Fix more broken speculative decode tests (vllm-project#17450)

Signed-off-by: Huy Do <huydhn@gmail.com>

Author: huydhn

tests/spec_decode/e2e/test_medusa_correctness.py

@@ -205,7 +205,7 @@ def test_medusa_e2e_greedy_correctness_cuda_graph(
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
-        "block_size": 8,
+        "block_size": 16,
         # 2 for small prompt, 256//8 for generated.
         "num_gpu_blocks_override": 2 + 256 // 8,
         "max_model_len": (2 + 256 // 8) * 8,

tests/spec_decode/e2e/test_mlp_correctness.py

@@ -267,7 +267,7 @@ def test_mlp_e2e_seeded_correctness(vllm_runner, common_llm_kwargs,
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
-        "block_size": 8,
+        "block_size": 16,
         # 2 for small prompt, 256//8 for generated.
         "num_gpu_blocks_override": 2 + 256 // 8,
         "max_model_len": (2 + 256 // 8) * 8,
@@ -321,7 +321,7 @@ def test_mlp_e2e_greedy_correctness_with_preemption(
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
-        "block_size": 8,
+        "block_size": 16,
         # 2 for small prompt, 256//8 for generated.
         "num_gpu_blocks_override": 2 + 256 // 8,
         "max_model_len": (2 + 256 // 8) * 8,

tests/spec_decode/e2e/test_ngram_correctness.py

@@ -152,7 +152,7 @@ def test_ngram_e2e_greedy_logprobs(vllm_runner, common_llm_kwargs,
 @pytest.mark.parametrize(
     "common_llm_kwargs",
     [{
-        "block_size": 8,
+        "block_size": 16,
         # 2 for small prompt, 256//8 for generated.
         "num_gpu_blocks_override": 2 + 256 // 8,
         "max_model_len": (2 + 256 // 8) * 8,

vllm/spec_decode/multi_step_worker.py

@@ -51,9 +51,14 @@ def init_device(self) -> None:
     def set_include_gpu_probs_tensor(self) -> None:
         # Need include_gpu_probs_tensor for MultiStepWorker
         self.model_runner.sampler.include_gpu_probs_tensor = True
+        if hasattr(self.model_runner.model, "sampler"):
+            (self.model_runner.model.sampler.include_gpu_probs_tensor) = True
 
     def set_should_modify_greedy_probs_inplace(self) -> None:
         self.model_runner.sampler.should_modify_greedy_probs_inplace = True
+        if hasattr(self.model_runner.model, "sampler"):
+            (self.model_runner.model.sampler.should_modify_greedy_probs_inplace
+             ) = True
 
     @torch.inference_mode()
     def sampler_output(