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/configs/config.yaml

@@ -0,0 +1,73 @@
+log:
+  dir: "logs"
+  file: "lexllama.log"
+  #log level: debug, info, warn, error, crit
+  level: "debug"
+  backup_count: -1
+
+server:
+  ip: 0.0.0.0
+  port: 10002
+
+db:
+  type: "sqllite"
+  database: "server.db"
+  host: "./"
+  pool_size: 10
+
+user:
+  secret_key: "981f1dd2a44e27d68759d0252a486568ed43480b4e616a26e3af3709c3a7ce73"
+  algorithm: "HS256"
+
+model:
+  # type: transformers
+  type: balance_serve
+  # type: ktransformers
+
+  name: DeepSeek-Coder-V2-Instruct
+  path: deepseek-ai/DeepSeek-V2-Lite-Chat
+  gguf_path: /mnt/data/models/Smallthinker-21B
+
+  device: cuda:0
+  cache_lens: 16384
+  max_new_tokens: 500
+web:
+  mount: False
+  open_cross_domain: True
+
+ext:
+  cpu_infer: 10
+
+long_context:
+  max_seq_len: 32000
+  block_size: 128
+  local_windows_len: 4096
+  second_select_num: 32
+  anchor_type: DYNAMIC
+  kv_type: FP16
+  dense_layer_num: 2
+  anchor_num: 1
+  preselect_block: True
+  head_select_mode: SHARED
+  preselect_block_count: 32
+  layer_step: 1
+  token_step: 
+
+local_chat:
+  prompt_file: ""
+
+async_server:
+  sched_strategy: "FCFS"
+  sched_port: 56441
+  sched_metrics_port: 54321
+  kvc2_metrics_port: 54391
+  max_batch_size: 4  # decode count + prefill count, in one mini batch
+
+attn:
+  page_size: 256
+  chunk_size: 256
+kvc2:
+  gpu_only: true 
+  utilization_percentage: 1.0
+  cpu_memory_size_GB: 500
+  disk_path: /home/wjh/kvc