[BugFix] Support setting tp=1 for the Eagle draft model to take effect (#6097)

According to the official documentation, the parameter
"draft_tensor_parallel_size": 1 is supposed to be applied to the Eagle3
model. However, based on actual debugging, it was found that the number
of tensor parallelisms (tp) of the Eagle model is consistent with that
of the target model. The setting of tp for the draft model did not take
effect as expected.

**Note:** This feature has not been superimposed and tested with `sp`
and `dp`. It will be adapted later
No
```python
from vllm import LLM, SamplingParams

def main():
    prompts = [
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
    llm = LLM(
            model="meta-llama/Llama-3.1-8B-Instruct",
            tensor_parallel_size=4,
            gpu_memory_utilization=0.9,
            enforce_eager=True,
            speculative_config={
                "method": "eagle3",
                "model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
                "draft_tensor_parallel_size": 1,
                "num_speculative_tokens": 3,
            },
        )
    outputs = llm.generate(prompts, sampling_params)
    print(f"Outputs: {outputs}")
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```

Fixes vllm-project/vllm#31345

### What this PR does / why we need it?

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
d68209402d

Signed-off-by: zhaomingyu <zhaomingyu13@h-partners.com>
Co-authored-by: drslark <slarksblood@qq.com>

tests/e2e/singlecard/spec_decode/test_mtp_eagle_correctness.py

@@ -23,6 +23,7 @@
 from __future__ import annotations
 
 import os
+from typing import Union
 
 import pytest
 from vllm import SamplingParams
@@ -124,11 +125,11 @@ def test_deepseek_mtp_correctness(model_name: str, num_speculative_tokens: int,
 @pytest.mark.parametrize("method", ["eagle", "eagle3"])
 @pytest.mark.parametrize("disable_padded_drafter_batch", [True, False])
 @pytest.mark.parametrize("async_scheduling", [True, False])
-def test_llama_qwen3_eagle_correctness(model_name: str, model_name_main: str,
-                                       num_speculative_tokens: int,
-                                       method: str,
-                                       disable_padded_drafter_batch: bool,
-                                       async_scheduling: bool):
+@pytest.mark.parametrize("draft_tensor_parallel_size", [None, 1])
+def test_llama_qwen3_eagle_correctness(
+        model_name: str, model_name_main: str, num_speculative_tokens: int,
+        method: str, disable_padded_drafter_batch: bool,
+        async_scheduling: bool, draft_tensor_parallel_size: Union[None, int]):
 
     example_prompts = [
         "Hello, my name is",
@@ -163,6 +164,8 @@ def test_llama_qwen3_eagle_correctness(model_name: str, model_name_main: str,
                         "method": method,
                         "model": model_name,
                         "num_speculative_tokens": num_speculative_tokens,
+                        "draft_tensor_parallel_size":
+                        draft_tensor_parallel_size,
                         "max_model_len": 128,
                         "draft_vocab_size": 128256,
                     },

tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 import math
 import os
 import random
-from typing import Any
+from typing import Any, Union
 
 import pytest
 from transformers import AutoTokenizer
@@ -267,9 +267,11 @@ def test_suffix_acceptance(
 
 
 @pytest.mark.parametrize("use_eagle3", [True], ids=["eagle3"])
+@pytest.mark.parametrize("draft_tensor_parallel_size", [None, 1])
 def test_eagle_logprobs(
     model_name: str,
     use_eagle3: bool,
+    draft_tensor_parallel_size: Union[None, int],
 ):
     prompt = {"role": "user", "content": "Hello world " * 10}
     sampling_params = SamplingParams(temperature=0,
@@ -296,6 +298,7 @@ def test_eagle_logprobs(
                 "method": "eagle3" if use_eagle3 else "eagle",
                 "model": spec_model_name,
                 "num_speculative_tokens": 2,
+                "draft_tensor_parallel_size": draft_tensor_parallel_size,
                 "max_model_len": 128,
             },
             max_model_len=128,
@@ -321,11 +324,13 @@ def test_eagle_logprobs(
 
 @pytest.mark.parametrize("method", MODELS.keys())
 @pytest.mark.parametrize("num_speculative_tokens", [3])
+@pytest.mark.parametrize("draft_tensor_parallel_size", [None, 1])
 @pytest.mark.parametrize("disable_padded_drafter_batch", [True, False])
 @pytest.mark.parametrize("async_scheduling", [True, False])
 def test_llama_qwen_eagle_acceptance(
     method: str,
     num_speculative_tokens: int,
+    draft_tensor_parallel_size: Union[None, int],
     disable_padded_drafter_batch: bool,
     async_scheduling: bool,
 ):
@@ -376,6 +381,7 @@ def test_llama_qwen_eagle_acceptance(
     speculative_config = {
         "method": method,
         "num_speculative_tokens": num_speculative_tokens,
+        "draft_tensor_parallel_size": draft_tensor_parallel_size,
         "disable_padded_drafter_batch": disable_padded_drafter_batch,
         "model": spec_model_name,
     }

tests/ut/spec_decode/test_eagle_proposer.py

@@ -27,6 +27,8 @@ class TestEagleProposerInitialization(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
         self.vllm_config.model_config.uses_mrope = False
+        self.vllm_config.parallel_config.tensor_parallel_size = 1
+        self.vllm_config.speculative_config.draft_tensor_parallel_size = 1
         self.vllm_config.speculative_config.num_speculative_tokens = 2
         self.vllm_config.speculative_config.speculative_token_tree = str([
             (i + 1) * (0, ) for i in range(2)
@@ -115,6 +117,8 @@ class TestEagleProposerLoadModel(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
         self.vllm_config.model_config.uses_mrope = False
+        self.vllm_config.parallel_config.tensor_parallel_size = 1
+        self.vllm_config.speculative_config.draft_tensor_parallel_size = 1
         self.vllm_config.speculative_config.num_speculative_tokens = 2
         self.vllm_config.speculative_config.speculative_token_tree = str([
             (i + 1) * (0, ) for i in range(2)
@@ -256,6 +260,8 @@ class TestEagleProposerDummyRun(TestBase):
         self.vllm_config.model_config.max_model_len = 2048
         self.vllm_config.model_config.uses_mrope = False
         self.vllm_config.model_config.use_mla = False
+        self.vllm_config.parallel_config.tensor_parallel_size = 1
+        self.vllm_config.speculative_config.draft_tensor_parallel_size = 1
         self.vllm_config.speculative_config.speculative_token_tree = str([
             (i + 1) * (0, ) for i in range(4)
         ])
@@ -370,6 +376,8 @@ class TestEagleProposerHelperMethods(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
         self.vllm_config.model_config.uses_mrope = False
+        self.vllm_config.parallel_config.tensor_parallel_size = 1
+        self.vllm_config.speculative_config.draft_tensor_parallel_size = 1
         self.vllm_config.speculative_config.num_speculative_tokens = 2
         self.vllm_config.speculative_config.speculative_token_tree = str([
             (i + 1) * (0, ) for i in range(2)

tests/ut/spec_decode/test_mtp_proposer.py

@@ -42,6 +42,9 @@ class TestMtpProposer:
         config.model_config.max_model_len = 2048
         config.model_config.uses_mrope = False
         config.model_config.hf_text_config = None
+        config.model_config.hf_config = None
+        config.parallel_config.tensor_parallel_size = 1
+        config.speculative_config.draft_tensor_parallel_size = 1
 
         config.load_config = None
 

vllm_ascend/spec_decode/eagle_proposer.py

@@ -115,6 +115,27 @@ class EagleProposer(VllmEagleProposer):
 
         self.use_sparse = hasattr(vllm_config.model_config.hf_text_config,
                                   "index_topk")
+        # NOTE:
+        # `draft_tensor_parallel_size` does not take effect for Eagle:
+        # the draft model uses the same TP size as the target model in practice.
+        # so we applied this patch to set tp=1 of draft model separately.
+        # Due to verification of `_verify_and_get_draft_tp` in vllm,
+        # the value of `draft_tensor_parallel_size` here will either be 1 separately
+        # or the same as target model.
+        # TODO(zhaomingyu13): If we want to adapt to the case where draft model tp
+        # is not 1 and differs from target model, this part should be rewritten.
+        if (vllm_config.parallel_config.tensor_parallel_size
+                != self.speculative_config.draft_tensor_parallel_size):
+            tp_group = init_model_parallel_group(
+                [[get_world_group().rank]],
+                get_world_group().rank,
+                torch.distributed.get_backend(get_world_group().device_group),
+                use_message_queue_broadcaster=True,
+                group_name="tp",
+            )
+            self.tp_group_context = patch_tensor_parallel_group(tp_group)
+        else:
+            self.tp_group_context = nullcontext()
 
         self.use_cuda_graph = (self.runner._use_aclgraph()
                                and not self.speculative_config.enforce_eager

vllm_ascend/worker/model_runner_v1.py

@@ -170,6 +170,10 @@ def graph_capture(device: torch.device):
         yield graph_capture_context
 
 
+def get_tp_context(drafter):
+    return getattr(drafter, "tp_group_context", nullcontext())
+
+
 class ExecuteModelState(NamedTuple):
     """Ephemeral cached state transferred between execute_model() and
     sample_tokens(), after execute_model() returns None."""
@@ -2339,7 +2343,8 @@ class NPUModelRunner(GPUModelRunner):
                 model_register(self.model, self.model_config)
             if self.drafter:
                 logger.info("Loading drafter model...")
-                self.drafter.load_model(self.model)
+                with get_tp_context(self.drafter):
+                    self.drafter.load_model(self.model)
                 if self.use_aux_hidden_state_outputs:
                     self.model.set_aux_hidden_state_layers(
                         self.model.get_eagle3_aux_hidden_state_layers())
@@ -2715,11 +2720,15 @@ class NPUModelRunner(GPUModelRunner):
         kernel_block_sizes = []
         for kv_cache_group_id, kv_cache_group in enumerate(
                 kv_cache_config.kv_cache_groups):
-
-            if isinstance(kv_cache_group.kv_cache_spec,
-                          EncoderOnlyAttentionSpec):
+            kv_cache_spec = kv_cache_group.kv_cache_spec
+            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
+                # All layers in the UniformTypeKVCacheSpecs have the same type,
+                # Pick an arbitrary one to dispatch.
+                kv_cache_spec = next(
+                    iter(kv_cache_spec.kv_cache_specs.values()))
+            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
                 continue
-            elif isinstance(kv_cache_group.kv_cache_spec, AttentionSpec):
+            elif isinstance(kv_cache_spec, AttentionSpec):
                 # This is an attention backend that supports virtual
                 # block splitting. Get the supported block sizes from
                 # the backend.