feat(dflash): Add DFlash support

docs/configuration/server.md

@@ -124,7 +124,7 @@ the values accepted by the bundled `tokenspeed-smg` package.
 | Parameter | Purpose |
 | --- | --- |
 | `--speculative-config` | JSON speculative decoding configuration. |
-| `--speculative-algorithm` | Speculative algorithm, such as `EAGLE3` or `MTP`. |
+| `--speculative-algorithm` | Speculative algorithm, such as `EAGLE3`, `MTP`, or `DFLASH`. |
 | `--speculative-draft-model-path` | Draft model path or repo ID. |
 | `--speculative-draft-model-quantization` | Draft model quantization. Defaults to `unquant`. |
 | `--speculative-num-steps` | Number of draft model steps. Defaults to `3`. |

docs/recipes/models.md

@@ -33,6 +33,38 @@ tokenspeed serve nvidia/Kimi-K2.5-NVFP4 \
 For K2.6, keep the same parameter shape and change the checkpoint and parser
 only if the model card requires a different value.
 
+To enable a compatible DFlash draft model, keep the target launch shape and add
+the draft model path plus DFlash speculative decoding options:
+
+```bash
+tokenspeed serve nvidia/Kimi-K2.6-NVFP4 \
+  --served-model-name kimi-k2.6 \
+  --trust-remote-code \
+  --max-model-len 262144 \
+  --kv-cache-dtype fp8 \
+  --quantization nvfp4 \
+  --tensor-parallel-size 4 \
+  --enable-expert-parallel \
+  --chunked-prefill-size 8192 \
+  --max-num-seqs 256 \
+  --attention-backend tokenspeed_mla \
+  --moe-backend flashinfer_trtllm \
+  --reasoning-parser kimi_k25 \
+  --tool-call-parser kimik2 \
+  --speculative-algorithm DFLASH \
+  --speculative-draft-model-path /path/to/kimi-k2.6-dflash \
+  --speculative-num-draft-tokens 8 \
+  --speculative-num-steps 7 \
+  --drafter-attention-backend fa4 \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
+Known limitation: native TokenSpeed DFlash currently uses full-history draft
+attention. It does not yet expose an equivalent of SGLang's
+`--speculative-dflash-draft-window-size`; add such a flag before relying on
+bounded draft attention for long-context deployments.
+
 ## GLM5 / GLM5.2
 
 GLM5 launches usually need remote code, long context, expert parallelism, FP8 KV

python/tokenspeed/runtime/execution/cuda_graph_wrapper.py

@@ -430,6 +430,11 @@ def run_once():
         torch.cuda.synchronize()
         dist.barrier()
 
+        # Warmups can switch a backend back to eager metadata objects. Restore
+        # the graph-backed metadata immediately before capture so replay-time
+        # metadata refreshes update the same tensors recorded by the graph.
+        self._init_capture_metadata(bs)
+
         # Fill sampler buffers OUTSIDE the capture so RNG ops aren't recorded.
         if self.sampling_backend is not None:
             self.sampling_backend.prepare_capture(