Frameworks such as PyTorch or TensorFlow Eager nowadays have dynamic graph support, which is a fancy word to describe when a computation is carried out while constructing the computation graph.
If dynamic graph execution is just about executing a command when issuing it, this is not interesting. Dynamic graph execution by these frameworks also supports automatic differentiation. A good dynamic graph execution framework such as PyTorch enables easier debugging, more intuitive coding thus quicker experimentation cycle.
That has been said, there are a few drawbacks when you support dynamic graph execution naively.
- Limited optimization opportunities. With dynamic graph execution, the framework lacks the foresight, makes optimizations such as common sub-expression elimination or data layout optimization hard to implement;
- Unbounded memory usage. Since a dynamic graph execution engine needs to be able to differentiate arbitrary variables within the framework, a Wengert list (a tape) has to be kept. In many situations, to trim that list requires user attention otherwise the memory usage will continue to grow.
To work-around 1., mixing static graph execution with dynamic graph execution is desirable. However, that imposes its own set of problems: when a static graph contains a dynamic graph, and if the static graph contains a loop structure, the tape for the static graph need to cross into the dynamic graph to continue work. When a dynamic graph contains a static graph, the Wengert list (the tape) of the dynamic graph need to not only store the tensors, but also the static graph as a whole.
NNC’s dynamic graph execution design will attempt to address above problems with reasonable compromises. It borrows some good ideas from 10 years ago when I first started to implement ccv.
Naming The Variable
Like in most frameworks, dynamic graph execution in NNC operates at variables. Dynamic graph executes command on a set of input variables, writes the result to a set of output variables. Variables can be inspected anytime with
ccv_nnc_tensor_from_variable. The underlying tensor may not be allocated when the variable is created.
ccv_nnc_tensor_variable_t is an opaque structure and its inner work shouldn’t be of an interest to users.
Tracing The Operation
Frameworks such as PyTorch or TensorFlow Eager use the tape to record which operations are executed, and record the inputs / outputs along the way. automatic differentiation was implemented (its reverse mode) by walking back on the tape. This is simple to implement, and easier to support higher order gradients (by record another tape while walking back on the existing tape). This also makes optimizations on the automatic differentiation pass difficult because no data dependencies are specified. It is definitely possible to infer the data dependencies from the tape, and then employ optimizations or automatic parallelization. For mature framework such as TensorFlow, that kind of work is to reimplement some of the fundamental pieces of the software.
NNC uses its symbolic graph (Level-3 APIs) to trace the operation. When a command executed on a dynamic graph, we can figure out data dependencies with input variables (each input variable has a unique tensor symbol assigned). Even though the variables in the dynamic graph don’t follow the static single assignment (SSA) rule, the underlying tensors and tensor symbols do. Thus, through the normal execution of the dynamic graph, we have formed a full symbolic graph for later computation.
Upon automatic differentiation, no tape is used (or, the symbolic graph serves as an advanced tape). We simply leverage the ahead of time automatic differentiation system implemented in symbolic graph to optimize, compile and schedule the actual computation. That means any optimization techniques we implemented on Level-2 or Level-3 APIs will be available to dynamic graph as well.
Optimizations (Not Ready)
At this point, dynamic graph looks suspiciously like just another function dispatching mechanism. Ten years ago, when I started ccv, one of the motivation is to implement a function memorization technique, at that time, it is called cached image processing to workaround issues that in traditional computer vision pipeline, low level feature extraction passes often shared between different components (face detector, motion tracker etc.). In symbolic graph, this is trivially implemented as common sub-expression elimination (CSE). CSE cannot be implemented in dynamic graph because it cannot look ahead. However, the same memorization technique can be used to avoid duplicate computations.
In PyTorch, there is a need to
requires_grad such that the framework knows which variable should be discarded to save memory. If it is not done carefully, the memory usage can grow unbounded. Dynamic graph here provides
ccv_nnc_tensor_variable_free where when a tensor variable is freed, we will release its memory when it is safe. This method meant to hook up with object finalization methods in host languages (C++’s destructor, Objective-C’s
deinit in Swift,
finalize in Java,
tp_dealloc in Python).
Because symbolic graph formed from dynamic graph execution contains the proper data dependencies, memory reduction techniques such as automatic binomial checkpointing can be implemented with a change of cache eviction policy. If we implemented binomial checkpointing in symbolic graph as one optimization pass, we can also leverage that upon automatic differentiation in dynamic graph. The flexibility of sharing the same underlying infrastructure is very satisfying.
Interoperability (Not Ready)
There are some sticky issues with interoperability between static graph (the symbolic graph we formed by hand) with dynamic graph. The way they interoperate is through
CCV_NNC_CUSTOM_BACKWARD functions. When a static graph includes a dynamic graph, its tape needs to book-keeping for the dynamic graph. When a dynamic graph includes a static graph, it also needs to create a tape at that point for the execution. All these implies significant changes for the
ccv_nnc_tensor_tape_t implementation to accommodate these new requirements.
One of the major reason (or the reason) to use dynamic graph is its unparalleled debuggability. You can inspect tensors as you go in the code. However, this ability can be retained if the execution is separated from the dynamic graph forming. Your code can go a long way by forming computations and the underlying execution could be asynchronous. The synchronization happens only when you inspect these tensors to either debug, or practically, determine the control flow. This also offers limited look ahead ability to dynamic graph that enables more shared optimizations from Level-3 APIs. Implementing this is complicated. Synchronization point can easily turned into deadlock point, and the inter-play of static graph inside a dynamic graph inside a static graph could be more delicate. In a world where we modify languages to extract static graph (Swift for TensorFlow), the reason to have this kind of sophisticated dynamic graph implementation may be mooted.