[Bugfix] Fix v1/spec_decode/test_ngram.py (vllm-project#16895)

Signed-off-by: qizixi <[email protected]>

Author: zixi-qi

tests/v1/spec_decode/test_ngram.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 
+from vllm.config import SpeculativeConfig, VllmConfig
 from vllm.v1.spec_decode.ngram_proposer import (NgramProposer,
                                                 _find_subarray_kmp,
                                                 _kmp_lps_array)
@@ -39,50 +40,40 @@ def test_find_subarray_kmp():
 
 
 def test_ngram_proposer():
-    proposer = NgramProposer()
+
+    def ngram_proposer(min_n: int, max_n: int, k: int) -> NgramProposer:
+        return NgramProposer(vllm_config=VllmConfig(
+            speculative_config=SpeculativeConfig.from_dict(
+                {
+                    "prompt_lookup_min": min_n,
+                    "prompt_lookup_max": max_n,
+                    "num_speculative_tokens": k,
+                    "method": "ngram",
+                })))
 
     # No match.
-    result = proposer.propose(
-        context_token_ids=np.array([1, 2, 3, 4, 5]),
-        min_n=2,
-        max_n=2,
-        k=2,
-    )
+    result = ngram_proposer(
+        2, 2, 2).propose(context_token_ids=np.array([1, 2, 3, 4, 5]))
     assert result is None
 
     # No match for 4-gram.
-    result = proposer.propose(
-        context_token_ids=np.array([1, 2, 3, 4, 1, 2, 3]),
-        min_n=4,
-        max_n=4,
-        k=2,
-    )
+    result = ngram_proposer(
+        4, 4, 2).propose(context_token_ids=np.array([1, 2, 3, 4, 1, 2, 3]))
     assert result is None
 
     # No match for 4-gram but match for 3-gram.
-    result = proposer.propose(
-        context_token_ids=np.array([1, 2, 3, 4, 1, 2, 3]),
-        min_n=3,
-        max_n=4,
-        k=2,
-    )
+    result = ngram_proposer(
+        3, 4, 2).propose(context_token_ids=np.array([1, 2, 3, 4, 1, 2, 3]))
     assert np.array_equal(result, np.array([4, 1]))
 
     # Match for both 4-gram and 3-gram.
     # In this case, the proposer should return the 4-gram match.
-    result = proposer.propose(
-        context_token_ids=np.array([2, 3, 4, 5, 1, 2, 3, 4, 1, 2, 3, 4]),
-        min_n=3,
-        max_n=4,
-        k=2,
-    )
+    result = ngram_proposer(3, 4, 2).propose(
+        context_token_ids=np.array([2, 3, 4, 5, 1, 2, 3, 4, 1, 2, 3, 4]))
     assert np.array_equal(result, np.array([1, 2]))  # Not [5, 1]
 
     # Match for 2-gram and 3-gram, but not 4-gram.
-    result = proposer.propose(
-        context_token_ids=np.array([3, 4, 5, 2, 3, 4, 1, 2, 3, 4]),
-        min_n=2,
-        max_n=4,
-        k=2,
-    )
+    result = ngram_proposer(
+        2, 4,
+        2).propose(context_token_ids=np.array([3, 4, 5, 2, 3, 4, 1, 2, 3, 4]))
     assert np.array_equal(result, np.array([1, 2]))  # Not [5, 2]

vllm/config.py

@@ -120,7 +120,7 @@ def get_attr_docs(cls: type[Any]) -> dict[str, str]:
     def pairwise(iterable):
         """
         Manually implement https://docs.python.org/3/library/itertools.html#itertools.pairwise
-        
+
         Can be removed when Python 3.9 support is dropped.
         """
         iterator = iter(iterable)
@@ -266,7 +266,7 @@ class ModelConfig:
         config_format: The config format which shall be loaded.
             Defaults to 'auto' which defaults to 'hf'.
         hf_token: The token to use as HTTP bearer authorization for remote files
-            . If `True`, will use the token generated when running 
+            . If `True`, will use the token generated when running
             `huggingface-cli login` (stored in `~/.huggingface`).
         hf_overrides: If a dictionary, contains arguments to be forwarded to the
             HuggingFace config. If a callable, it is called to update the
@@ -1624,7 +1624,7 @@ class ParallelConfig:
     """The full name of the worker class to use. If "auto", the worker class
     will be determined based on the platform."""
     sd_worker_cls: str = "auto"
-    """The full name of the worker class to use for speculative decofing. 
+    """The full name of the worker class to use for speculative decofing.
     If "auto", the worker class will be determined based on the platform."""
     worker_extension_cls: str = ""
     """The full name of the worker extension class to use. The worker extension
@@ -1815,13 +1815,13 @@ class SchedulerConfig:
 
     max_num_batched_tokens: int = None  # type: ignore
     """Maximum number of tokens to be processed in a single iteration.
-    
+
     This config has no static default. If left unspecified by the user, it will
     be set in `EngineArgs.create_engine_config` based on the usage context."""
 
     max_num_seqs: int = None  # type: ignore
     """Maximum number of sequences to be processed in a single iteration.
-    
+
     This config has no static default. If left unspecified by the user, it will
     be set in `EngineArgs.create_engine_config` based on the usage context."""
 
@@ -1867,7 +1867,7 @@ class SchedulerConfig:
     # TODO (ywang96): Make this configurable.
     max_num_encoder_input_tokens: int = field(init=False)
     """Multimodal encoder compute budget, only used in V1.
-    
+
     NOTE: This is not currently configurable. It will be overridden by
     max_num_batched_tokens in case max multimodal embedding size is larger."""
 
@@ -2306,7 +2306,8 @@ def __post_init__(self):
         if self.model is None and self.num_speculative_tokens is not None:
             # TODO(Shangming): Refactor mtp configuration logic when supporting
             # mtp acceleration for more models besides deepseek_v3
-            if self.target_model_config.hf_text_config.model_type \
+            if self.target_model_config and \
+                self.target_model_config.hf_text_config.model_type \
                         == "deepseek_v3":
                 # use the draft model from the same model:
                 self.model = self.target_model_config.model