[Spec Decode] DSpark confidence-scheduled verification

Implement host-side confidence scheduling for DSpark with automatically selected compact or masked verification, request-conditioned SPS profiling, and online temperature calibration.

Centralize scheduling and batch transformation in ConfidenceManager, use a fixed two-slot pinned-host copy ring, fuse compact batch rewrites, and keep shared model-runner and warmup hooks minimal.

Validated with focused CUDA tests, a GPU-sync-checked serving probe, GSM8K, and a 25-cell natural-EOS throughput matrix.

Co-authored-by: OpenAI Codex <codex@openai.com>

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

Author: LucasWilkinson

tests/evals/gsm8k/configs/DeepSeek-V4-Flash-DSpark-varlen-TP4.yaml

@@ -0,0 +1,22 @@
+model_name: "deepseek-ai/DeepSeek-V4-Flash-DSpark"
+accuracy_threshold: 0.92
+num_questions: 1319
+num_fewshot: 5
+startup_max_wait_seconds: 1800
+server_args: >-
+  --tokenizer-mode deepseek_v4
+  --trust-remote-code
+  --dtype bfloat16
+  --max-model-len 8192
+  --tensor-parallel-size 4
+  --enable-expert-parallel
+  --block-size 256
+  --gpu-memory-utilization 0.5
+  --kv-cache-dtype fp8
+  --max-num-batched-tokens 16384
+  --max-num-seqs 32
+  --speculative-config '{"method":"dspark",
+  "model":"deepseek-ai/DeepSeek-V4-Flash-DSpark",
+  "attention_backend":"FLASH_ATTN","num_speculative_tokens":7,
+  "draft_sample_method":"probabilistic","dspark_confidence_threshold":0.0,
+  "dspark_budget_frac":0.5,"confidence_based_verification":"auto"}'

tests/evals/gsm8k/configs/dspark_list.txt

@@ -0,0 +1 @@
+DeepSeek-V4-Flash-DSpark-varlen-TP4.yaml

tests/test_config.py

@@ -1572,6 +1572,62 @@ def test_draft_sample_method_gumbel_is_rejected():
         )
 
 
+def test_dspark_confidence_config_validation():
+    speculative_config = SpeculativeConfig(
+        method="ngram",
+        num_speculative_tokens=1,
+        dspark_confidence_threshold=0.25,
+        dspark_budget_frac=0.5,
+        confidence_based_verification="mask",
+    )
+    assert speculative_config.dspark_confidence_threshold == 0.25
+    assert speculative_config.dspark_budget_frac == 0.5
+    assert speculative_config.confidence_based_verification == "mask"
+    assert (
+        SpeculativeConfig(
+            method="ngram", num_speculative_tokens=1
+        ).confidence_based_verification
+        == "auto"
+    )
+    for mode in ("none", "off", "auto", "mask"):
+        assert (
+            SpeculativeConfig(
+                method="ngram",
+                num_speculative_tokens=1,
+                confidence_based_verification=mode,
+            ).confidence_based_verification
+            == mode
+        )
+    with pytest.raises(ValidationError, match="confidence_based_verification"):
+        SpeculativeConfig(
+            method="ngram",
+            num_speculative_tokens=1,
+            confidence_based_verification="compact",
+        )
+    assert (
+        SpeculativeConfig(
+            method="ngram", num_speculative_tokens=1
+        ).dspark_confidence_threshold
+        == 0.0
+    )
+
+    for threshold in (-0.1, 1.1):
+        with pytest.raises(ValueError, match="dspark_confidence_threshold"):
+            SpeculativeConfig(
+                method="ngram",
+                num_speculative_tokens=1,
+                dspark_confidence_threshold=threshold,
+            )
+
+    for budget_frac in (0.0, -0.1, 1.1):
+        with pytest.raises(ValueError, match="dspark_budget_frac"):
+            SpeculativeConfig(
+                method="ngram",
+                num_speculative_tokens=1,
+                dspark_budget_frac=budget_frac,
+            )
+
+
 def test_ir_op_priority_default():
     """Test that IR op priority defaults are set correctly."""
     from vllm.config.kernel import IrOpPriorityConfig