docs: sync all documentation with current codebase

- remove GenerationRequest and generate_with_request references (class deleted)
- document cuda>flash>torch default priority and FlashAttnBackend
- add ASTR_BACKEND env var to backend docs, TorchNativeBackend (default) → (fallback)
- fix JsonlStore transform routing → DatasetFactory ownership
- fix CudaBackend fallback chain description (FlashAttn → TorchNative)
- add FlashAttnBackend to architecture strategy table
- add router_stats to DecoderOutput/FFNOutput TypedDict diagrams
- add decode_o_part/ml_part/decode_out to KVCache diagram
- add --append_eos/--no-append_eos to IFD evaluation parameter table
- update get-started CUDA kernel note (no longer requires explicit attn_backend activation)
- fix python -m scripts.tools.server (no __init__.py) → direct script call

docs/developer/cuda_kernels.md

@@ -86,8 +86,12 @@ Each kernel in `astrai/extension/lib` is compiled as an independent pybind11 mod
 `astrai/extension/attention_backend.py` provides the backend abstraction:
 
 - **`AttentionBackend`** (ABC): `fwd_decode` / `fwd_prefill` abstract methods, `forward` dispatches by q_len
-- **`TorchNativeBackend`**: SDPA with indirect KV cache gather (default)
-- **`CudaBackend`**: CUDA kernel dispatch — decode via `attn_paged_decode` (page_size=1), prefill via `attn_paged_prefill` (ragged batch, `qo_indptr` + `kv_indptr`)
+- **`CudaBackend`**: CUDA kernel dispatch — decode via `attn_paged_decode` (page_size=1), prefill via `attn_paged_prefill` (ragged batch, `qo_indptr` + `kv_indptr`). Default on GPU.
+- **`FlashAttnBackend`**: Optional flash-attn dispatch with `flash_attn_with_kvcache` fast path.
+- **`TorchNativeBackend`**: SDPA with indirect KV cache gather (always-available fallback)
+
+Default priority: cuda > flash > torch. Set ``ASTR_BACKEND=cuda|torch_native|flash``
+to override the default.
 
 Select a backend via context manager (mirrors `torch.nn.attention.sdpa_kernel`):
 
@@ -98,7 +102,7 @@ with attn_backend(ATTN_BACKEND.CUDA):
     engine.generate("hello")
 ```
 
-`CudaBackend` falls back to `TorchNativeBackend` when a kernel is not available.
+`CudaBackend` falls back to `FlashAttnBackend` (when flash-attn is installed and supports the input dtype) or `TorchNativeBackend` otherwise.
 
 ### Rotary Backend