We present GSPMD, an automatic, compiler-based parallelization system for common machine learning computation graphs. It allows users to write programs in the same way as for a single device, then give hints through a few annotations on how to distribute tensors, based on which GSPMD will parallelize the computation. Its representation of partitioning is simple yet general, allowing it to express different or mixed paradigms of parallelism on a wide variety of models.GSPMD infers the partitioning for every operator in the graph based on limited user annotations, making it convenient to scale up existing single-device programs. It solves several technical challenges for production usage, such as static shape constraints, uneven partitioning, exchange of halo data, and nested operator partitioning. These techniques allow GSPMD to achieve 50% to 62% compute utilization on 128 to 2048 Cloud TPUv3 cores for models with up to one trillion parameters.GSPMD produces a single program for all devices, which adjusts its behavior based on a run-time partition ID, and uses collective operators for cross-device communication. This property allows the system itself to be scalable: the compilation time stays constant with increasing number of devices.
Several pathogenic species are capable of heritable and reversible switching between two epigenetic states, "white" and "opaque." In, white cells are essentially sterile, whereas opaque cells are mating-proficient. Here, we interrogate the mechanism by which the white-opaque switch regulates sexual fecundity and identify four genes in the pheromone MAPK pathway that are expressed at significantly higher levels in opaque cells than in white cells. These genes encode the β subunit of the G-protein complex (), the pheromone MAPK scaffold (), and the two terminal MAP kinases (). To define the contribution of each factor to mating, white cells were reverse-engineered to express elevated, opaque-like levels of these factors, either singly or in combination. We show that white cells co-overexpressing, , and undergo mating four orders of magnitude more efficiently than control white cells and at a frequency approaching that of opaque cells. Moreover, engineered white cells recapitulate the transcriptional and morphological responses of opaque cells to pheromone. These results therefore reveal multiple bottlenecks in pheromone MAPK signaling in white cells and that alleviation of these bottlenecks enables efficient mating by these "sterile" cell types. Taken together, our findings establish that differential expression of several MAPK factors underlies the epigenetic control of mating in We also discuss how fitness advantages could have driven the evolution of a toggle switch to regulate sexual reproduction in pathogenic species.
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one using a spectrogram and the other using audio with lower fidelity. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era, but goes beyond to ground finegrained semantics of the prompt. Pretrained large language models play a key role in this storythey are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models.
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