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Note that this module-server structure applied over the module hierarchy endows the library with flexibility in dealing with a variety of use circumstances, reminiscent of supporting module reuse, supporting arbitrary mannequin architectures and framework APIs, conditional execution, and handling completely different pipeline schedules easily, in sharp distinction to other implementations of pipeline parallelism. An execution server consists of an enter queue, a server for the queue (executed on essential thread), a set of worker threads (Python threads), and a set of modules assigned to the server by the partitioning algorithm. 3) auto-partitioning algorithm mechanically locations modules that share parameters on the identical system, (4) re-used modules could be executed just by sending a number of requests to the machine that holds the module. EXECUTING state at a time. Moreover, at any point in time, only one thread per rank (across most important thread and worker threads) actively executes, whereas the remaining wait. The duties requested from an execution server, in addition to responses to the requests made by the server, are all enqueued within the input queue.
An instance module graph and partition is provided in Figure 2. Note that there is no assumption on any explicit computational graph structure in this view of the model, which is why the pipeline parallelism framework is generic. Further, in contrast to different implementations of pipeline parallelism, the library doesn’t view the mannequin as a flat sequence of phases (for example, a sequence of layers the place certain blocks of layers are assigned to particular devices), however as a hierarchy of modules based mostly on the existing hierarchy in mannequin definition. Seek advice from Appendix D for example partitions over this module hierarchy. 0 moreover enqueues a brand new execution request to itself, corresponding to the execution of the highest-stage smp.step node, which consists of the contents of the smp.step perform. §4.5; for now, we assume the module assignment is given. From that time on, execution follows the request-response paradigm. Pipelining across microbatches is achieved by splitting the enter tensors to smp.step-perform across the batch dimension, and initiating microbatch execution requests at totally different instances (See §4.3).
Despite this, SageMaker model parallelism is designed to maintain a lot of the core framework features and APIs, so that almost all existing coaching scripts will be supported with out significant refactors. Pipeline parallelism on SageMaker is just not limited to explicit model definition APIs akin to nn.Sequential, in contrast to the present pipeline parallelism implementations. Module/parameter re-use is automatically dealt with, without requiring particular enter or code change from the consumer. This is often a problem since different versions of a parameter residing on completely different units need to be stored synchronized. The library achieves this by putting the modules that share a parameter at the identical machine routinely, removing the need for synchronization. This permits flexibility to support various model architectures that isn’t limited to a single contiguous block of isomorphic layers, as might be defined in the third point. Conditional execution (if/else blocks) is supported, thanks to the module-server architecture, which doesn’t have a priori assumptions in regards to the architecture or control circulate. Training steps with the library are semantically and mathematically equal to the unique coaching step, hence it does not introduce model accuracy penalties.