cyx-6 commented on code in PR #169:
URL: https://github.com/apache/tvm-ffi/pull/169#discussion_r2543881059
##########
include/tvm/ffi/container/tensor.h:
##########
@@ -350,6 +350,50 @@ class Tensor : public ObjectRef {
bool IsAligned(size_t alignment) const { return tvm::ffi::IsAligned(*get(),
alignment); }
/*!
* \brief Create a Tensor from a NDAllocator.
+ *
+ *
+ * Example usage:
Review Comment:
added
##########
docs/guides/kernel_library_guide.rst:
##########
@@ -0,0 +1,167 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements. See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership. The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License. You may obtain a copy of the License at
+..
+.. http://www.apache.org/licenses/LICENSE-2.0
+..
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied. See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+====================
+Kernel Library Guide
+====================
+
+This guide serves as a quick start for shipping python version and machine
learning(ML) framework agnostic kernel libraries with TVM FFI. With the help of
TVM FFI, we can connect the kernel libraries to multiple ML framework, such as
PyTorch, XLA, JAX, together with the minimal efforts.
+
+Tensor
+======
+
+Almost all kernel libraries are about tensor computation and manipulation. For
better adaptation to different ML frameworks, TVM FFI provides a minimal set of
data structures to represent tensors from ML frameworks, including the tensor
basic attributes and storage pointer.
+To be specific, in TVM FFI, two types of tensor constructs,
:cpp:class:`~tvm::ffi::Tensor` and :cpp:class:`~tvm::ffi::TensorView`, can be
used to represent a tensor from ML frameworks.
+
+Tensor and TensorView
+---------------------
+
+Though both :cpp:class:`~tvm::ffi::Tensor` and
:cpp:class:`~tvm::ffi::TensorView` are designed to represent tensors from ML
frameworks that interact with the TVM FFI ABI. They are backed by the
`DLTensor` in DLPack in practice. The main difference is whether it is an
owning tensor structure.
+
+:cpp:class:`tvm::ffi::Tensor`
+ :cpp:class:`~tvm::ffi::Tensor` is a completely owning tensor with reference
counting. It can be created and passed between C++ and Python side safely. When
the counting reference goes to zero, its underlying deleter function will be
called to free the tensor storage.
+
+:cpp:class:`tvm::ffi::TensorView`
+ :cpp:class:`~tvm::ffi::TensorView` is a non-owning view of an existing
tensor, pointing to an existing tensor (e.g., a tensor allocated by PyTorch).
+
+It is **recommended** to use :cpp:class:`~tvm::ffi::TensorView` when possible,
that helps us to support more cases, including cases where only view but not
strong reference are passed, like XLA buffer.
+It is also more lightweight. However, since :cpp:class:`~tvm::ffi::TensorView`
is a non-owning view, it is the user's responsibility to ensure the lifetime of
underlying tensor data and attributes of the viewed tensor object.
+
+Tensor Attributes
+-----------------
+
+For the sake of convenience, :cpp:class:`~tvm::ffi::TensorView` and
:cpp:class:`~tvm::ffi::Tensor` align the following attributes retrieval mehtods
to :cpp:class:`at::Tensor` interface, to obtain tensor basic attributes and
storage pointer:
+``dim``, ``dtype``, ``sizes``, ``size``, ``strides``, ``stride``, ``numel``,
``data_ptr``, ``device``, ``is_contiguous``
+
+:c:struct:`DLDataType`
+ The ``dtype`` of the tensor. It's represented by a struct with three fields:
code, bits, and lanes, defined by DLPack protocol.
+
+:c:struct:`DLDevice`
+ The ``device`` where the tensor is stored. It is represented by a struct with
two fields: device_type and device_id, defined by DLPack protocol.
+
+:cpp:class:`tvm::ffi::ShapeView`
+ The ``sizes`` and ``strides`` attributes retrieval are returned as
:cpp:class:`~tvm::ffi::ShapeView`. It is an iterate-able data structure storing
the shapes or strides data as ``int64_t`` array.
+
+Tensor Allocation
+-----------------
+
+TVM FFI provides several methods to create or allocate tensors at C++ runtime.
Generally, there are two types of tensor creation methods:
+
+* Allocate a tensor with new storage from scratch, i.e.
:cpp:func:`~tvm::ffi::Tensor::FromEnvAlloc` and
:cpp:func:`~tvm::ffi::Tensor::FromNDAlloc`. By this types of methods, the
shapes, strides, data types, devices and other attributes are required for the
allocation.
+* Create a tensor with existing storage following DLPack protocol, i.e.
:cpp:func:`~tvm::ffi::Tensor::FromDLPack` and
:cpp:func:`~tvm::ffi::Tensor::FromDLPackVersioned`. By this types of methods,
the shapes, data types, devices and other attributes can be inferred from the
DLPack attributes.
+
+FromEnvAlloc
+^^^^^^^^^^^^
+
+To better adapt to the ML framework, it is **recommended** to reuse the
framework tensor allocator anyway, instead of directly allocating the tensors
via CUDA runtime API, like ``cudaMalloc``. Since reusing the framework tensor
allocator:
+
+* Benefit from the framework's native caching allocator or related allocation
mechanism.
+* Help framework tracking memory usage and planning globally.
+
+For this case, TVM FFI provides :cpp:func:`tvm::ffi::Tensor::FromEnvAlloc`. It
internally calls the framework tensor allocator. To determine which framework
tensor allocator, TVM FFI infers it from the passed-in framework tensors. For
example, when calling the kernel library at Python side, there is an input
framework tensor if of type ``torch.Tensor``, TVM FFI will automatically bind
the :cpp:func:`at::empty` as the current framework tensor allocator by
``TVMFFIEnvTensorAlloc``. And then the
:cpp:func:`~tvm::ffi::Tensor::FromEnvAlloc` is calling the
:cpp:class:`at::empty` actually:
+
+.. code-block:: c++
+
+ ffi::Tensor tensor = ffi::Tensor::FromEnvAlloc(TVMFFIEnvTensorAlloc, ...);
+
+which is equivalent to:
+
+.. code-block:: c++
+
+ at::Tensor tensor = at::empty(...);
+
+FromNDAlloc
+^^^^^^^^^^^
+
+:cpp:func:`tvm::ffi::Tensor::FromNDAlloc` can be used to create a tensor with
custom memory allocator. It's used by the kernel provider if they don't want to
rely on the framework tensor allocator. Instead, they provide their own custom
allocator and deleter for tensor allocation and free. However, the tensors
allocated by ``FromNDAlloc`` only retain the function pointer to its custom
deleter for deconstruction. The custom deletes are all owned by the kernel
library still. So it is important to make sure the loaded kernel library,
:py:class:`tvm_ffi.Module`, outlives the tensors allocated by ``FromNDAlloc``.
Otherwise, the function pointers to the custom deleter will be invalid. Here a
typical approach is to retain the loaded :py:class:`tvm_ffi.Module` globally or
for the period of time. But ``FromEnvAlloc`` is free of this issue, which is
more **recommended** in practice.
+
Review Comment:
updated
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
