Refactor: restructure repository to focus on kt-kernel and KT-SFT modulesq recon (#1581)

* refactor: move legacy code to archive/ directory

  - Moved ktransformers, csrc, third_party, merge_tensors to archive/
  - Moved build scripts and configurations to archive/
  - Kept kt-kernel, KT-SFT, doc, and README files in root
  - Preserved complete git history for all moved files

* refactor: restructure repository to focus on kt-kernel and KT-SFT modules

* fix README

* fix README

* fix README

* fix README

* docs: add performance benchmarks to kt-kernel section

Add comprehensive performance data for kt-kernel to match KT-SFT's presentation:
- AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch)
- Prefill phase: up to 20× speedup vs baseline
- Decode phase: up to 4× speedup
- NUMA optimization: up to 63% throughput improvement
- Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8

Source: https://lmsys.org/blog/2025-10-22-KTransformers/

This provides users with concrete performance metrics for both core modules,
making it easier to understand the capabilities of each component.

* refactor: improve kt-kernel performance data with specific hardware and models

Replace generic performance descriptions with concrete benchmarks:
- Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX
- Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B)
- Show detailed metrics: total throughput, output throughput, concurrency details
- Match KT-SFT presentation style for consistency

This provides users with actionable performance data they can use to evaluate
hardware requirements and expected performance for their use cases.

* fix README

* docs: clean up performance table and improve formatting

* add pic for README

* refactor: simplify .gitmodules and backup legacy submodules

- Remove 7 legacy submodules from root .gitmodules (archive/third_party/*)
- Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11)
- Backup complete .gitmodules to archive/.gitmodules
- Add documentation in archive/README.md for researchers who need legacy submodules

This reduces initial clone size by ~500MB and avoids downloading unused dependencies.

* refactor: move doc/ back to root directory

Keep documentation in root for easier access and maintenance.

* refactor: consolidate all images to doc/assets/

- Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/
- Remove KT-SFT/assets/ (images already in doc/assets/)
- Update KT-SFT/README.md image references to ../doc/assets/
- Eliminates ~7.9MB image duplication
- Centralizes all documentation assets in one location

* fix pic path for README

archive/ktransformers/tests/dequant_gpu_t.py

@@ -0,0 +1,40 @@
+import os 
+os.environ["CUDA_VISIBLE_DEVICES"]="1"
+# add path
+import sys
+sys.path.append("../..")
+import pycuda.autoinit
+import pycuda.driver as cuda
+from pycuda.compiler import SourceModule
+import numpy as np
+from ktransformers.operators.linear import KTransformersLinear, KLinearMarlin
+from ktransformers.operators.experts import KTransformersExperts, KExpertsTorch
+from ktransformers.util.custom_loader import GGUFLoader, dequantize_q4_k_gpu, dequantize_q4_k
+import torch
+import KTransformersOps
+torch.set_default_dtype(torch.bfloat16)
+import time
+from transformers import (
+    AutoConfig,
+)
+
+gguf_config = GGUFLoader("/data/Qwen2-57B-A14B-Instruct-GGUF/q4_k_m")
+model_name = "/data/Qwen2-57B-A14B-Instruct"
+key = "blk.0."
+target = "ffn_up_exps.weight"
+
+data = gguf_config.get_mmap_tensor(key + target)
+
+_, factors, offsets, qs1, qs2= dequantize_q4_k(data)
+factors_cpu = torch.from_numpy(factors)
+offsets_cpu = torch.from_numpy(offsets)
+qs1_cpu = torch.from_numpy(qs1)
+qs2_cpu = torch.from_numpy(qs2)
+
+
+_, factors, offsets, qs1, qs2 = dequantize_q4_k_gpu(data)
+
+print(torch.allclose(factors.cpu(), factors_cpu))
+print(torch.allclose(offsets.cpu(), offsets_cpu))
+print(torch.allclose(qs1.cpu(), qs1_cpu))
+print(torch.allclose(qs2.cpu(), qs2_cpu))