Revert "[BugFix] Support setting tp=1 for the Eagle draft model to take effect (#5519)"(#5902)

This reverts commit d886b81971. it breaks pd function

- vLLM version: v0.13.0
- vLLM main:
bde38c11df

Signed-off-by: zhaomingyu <zhaomingyu13@h-partners.com>

tests/e2e/singlecard/spec_decode/test_mtp_eagle_correctness.py

@@ -23,7 +23,6 @@
 from __future__ import annotations
 
 import os
-from typing import Union
 
 import pytest
 from vllm import SamplingParams
@@ -124,11 +123,11 @@ 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])
-@pytest.mark.parametrize("draft_tensor_parallel_size", [None, 1])
+def test_llama_qwen3_eagle_correctness(model_name: str, model_name_main: str,
-def test_llama_qwen3_eagle_correctness(
+                                       num_speculative_tokens: int,
-        model_name: str, model_name_main: str, num_speculative_tokens: int,
+                                       method: str,
-        method: str, disable_padded_drafter_batch: bool,
+                                       disable_padded_drafter_batch: bool,
-        async_scheduling: bool, draft_tensor_parallel_size: Union[None, int]):
+                                       async_scheduling: bool):
 
     example_prompts = [
         "Hello, my name is",
@@ -163,8 +162,6 @@ def test_llama_qwen3_eagle_correctness(
                         "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, Union
+from typing import Any
 
 import pytest
 from transformers import AutoTokenizer
@@ -217,11 +217,9 @@ 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,
@@ -248,7 +246,6 @@ 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,
@@ -274,13 +271,11 @@ 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,
 ):
@@ -331,7 +326,6 @@ 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

@@ -27,8 +27,6 @@ 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)
@@ -116,8 +114,6 @@ 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)
@@ -250,8 +246,6 @@ class TestEagleProposerDummyRun(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.speculative_token_tree = str([
             (i + 1) * (0, ) for i in range(4)
         ])
@@ -366,8 +360,6 @@ 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

@@ -42,9 +42,6 @@ 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

@@ -130,27 +130,6 @@ 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()
 
         # TODO: Remove it when the bug of fx-graph is solved
         self.maybe_eager_context: ContextManager[Any] = nullcontext()

vllm_ascend/worker/model_runner_v1.py

@@ -165,10 +165,6 @@ 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."""
@@ -2326,8 +2322,7 @@ class NPUModelRunner(GPUModelRunner):
                 model_register(self.model, self.model_config)
             if self.drafter:
                 logger.info("Loading drafter model...")
-                with get_tp_context(self.drafter):
+                self.drafter.load_model(self.model)
-                    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())
@@ -2703,15 +2698,11 @@ class NPUModelRunner(GPUModelRunner):
         kernel_block_sizes = []
         for kv_cache_group_id, kv_cache_group in enumerate(
                 kv_cache_config.kv_cache_groups):
-            kv_cache_spec = kv_cache_group.kv_cache_spec
+
-            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
+            if isinstance(kv_cache_group.kv_cache_spec,
-                # All layers in the UniformTypeKVCacheSpecs have the same type,
+                          EncoderOnlyAttentionSpec):
-                # 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_spec, AttentionSpec):
+            elif isinstance(kv_cache_group.kv_cache_spec, AttentionSpec):
                 # This is an attention backend that supports virtual
                 # block splitting. Get the supported block sizes from
                 # the backend.