Eric Lunderberg
ef35acbb96
`runtime.TVMArrayAllocWithScope` predates the introduction of ShapeTuple, and its use simplifies the `tvm.nd.empty` function. The two modified locations are the only occurrences of the string "TVMArrayAllocWithScope" in the repository, so no other call sites should need to be updated.
601 lines
16 KiB
Python
601 lines
16 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name, unused-import, redefined-outer-name
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"""Runtime NDArray API"""
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import ctypes
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import warnings
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import numpy as np
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import tvm._ffi
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from tvm._ffi.base import _LIB, check_call, c_array, string_types, _FFI_MODE
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from tvm._ffi.runtime_ctypes import DataType, Device, TVMArray, TVMArrayHandle
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from tvm._ffi.runtime_ctypes import DataTypeCode, tvm_shape_index_t
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from . import _ffi_api
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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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from tvm._ffi._cy3.core import _set_class_ndarray, _make_array, _from_dlpack
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from tvm._ffi._cy3.core import NDArrayBase
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except (RuntimeError, ImportError) as error:
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# pylint: disable=wrong-import-position
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if _FFI_MODE == "cython":
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raise error
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from tvm._ffi._ctypes.ndarray import _set_class_ndarray, _make_array, _from_dlpack
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from tvm._ffi._ctypes.ndarray import NDArrayBase
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@tvm._ffi.register_object("runtime.NDArray")
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class NDArray(NDArrayBase):
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"""Lightweight NDArray class of TVM runtime.
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Strictly this is only an Array Container (a buffer object)
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No arthimetic operations are defined.
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All operations are performed by TVM functions.
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The goal is not to re-build yet another array library.
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Instead, this is a minimal data structure to demonstrate
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how can we use TVM in existing project which might have their own array containers.
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"""
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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 device(self):
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"""Device of this array"""
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return self.handle.contents.device
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def __dlpack__(self, stream=None): # pylint: disable=unused-argument
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"""Export the array for consumption by from_dlpack() as a DLPack capsule.
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Parameters
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----------
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stream : int, optional
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A Python integer representing a pointer to a stream.
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Stream is provided by the consumer to the producer to instruct the producer
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to ensure that operations can safely be performed on the array.
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Returns
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-------
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capsule : PyCapsule
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A DLPack capsule for the array, containing a DLPackManagedTensor.
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"""
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return self.to_dlpack()
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def __dlpack_device__(self):
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"""Return a tuple of device_type, device_id in DLPack convention"""
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return (self.handle.contents.device.device_type, self.handle.contents.device.device_id)
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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 (
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not isinstance(in_slice, slice)
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or in_slice.start is not None
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or in_slice.stop is not None
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):
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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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"""Perform 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(
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"array must be an array_like data,"
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+ "type %s is not supported" % str(type(source_array))
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)
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t = DataType(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(
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"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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)
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)
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numpy_str_map = DataType.NUMPY2STR
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np_dtype_str = (
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numpy_str_map[source_array.dtype]
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if source_array.dtype in numpy_str_map
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else str(source_array.dtype)
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)
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if (not source_array.flags["C_CONTIGUOUS"]) or (
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dtype == "bfloat16" or dtype != np_dtype_str
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):
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source_array = np.ascontiguousarray(
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source_array, dtype="uint16" if dtype == "bfloat16" else dtype
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)
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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.nd.NDArray shape={0}, {1}>\n".format(self.shape, self.device)
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res += self.numpy().__repr__()
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return res
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def __str__(self):
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return str(self.numpy())
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def asnumpy(self):
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"""Convert this array to numpy array. This API will be deprecated in TVM v0.8 release.
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Please use `numpy` instead."""
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warnings.warn(
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"NDArray.asnumpy() will be deprecated in TVM v0.8 release. "
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"Please use NDArray.numpy() instead.",
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DeprecationWarning,
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)
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return self.numpy()
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def numpy(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 = DataType(self.dtype)
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shape, dtype = self.shape, self.dtype
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old_dtype = 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 dtype == "int4":
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dtype = "int8"
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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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if old_dtype == "int4":
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length = np_arr.size
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np_arr_ret = np.empty((length,), dtype="int8")
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np_arr = np_arr.reshape((length,))
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old_index = np.bitwise_and(np_arr, 0x0F)
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even_index = np.bitwise_and(np_arr >> 4, 0x0F)
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np_arr_ret[1::2] = old_index[0 : length // 2]
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np_arr_ret[0::2] = even_index[0 : length // 2]
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return np_arr_ret.reshape(shape)
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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, NDArrayBase):
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return self._copyto(target)
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if isinstance(target, Device):
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res = empty(self.shape, self.dtype, target)
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return self._copyto(res)
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raise ValueError("Unsupported target type %s" % str(type(target)))
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def _create_view(self, shape):
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"""Create a view into an existing array.
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The view shares the same allocation and datatype as the
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existing array, but can have a different array shape. This is
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useful for runtimes that support non-flat memory, where both
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the physical shape of an allocation and the logical shape of
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the tensor it represents may need to be independently
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specified.
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Warning: This function should not be used outside of low-level
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manipulations, as it breaks non-aliasing assumptions made by
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TVM. This function may also be removed/replaced in the
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future.
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Parameters
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----------
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shape: Union[tvm.runtime.ShapeTuple, Sequence[typing.SupportsInt]]
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The shape of the view.
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"""
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if not isinstance(shape, tvm.runtime.ShapeTuple):
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shape = tvm.runtime.ShapeTuple([int(dim) for dim in shape])
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return _ffi_api.TVMArrayCreateView(self, shape)
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def device(dev_type, dev_id=0):
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"""Construct a TVM device 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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dev: tvm.runtime.Device
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The corresponding device.
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Examples
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--------
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Device can be used to create reflection of device by
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string representation of the device type.
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.. code-block:: python
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assert tvm.device("cpu", 1) == tvm.cpu(1)
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assert tvm.device("cuda", 0) == tvm.cuda(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 Device.STR2MASK:
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raise ValueError("Unknown device type %s" % dev_type)
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dev_type = Device.STR2MASK[dev_type]
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return Device(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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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 = DataType(np.dtype(data.dtype).name)
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arr.ndim = data.ndim
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# CPU device
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arr.device = device(1, 0)
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return arr, shape
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def empty(shape, dtype="float32", device=device(1, 0), mem_scope=None):
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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 : Union[tvm.runtime.ShapeTuple, Sequence[typing.SupportsInt]]
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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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device : Device
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The device of the array.
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mem_scope : Optional[str]
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The memory scope 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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if not isinstance(shape, tvm.runtime.ShapeTuple):
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shape = tvm.runtime.ShapeTuple([int(dim) for dim in shape])
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dtype = DataType(dtype)
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arr = _ffi_api.TVMArrayAllocWithScope(shape, dtype, device, mem_scope)
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return arr
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def from_dlpack(dltensor):
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"""Produces an array from an object with __dlpack__ method or a DLPack tensor w/o 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 : object with __dlpack__ attribute or a DLPack capsule
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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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t = type(dltensor)
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if t.__module__ == "builtins" and t.__name__ == "PyCapsule":
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return _from_dlpack(dltensor)
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if hasattr(dltensor, "__dlpack__"):
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dlpack_caps = dltensor.__dlpack__()
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return _from_dlpack(dlpack_caps)
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raise AttributeError("Required attribute __dlpack__ not found")
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def cpu(dev_id=0):
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"""Construct a CPU device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(1, dev_id)
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def cuda(dev_id=0):
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"""Construct a CUDA GPU device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(2, dev_id)
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def gpu(dev_id=0):
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"""Construct a CUDA GPU device
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deprecated:: 0.9.0
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Use :py:func:`tvm.cuda` instead.
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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warnings.warn(
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"Please use tvm.cuda() instead of tvm.gpu(). tvm.gpu() is going to be deprecated in 0.9.0",
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)
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return Device(2, dev_id)
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def rocm(dev_id=0):
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"""Construct a ROCM device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(10, dev_id)
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def opencl(dev_id=0):
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"""Construct a OpenCL device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(4, dev_id)
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def metal(dev_id=0):
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"""Construct a metal device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(8, dev_id)
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def vpi(dev_id=0):
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"""Construct a VPI simulated device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(9, dev_id)
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def vulkan(dev_id=0):
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"""Construct a Vulkan device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(7, dev_id)
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def ext_dev(dev_id=0):
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"""Construct a extension device
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Parameters
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----------
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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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dev : Device
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The created device
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Note
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----
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This API is reserved for quick testing of new
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device by plugin device API as ext_dev.
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"""
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return Device(12, dev_id)
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def hexagon(dev_id=0):
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"""Construct a Hexagon device
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(14, dev_id)
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def webgpu(dev_id=0):
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"""Construct a webgpu device.
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Parameters
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----------
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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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dev : Device
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The created device
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"""
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return Device(15, dev_id)
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cl = opencl
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mtl = metal
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def array(arr, device=cpu(0)):
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"""Create an array from source arr.
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Parameters
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----------
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arr : numpy.ndarray
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The array to be copied from
|
|
|
|
device : Device, optional
|
|
The device device to create the array
|
|
|
|
Returns
|
|
-------
|
|
ret : NDArray
|
|
The created array
|
|
"""
|
|
if isinstance(arr, tvm.ir.container.Array):
|
|
raise AttributeError("arr is an instance of", type(arr))
|
|
|
|
if not isinstance(arr, (np.ndarray, NDArray)):
|
|
arr = np.array(arr)
|
|
return empty(arr.shape, arr.dtype, device).copyfrom(arr)
|
|
|
|
|
|
# Register back to FFI
|
|
_set_class_ndarray(NDArray)
|