Support weight sharing in QNN GPU#2325
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olive/passes/onnx/static_llm.py
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| decoder_config_extra=decoder_config_extra, | ||
| composite_components=None, | ||
| ) | ||
| return handler |
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jambayk
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Feb 11, 2026
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qti-mattsinc
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Mar 9, 2026
| "List of context lengths to generate static models QNN_GPU." | ||
| "If None or empty, falls back to single 'context_length'." | ||
| ), | ||
| ), |
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@vjatoth-qti, would the intended use of this in a recipe look like this?
"st": {
"type": "StaticLLM",
"batch_size": 1,
"context_lengths": [1, 64]
}Do we ever expect len(context_lengths) > 2? If not, it seems like we could follow the NPU strategy for StaticLLM, where "context_length": x always implies a hybrid AR1 + ARx model, and we wouldn't need a new "context_lengths" key.
qti-mattsinc
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Mar 9, 2026
| if str(device).lower() == "gpu": | ||
| provider_options["backend_path"] = "libQnnGpu.so" if platform.system() == "Linux" else "QnnGpu.dll" | ||
| if share_ep_contexts: | ||
| provider_options["enable_gpu_weight_sharing"] = "1" |
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Is enable_gpu_weight_sharing a supported provider option? The corresponding PR to onnxruntime-qnn (onnxruntime/onnxruntime-qnn#67) does not add it as one.
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