[V1][Spec Decode] Respect prompt_lookup_max (#15348)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

tests/v1/spec_decode/test_ngram.py

@@ -2,7 +2,8 @@
 
 import numpy as np
 
-from vllm.v1.spec_decode.ngram_proposer import (_find_subarray_kmp,
+from vllm.v1.spec_decode.ngram_proposer import (NgramProposer,
+                                                _find_subarray_kmp,
                                                 _kmp_lps_array)
 
 
@@ -35,3 +36,53 @@ def test_find_subarray_kmp():
     # Return on the first match
     np.testing.assert_array_equal(_find_subarray_kmp(X, 1, 3),
                                   np.array([6, 2, 3]))
+
+
+def test_ngram_proposer():
+    proposer = NgramProposer()
+
+    # No match.
+    result = proposer.propose(
+        context_token_ids=np.array([1, 2, 3, 4, 5]),
+        min_n=2,
+        max_n=2,
+        k=2,
+    )
+    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,
+    )
+    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,
+    )
+    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,
+    )
+    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,
+    )
+    assert np.array_equal(result, np.array([1, 2]))  # Not [5, 2]

vllm/v1/spec_decode/ngram_proposer.py

@@ -10,7 +10,8 @@ class NgramProposer:
     def propose(
         self,
         context_token_ids: np.ndarray,
-        n: int,
+        min_n: int,
+        max_n: int,
         k: int,
     ) -> Optional[np.ndarray]:
         """Proposes the next sequence of tokens based on n-gram pattern 
@@ -21,7 +22,8 @@ class NgramProposer:
         Args:
             context_token_ids: Numpy array of token IDs representing the 
                                context sequence.
-            n: Length of the n-gram to match.
+            min_n: Minimum length of the n-gram to match.
+            max_n: Maximum length of the n-gram to match.
             k: Number of tokens follow the match. If there are less 
                than k tokens follow the match, we will return 
                the maximum amount of tokens until the end.
@@ -32,14 +34,21 @@ class NgramProposer:
             None: If no matching n-gram pattern is found.
         
         Example:
-            If context_token_ids = [1,2,3,4,2,3], n = 2, and k = 4:
+            If context_token_ids = [1,2,3,4,2,3], min_n = 2, max_n = 3, and
+            k = 4:
+            - The last 3 (= max_n) tokens [4,2,3] cannot find a match.
             - The last 2 tokens [2,3] will be matched against the previous 
               4 tokens [1,2,3,4].
             - Finding a match of [2,3] would return the tokens that 
               followed that pattern. Here we will return [4,2,3] because 
               we only have three tokens after the match.
         """
-        return _find_subarray_kmp(context_token_ids, n, k)
+        # TODO(woosuk): Optimize this.
+        for n in range(max_n, min_n - 1, -1):
+            result = _find_subarray_kmp(context_token_ids, n, k)
+            if result is not None:
+                return result
+        return None
 
 
 @jit(nopython=True)

vllm/v1/worker/gpu_model_runner.py

@@ -160,6 +160,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 self.drafter.propose(
                     np.zeros(1024, dtype=np.int32),
                     self.speculative_config.prompt_lookup_min,
+                    self.speculative_config.prompt_lookup_max,
                     self.speculative_config.num_speculative_tokens,
                 )
                 self.rejection_sampler = RejectionSampler()
@@ -1155,6 +1156,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             drafter_output = self.drafter.propose(
                 self.input_batch.token_ids_cpu[i, :end_idx],
                 self.speculative_config.prompt_lookup_min,
+                self.speculative_config.prompt_lookup_max,
                 self.speculative_config.num_speculative_tokens,
             )
             if drafter_output is None or len(drafter_output) == 0: