[Model] MLPSpeculator speculative decoding support (#4947)

Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>

Co-authored-by: Thomas Parnell <tpa@zurich.ibm.com>
Co-authored-by: Nick Hill <nickhill@us.ibm.com>
Co-authored-by: Davis Wertheimer <Davis.Wertheimer@ibm.com>

vllm/transformers_utils/configs/__init__.py

@@ -5,6 +5,7 @@ from vllm.transformers_utils.configs.dbrx import DbrxConfig
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
 from vllm.transformers_utils.configs.jais import JAISConfig
+from vllm.transformers_utils.configs.mlp_speculator import MLPSpeculatorConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
 
 __all__ = [
@@ -13,4 +14,5 @@ __all__ = [
     "MPTConfig",
     "RWConfig",
     "JAISConfig",
+    "MLPSpeculatorConfig",
 ]

vllm/transformers_utils/configs/mlp_speculator.py

@@ -0,0 +1,50 @@
+from typing import List, Optional
+
+from transformers import PretrainedConfig
+
+
+class MLPSpeculatorConfig(PretrainedConfig):
+    model_type = "mlp_speculator"
+
+    attribute_map = {
+        "hidden_size": "emb_dim",
+    }
+
+    def __init__(self,
+                 vocab_size: int = 32000,
+                 emb_dim: int = 4096,
+                 inner_dim: int = 0,
+                 n_predict: int = 3,
+                 top_k_tokens_per_head: Optional[List[int]] = None,
+                 n_candidates: int = 5,
+                 **kwargs):
+        """
+        Initialize an MLPSpeculatorConfig
+
+        Args:
+            vocab_size: int
+                the model vocab size
+            emb_dim: int
+                the model embedding dimension
+            inner_dim: int
+                the inner dimension of the model. If 0, will be the emb_dim.
+            n_predict: int
+                the number of lookaheads for the speculator
+            top_k_tokens_per_head: List[int]
+                Number of tokens to consider from each head when forming the
+                candidate tree.
+                For each candidate branch in the tree, head n produces topk[n]
+                additional sub-branches.
+            n_candidates: int
+                number of child candidates to create per sequence
+        """
+        if top_k_tokens_per_head is None:
+            top_k_tokens_per_head = [5, 4, 3]
+        assert len(top_k_tokens_per_head) == n_predict
+        self.vocab_size = vocab_size
+        self.emb_dim = emb_dim
+        self.inner_dim = inner_dim
+        self.n_predict = n_predict
+        self.top_k_tokens_per_head = top_k_tokens_per_head
+        self.n_candidates = n_candidates
+        super().__init__(**kwargs)