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My heavily engineered repo; https://github.com/AbstractEyes/pytorch-parallel-compiler has been directly integrated into the geofractal repo for v1.2, if you use the geofractal repo be sure to pull for potential performance increases.
The WideRouter will enable multiple core new features; the predominant two for our next experiment are as follows.
1. Directly integrated multi-opinion constellation structures. This will enable dynamic compiled expansions internally within the structure for huge performance gains.
2. Controllable stage-by-stage compilation. Each stage can be compiled or not. SVD being notoriously non-compiler friendly due to the linalg.egens, I will be addressing this particular function DIRECTLY soon. There will be no quarter for graph breaks.
If the WideRouter causes any major bugs or breaks with your code, bad calculations, incorrect deviated gradients, twisted or contorted dtype outputs, or any major compilation errors; please don't hesitate to open a pull request. Claude and I will abruptly solve any major issues.
Once everything is perfectly in-line and the graph matches, the transformer will have massive geometric performance boosts for huge structural basins with multiple layers of depth.
I will be addressing the linalg.eig+eigh directly in conjunction with multiple argsort functions that are causing huge performance dips. As well as addressing every single use of .item() that can present itself in the compiler's path.
After this, the ensemble topological transformer will be a-go. Which will enable quaternion, FlowMagnitude, FlowAlignment, FlowVelocity, FlowVelocityQuaternion, FlowVelocityOrbital, FlowVelocityPentachoron, and multiple other flow matching systems that will improve performance by dominating amounts inline with minimal overhead cost due to the precomputed geometric structure.
The ensembles will feature multiple simultaneous batched and segmented forms of learning meant to train the oscillation omega predictor "Beatrix".
The WideRouter will enable multiple core new features; the predominant two for our next experiment are as follows.
1. Directly integrated multi-opinion constellation structures. This will enable dynamic compiled expansions internally within the structure for huge performance gains.
2. Controllable stage-by-stage compilation. Each stage can be compiled or not. SVD being notoriously non-compiler friendly due to the linalg.egens, I will be addressing this particular function DIRECTLY soon. There will be no quarter for graph breaks.
If the WideRouter causes any major bugs or breaks with your code, bad calculations, incorrect deviated gradients, twisted or contorted dtype outputs, or any major compilation errors; please don't hesitate to open a pull request. Claude and I will abruptly solve any major issues.
Once everything is perfectly in-line and the graph matches, the transformer will have massive geometric performance boosts for huge structural basins with multiple layers of depth.
I will be addressing the linalg.eig+eigh directly in conjunction with multiple argsort functions that are causing huge performance dips. As well as addressing every single use of .item() that can present itself in the compiler's path.
After this, the ensemble topological transformer will be a-go. Which will enable quaternion, FlowMagnitude, FlowAlignment, FlowVelocity, FlowVelocityQuaternion, FlowVelocityOrbital, FlowVelocityPentachoron, and multiple other flow matching systems that will improve performance by dominating amounts inline with minimal overhead cost due to the precomputed geometric structure.
The ensembles will feature multiple simultaneous batched and segmented forms of learning meant to train the oscillation omega predictor "Beatrix".