Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

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File family: gpu_pattern_analyzer, compression tests, reports.

What it does:
* Scans real tensors (weights, activations, etc.) and measures: - How many zeros - How sparse the matrices are - How many distinct values exist (for quantized models) - How similar batches are across time
* Generates reports (compression_report, JSON stats) that show: - Expected compression ratios for different tensor types - Which strategies work best (zero-run, sparse indices, dictionaries, deltas, LoreToken patterns)

This is the “scout” stage: before you hook anything in production, you know exactly where the GPU is wasting memory.

===== File family: several ''_cuda_hook and ''_interceptor C/C++ files + compiled .so libraries. =====

High level behavior:
* Runs in userspace (no kernel or driver hacks).
* Loaded with mechanisms like LD_PRELOAD so that calls such as: - cudaMalloc - cudaFree - cudaMemcpy / cudaMemcpyAsync
* are intercepted by your wrapper first.

The wrapper can:
# Log and pass calls through unchanged (transparent mode).
# Replace raw allocations with compressed buffers.
# Swap in optimized decode logic when data is read back.

Crucially: if anything looks unsafe, it can fall back to normal CUDA behavior, so worst case you lose compression, not stability.

Think of it as a shim library that quietly “shrinks” tensors behind the scenes.

===== File family: loretoken_gpu_compressor, lt_decode*.cu, lt_pack.py, etc. =====

This is where the actual compression strategies live. From the docs and report, it supports:
* Zero-run encoding - Extremely effective on ReLU activations that are mostly zero.
* Sparse matrix compression - Store only non-zero values + indices for pruned or sparse layers.
* Value dictionary compression - If a tensor uses a small set of values (quantized weights), store the dictionary + small indices.
* Batch delta compression - For similar inputs in a batch, store a base sample and small deltas instead of each full tensor.
* LoreToken semantic patterns - This is the special sauce: - Certain known structures / layouts (e.g., standard transformer blocks, repeated KV-cache patterns) can be represented using LoreToken-style codes rather than raw floats. - That turns big, regular patterns into tiny semantic markers that decode back into tensors when needed.

The compressor tools:
* Feed synthetic and real tensors through each strategy.
* Measure: - Compression ratio - Encode/decode time - Overall throughput impact
* Write everything to logs and JSON so you can see which strategy wins for each tensor type.

===== File family: gpu_safety_system, safe_production_hook, logs. =====

You’ve clearly built this with “I don’t want to brick my GPU” in mind:
* Safety system monitors: - CUDA error codes - Memory mismatches / size discrepancies - Performance regressions
* Production hook variants: - A “minimal” hook that only intercepts and logs. - A “safe production” hook that applies only proven-safe strategies. - Immediate fallback to plain CUDA if any check fails.

In other words, there’s a clear upgrade path:
# Log-only, zero-risk mode.
# Limited compression in safe regions (e.g., ReLU activations, known sparse blocks).
# Full experimental mode here in the lab.

If something goes wrong, you don’t corrupt model weights—you just lose the compression for that run.

===== File family: baseline & performance tests, decode+GEMM CUDA files, quick blob tests, etc. =====

They do things like:
* Baseline benchmarking - Measure pure 3090 performance before any hooks: - Bandwidth - Typical inference/training loops - Behavior under memory pressure
* Decode + GEMM benchmarks - Check the cost of “decompress → matrix multiply” vs “just multiply the full tensor.” - Goal: show that the overhead of decompression is smaller than the savings from moving less data.
* Real blob harnesses - Feed real model blobs (weights, KV cache dumps, activation snapshots) through the pipeline. - Verify bit-exact round-trip: original == decompress(compress(original)).

All of this produces human-readable logs (compression_report, logs directory) with stats like:
* Number of tensors compressed
* Total bytes saved
* Time spent compressing/decompressing
* Speedup vs. baseline

So it’s not just “the math says this will be faster”; you’ve actually instrumented the whole thing.