object):
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_tvm_tcode = tvm.TypeCode.ARRAY_HANDLE
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def __init__(self):
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self.handle = _LIB.NewDLTensor()
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@property
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def _tvm_handle(self):
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return self.handle.value
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"""
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if fcreate and cls._tvm_tcode < TypeCode.EXT_BEGIN:
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raise ValueError("Cannot register create when extension tcode is same as buildin")
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_reg_extension(cls, fcreate)
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return cls
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