Stream weights to the GPU when loading a model#7994
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mattsoulanille merged 16 commits intotensorflow:masterfrom Nov 28, 2023
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Stream weights to the GPU when loading a model#7994mattsoulanille merged 16 commits intotensorflow:masterfrom
mattsoulanille merged 16 commits intotensorflow:masterfrom
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Nov 16, 2023
tfjs-core/src/io/http.ts
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| return fetchURLs; | ||
| } | ||
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| // private get loadOptions(): LoadOptions { |
| * Whether to stream the model directly to the backend or cache all its | ||
| * weights on CPU. Useful for large models. | ||
| */ | ||
| streamWeights?: boolean; |
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this flag is the same name as the function streamWeights?: () => ReadableStream<ArrayBuffer>?
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Yes, but they're part of different interfaces. One is on the ModelArtifacts and is the actual function that, when called, will start streaming the weights. It's internal, and users shouldn't really need to access it.
The other is on the LoadOptions interface, and it's used to configure whether the model loader should stream the weights or load them normally. It's exposed to the user.
I can rename the one on ModelArtifacts to something like startWeightsStream or streamWeightsData.
This was referenced May 31, 2024
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When downloading model weight data, slice it into weight tensors and push them to the GPU eagerly. This avoids storing an extra copy of the weights on CPU, allowing for larger models (1.3B to possibly ~6.7B or larger) to be loaded without causing a V8 OOM crash.
When streaming the weights, check
CPU_HANDOFF_SIZE_THRESHOLDorWEBGPU_CPU_HANDOFF_SIZE_THRESHOLDto determine whether the weight should be sent to GPU or remain on CPU.This feature is guarded by the
streamWeightsoption inLoadOptions. Since most of TFJS's graph model saving relies on the CPU copy of the model, model saving is disabled when the model was streamed (i.e. it will throw an error since the weights ArrayBuffer is missing).To see the logs from the Cloud Build CI, please join either our discussion or announcement mailing list.