docs: fix stale developer documentation claims

- Move task_alloc/task_free/task_extend/task_cached/task_record_hashes and bind from the PagePool card to a new TaskCacheManager card matching pool.py
- Drop the nonexistent Executor tokenizer attribute and association, add task_cache instead
- Add AllocationStrategy/ContiguousStrategy/PagedStrategy cards and point Allocator/RadixCache composition at PagedStrategy
- Add TaskCacheManager and the allocation strategies to the module overview, add _task_cache to InferenceScheduler
- Fix the design-pattern count in the table of contents (15 -> 16)
- Rewrite the FlashAttnBackend class docstring: packed decode gathers flat K/V via req_to_token and calls flash_attn_varlen_func; dense prefill uses flash_attn_func (no flash_attn_with_kvcache exists)
- Apply the same correction to the backend bullets in internals.md and cuda_kernels.md
- Rename the stale fp8_mma_test.cu reference to fp8_test.cu in cuda_kernels.md

docs/developer/internals.md

@@ -185,7 +185,7 @@ The extension package separates mechanism from policy:
 Attention computation is decoupled from the model via `AttentionBackend` ABC (`astrai/extension/backend/attention.py`):
 
 - **`CudaBackend`** (default when supported): decode path uses `attn_paged_decode` with `page_size=1` (the `req_to_token` table serves as the page table, each token slot is a single-token "page"); prefill path uses the ragged-batch `attn_paged_prefill` (addresses each request via `qo_indptr` + `kv_indptr` directly against the flat pool).
-- **`FlashAttnBackend`**: optional flash-attn dispatch with `flash_attn_with_kvcache` fast path for contiguous cache; falls back to KV gather + `flash_attn_func`.
+- **`FlashAttnBackend`**: optional flash-attn dispatch; inference paths gather flat K/V from the pool via `req_to_token` and call `flash_attn_varlen_func` over the ragged batch (fp16/bf16 only); dense mask-free training calls use `flash_attn_func`.
 - **`TorchNativeBackend`** (always-available fallback): writes K/V to cache, gathers via `req_to_token` indirect indexing, calls `F.scaled_dot_product_attention`.
 - The `attention(...)` entry point uses cuda > flash > torch priority and chooses another compatible backend when an automatically selected backend cannot handle a call.
 - Resolution precedence is: explicit `attn_backend(...)` context > `ASTR_BACKEND` env > default. An explicit `attn_backend(...)` selection is strict (incompatible calls raise); `ASTR_BACKEND` is a default-level override that falls back to a compatible backend when incapable. Training calls (`fwd=None`, no KV cache) resolve by capability: the CUDA cache kernels cannot run without a cache, so they fall back to flash (mask-free/causal calls only) and finally to torch SDPA.