Tensors are created with
AssignableTensorOp with several key attributes passed in as arguments:
constant: Immutable tensor that can be inlined.
persistent: Value is retained across computations (for example, weights).
trainable: Marker for a tensor that is meant to be retrieved as part of a variable list.
input: Tensor that allows for a value to be passed in during runtime.
For convenience, we provide several helper functions to define commonly used tensor configurations, as shown below.
||True||True||False||False||Constants specified during graph creation.|
||False||True||False||True||Used for input values, typically from host.|
||False||True||False||False||Persistent tensors, such as velocity in SGD.|
||False||True||True||False||Parameters that are updated during training.|
Basic tensor descriptions¶
In Intel® Nervana™ graph, we often have to reason about tensors before any computations or allocations are performed. For this reason, we use
tensor descriptions to hold enough metadata about tensors for analysis/simplification. Basic tensor descriptions only have shape and element type information. Although the shape is an ordered list of lengths, the order does not imply a particular layout/striding for the dimensions. The basic tensor descriptions, with restrictions on dimensions and striding, are appropriate for the basic operations that all Intel Nervana graph transformers must implement. They might also be useful for front ends that describe tensors by shape.
If we know the layout of a tensor, we can compute the layout of subsequent slices and reshapings. But in Intel Nervana graph, we only know the layout for the subset of tensors whose layout has been explicitly provided by the frontend. But we still need information about which tensors are views of each other, dimension lengths, alignment constraints, slicing, etc. We use
BasicTensorDescription to represent all the information the graph needs to know about tensors. This might vary during the transformation process. Little might be known about a tensor when it is first added to the graph, but by the time execution occurs, the tensor’s layout needs to be known.
Describes a tensor by its shape and element type.
- dtype: The dtype of the elements.
- rank: The number of dimensions.
- read_only: True for an r-tensor, False for an l-tensor.
- shape: An n-tuple of non-negative integers. The length of the tuple is the rank.
- layout: strides and offset, if known.
Every basic tensor-valued
Op corresponds to an r-tensor, meaning a tensor whose value can be on the right side of an assignment. Allocations correspond to l-tensors, which are tensors whose values can be assigned. Each tensor has a tensor description that describes the tensor and which is computed from the tensor descriptions of the parameters and arguments to the
During the transformation process, the tensor description can be augmented with additional information, such as a storage layout and storage assignment. The value of an
Op might be a different view of a tensor, in which case the sharing must be indicated in its tensor description. An
AssignableTensorOp is a special case of a tensor-valued
Op in that its tensor is an l-tensor. At the end of the transformation process, all tensor descriptions for l-tensors must contain enough information for them to be allocated.
Abstractly, an n-tensor is a map from an n-dimensional rectangle of non-negative integers to values of homogeneous type. In programming languages, there are two kinds of values, l-values and r-values. L-values can appear on the left side of an assignment and r-values can appear on the right side of an assignment. For example,
x can be an l-value or an r-value, while
x + y is an r-value. Likewise, if a tensor’s values are l-values, the tensor is an l-tensor, and if the tensor’s values are r-values, the tensors is an r-tensor.
For example, the tensor
x is an l-tensor since values can be assigned to its elements, as in
x[...] = y, while the tensor
x + y is an r-tensor because values cannot be assigned to it. An r-tensor only needs to be able to provide values; it does not need to store them. The tensor
x + y could produce the value for an index
i by providing
x[i] + y[i] every time it is needed, and a constant tensor could ignore the index and always produce the value.
y is a simple view of tensor
x if there is some index translation function
f such that
y[i] == x[f(i)] for all valid indices of
y. Reshaping and slicing are two examples of tensor operations that create views.
A complex view involves multiple tensors and index translation functions. If
x is a list of tensors, and
f a list of index translation functions, and there is a selection function
s such that after setting
y[i] == x[s(i)][f[s(i)](i)] for all valid indices
y and all values of
x[...]. In this case,
s selects a tensor and index translation function for that tensor. Padding is an example of a complex view, in which a region of the values come from some other tensor, and the remaining values come from zero tensors. Transformers introduce views as they rewrite more abstract operations as simpler operations available on backends.
We can convert an r-tensor to an l-tensor by allocating an l-tensor for it initializing it with the r-values. If the values at particular indices are going to be used multiple times, this can reduce computation. Not all r-tensors should be converted to l-tensors. If
y are compatible 1-tensors,
x - y is a r-tensor. If we only want to compute the L2 norm of
x - y we could use NumPy to compute the following:
def L2(x, y): t = x - y return np.dot(t.T, t)
Starting with the subtraction operation, NumPy first allocates a tensor for
t. Every element in
t is touched once, and pages in
t are modified as elements are written in. Furthermore, accessing all the elements of
t can potentially evict other tensors from various CPU caches.
Next, a view of
t.T is allocated by NumPy. The memory footprint of a view is tiny compared to tensors. Computing the dot product accesses every element of
t again. If
t is larger than the memory cache, the recently cached elements near the end of
t will be evicted so the ones near the beginning of
t can be accessed. Also, because NumPy’s dot operator does not function in place, it will also allocate another tensor for the output.
When the function returns, the garbage collector sees that the view
t.T and the tensor
t are no longer referenced and will reclaim them. All the cache locations displaced by
t are now unused. Furthermore, even though
t is unallocated memory according the the heap, paging still sees it as modified pages. The page needs to be written back to paging before the physical memory can be given to other virtual memory. Likewise, the memory caches see the memory as modified and will need to invalidate caches for other cores.
Compare this with the following function:
def L2(x, y): s = 0 for i in len(x): s = s + (x[i] - y[i])^2 return s
As in the previous function,
y will need to enter the cache, but there are no other tensors that need to be allocated, cached, and reclaimed, and there are no dirty pages to evict.
Dense L-Tensor Implementation¶
An L-tensor is typically represented as a contiguous region of memory and a mapping from the index to a non-negative integer offset into this memory. Essentially, every n-d tensor is a view of our memory, a 1-d linear tensor. An l-value is therefore the base address plus the index, adjusted for element size, and the r-value is the contents of the l-value. The n-d index mapping is characterized by an n-tuple of integers, called the stride, at an offset. The offset is added to the dot product of the strides and the n-tuple index to get the linear offset. If the linear tensor also has an n-tuple of integers, called the shape, bounds checking may be performed on the index. Sometimes it is important to align elements on particular memory boundaries. In this case, in addition to a shape we require an additional n-tuple called the size, which is greater than or equal to the shape to add padding for alignment.
There are many ways to map an index to a linear index that correspond to permutations of the stride n-tuple. Two common special cases are Row-major and column-major ordering.
In row-major ordering, the strides are listed in decreasing order and can be calculated using partial products of the allocated sizes for each dimension, multiplied from the right
For column-major ordering , the strides are in increasing order and are calculated by multiplying the sizes from the left. For example, if the sizes of the dimensions of a 3D-tensor are
(5, 3, 2), then the row-major strides would be
(6, 2, 1) and
(1, 5, 15) for column major-order.
If two elements of the stride, shape, and size are permuted, then the same linear index is given by permuting the index in the same way. For example, a transpose view just requires these permutations.
Views allow for simpler implementation of tensor operations. For example, consider implementing a subtraction operation for arbitrary n-tensors of the same shape. For an implemented directory, an n-tuple index iterator would need to be maintained. However, if the n-tuple iterator would iterate over the linearized indices in the same order for both tensors, we can consider the flattened tensor view versions of these two tensors and use a single integer iterator to walk through pairs of elements from each tensor using the same offset for each. This produces the same result as if we had iterated through the two tensors using multidimensional indexing, but can result in the element pairs being accessed in different orders. This is only possible if the tensors have the same layout and strides.