[gpt-oss] Add gpt-oss bf16 support

vllm/v1/spec_decode/ngram_proposer.py

@@ -0,0 +1,132 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import numpy as np
+from numba import jit
+
+from vllm.config import VllmConfig
+
+
+class NgramProposer:
+
+    def __init__(self, vllm_config: VllmConfig):
+        # Minimum length of the n-gram to match.
+        self.min_n = vllm_config.speculative_config.prompt_lookup_min
+        # Maximum length of the n-gram to match.
+        self.max_n = vllm_config.speculative_config.prompt_lookup_max
+        # 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.
+        self.k = vllm_config.speculative_config.num_speculative_tokens
+        # Maximum length of the model.
+        self.max_model_len = vllm_config.model_config.max_model_len
+
+        # Trigger Numba JIT compilation for N-gram proposer.
+        # This usually takes less than 1 second.
+        self.propose(np.zeros(1024, dtype=np.int32))
+
+    def propose(
+        self,
+        context_token_ids: np.ndarray,
+    ) -> Optional[np.ndarray]:
+        """Proposes the next sequence of tokens based on n-gram pattern 
+        matching in the context. The function finds matches of the last n 
+        tokens in the previous context, and returns k tokens that followed 
+        that match.
+        
+        Args:
+            context_token_ids: Numpy array of token IDs representing the 
+                               context sequence.
+
+        Returns:
+            np.ndarray: The sequence of tokens that followed 
+                        the matched n-gram in the context.
+            None: If no matching n-gram pattern is found.
+
+        Example:
+            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.
+        """
+        # Do not generate draft tokens beyond the max model length.
+        k = min(self.k, self.max_model_len - context_token_ids.shape[0])
+        if k <= 0:
+            return None
+
+        # TODO(woosuk): Optimize this.
+        for n in range(self.max_n, self.min_n - 1, -1):
+            result = _find_subarray_kmp(context_token_ids, n, k)
+            if result is not None:
+                return result
+        return None
+
+    def load_model(self, *args, **kwargs):
+        # No model to load.
+        pass
+
+
+@jit(nopython=True)
+def _kmp_lps_array(pattern: np.ndarray) -> np.ndarray:
+    """
+    Build the lps (longest proper prefix which is also suffix) 
+    array for the pattern.
+    """
+    lps = np.zeros(len(pattern), dtype=np.int32)
+    prev_lps = 0  # length of the previous longest prefix suffix
+    i = 1
+
+    while i < len(pattern):
+        if pattern[i] == pattern[prev_lps]:
+            prev_lps += 1
+            lps[i] = prev_lps
+            i += 1
+        else:
+            if prev_lps != 0:
+                prev_lps = lps[prev_lps - 1]
+            else:
+                lps[i] = 0
+                i += 1
+    return lps
+
+
+@jit(nopython=True)
+def _find_subarray_kmp(
+    context_token_ids: np.ndarray,
+    n: int,
+    k: int,
+) -> Optional[np.ndarray]:
+    context_len = context_token_ids.shape[0]
+    assert n > 0
+
+    pattern = context_token_ids[-n:]
+    # Precompute lps array for Y
+    lps = _kmp_lps_array(pattern)
+
+    i = 0
+    j = 0
+    # -n because the last n tokens are used as pattern
+    while i < context_len - n:
+        if context_token_ids[i] == pattern[j]:
+            i += 1
+            j += 1
+
+            # If we have matched the entire Y
+            if j == n:
+                # Found pattern in context, gather the next K elements
+                return context_token_ids[i:i + k]
+        else:
+            # Mismatch
+            if j != 0:
+                # Use the lps array to avoid re-checking elements
+                j = lps[j - 1]
+            else:
+                i += 1
+
+    # Y not found
+    return None