Editing Openai/692db884-c2d8-800c-a357-aa2a0e684fbe (section)

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The hook is designed to sit in the execution path where:
* GPU memory is allocated / freed
* Tensors / buffers are written / read
* Certain operations that use those buffers are launched.

Instead of rewriting your model scripts, you attach this hook so that when your framework thinks it’s doing a normal CUDA call, it actually passes through the LoreToken layer first.

To your training script, nothing looks different:
* Same shapes
* Same dtypes
* Same API surface

Under the hood, the hook is managing compressed memory.

===== It doesn’t just naively zip everything. It uses several GPU-friendly strategies (taken from your compression report): =====
* Zero-run encoding – When activations come out of things like ReLU, huge slices are literal zero. Those get stored as “runs of zeros” instead of full arrays.
* Sparse matrix compression – Pruned / near-sparse weight matrices are stored in “only store non-zeros + where they go” formats.
* Value dictionaries / quantization – If many values repeat or fall into small numeric ranges, it can store them as small integer codes plus a tiny lookup table.
* Batch delta compression – When inputs or internal states across a batch are very similar, it stores “this one is like that one, plus small deltas.”
* LoreToken semantic compression – For structured stuff (prompts, KV cache with repeating patterns, etc.), it uses your LoreToken-style symbolic patterns instead of raw numeric soup.

Each of those is chosen because it:
* Compresses well
* Can be decoded insanely fast on GPU
* Fits the tensor layout the model expects.

===== Instead of leaving raw tensors sitting in VRAM, it: =====
# Stores a compact representation (compressed blobs + tiny metadata).
# Keeps track of what logical tensor each blob belongs to.
# Treats raw, expanded tensors as temporary views that can be reconstructed when needed.

So from CUDA’s perspective, you “have” a big tensor in VRAM, but really you have:
* A compressed blob
* Plus a decoder that can rebuild it fast enough that you don’t care.

===== When a kernel actually needs to do math on some data: =====
# The hook detects that the underlying tensor is stored in compressed form.
# It allocates a temporary working buffer (or reuses a small pool).
# Decodes the compressed representation into that buffer.
# Hands the pointer to CUDA as if it were the original, uncompressed tensor.
# After the operation, it can: - Recompress updated data, or - For read-only weights, just discard the temporary buffer.

This is how you get the equivalent of “24 GB card pretending to be 100+ GB”:
* You never keep all tensors fully expanded at once.
* You only expand what you’re actively touching right now.

===== From the summary + logs, there are clear safety rails: =====
* Error/Integrity checks If decode errors, mismatched sizes, or suspicious corruption show up more than a tiny threshold, the hook can: - Log it - Fail that path - Or fall back to an uncompressed / pass-through mode.
* Thermal & power guardrails It monitors temps, power draw, and performance. If compression/decoding stress pushes the GPU outside conservative limits, it can back off.
* Performance sanity checks If compression makes things slower than a defined “acceptable penalty,” it can selectively skip compressing certain tensors or disable itself.

All of that is there so that from the outside, your system either:
* Runs faster / with more effective VRAM, or
* Silently behaves like normal CUDA if something isn’t right.