[Speculative Decoding] Add speculators config support (#21345)

tests/speculative_decoding/speculators/test_eagle3.py

@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+
+@pytest.mark.parametrize(
+    "model_path",
+    [("nm-testing/SpeculatorLlama3-1-8B-Eagle3-converted-0717"),
+     ("nm-testing/SpeculatorLlama3-1-8B-Eagle3-converted-0717-quantized")])
+def test_llama(vllm_runner, example_prompts, model_path):
+    with vllm_runner(model_path, dtype=torch.bfloat16) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy(example_prompts,
+                                                  max_tokens=20)
+        print(vllm_outputs)
+        assert vllm_outputs

vllm/config.py

@@ -39,8 +39,8 @@ from vllm.transformers_utils.config import (
     ConfigFormat, get_config, get_hf_image_processor_config,
     get_hf_text_config, get_pooling_config,
     get_sentence_transformer_tokenizer_config, is_encoder_decoder,
-    try_get_generation_config, try_get_safetensors_metadata,
+    maybe_override_with_speculators_target_model, try_get_generation_config,
-    try_get_tokenizer_config, uses_mrope)
+    try_get_safetensors_metadata, try_get_tokenizer_config, uses_mrope)
 from vllm.transformers_utils.s3_utils import S3Model
 from vllm.transformers_utils.utils import is_s3, maybe_model_redirect
 # yapf conflicts with isort for this block
@@ -535,6 +535,15 @@ class ModelConfig:
                     "affect the random state of the Python process that "
                     "launched vLLM.", self.seed)
 
+        if self.runner != "draft":
+            # If we're not running the draft model, check for speculators config
+            # If speculators config, set model / tokenizer to be target model
+            self.model, self.tokenizer = maybe_override_with_speculators_target_model(  # noqa: E501
+                model=self.model,
+                tokenizer=self.tokenizer,
+                revision=self.revision,
+                trust_remote_code=self.trust_remote_code)
+
         # Keep set served_model_name before maybe_model_redirect(self.model)
         self.served_model_name = get_served_model_name(self.model,
                                                        self.served_model_name)
@@ -606,8 +615,8 @@ class ModelConfig:
                                self.config_format,
                                hf_overrides_kw=hf_overrides_kw,
                                hf_overrides_fn=hf_overrides_fn)
-        self.hf_config = hf_config
 
+        self.hf_config = hf_config
         self.hf_text_config = get_hf_text_config(self.hf_config)
         self.attention_chunk_size = getattr(self.hf_text_config,
                                             "attention_chunk_size", None)
@@ -2980,10 +2989,13 @@ class SpeculativeConfig:
                             "Chunked prefill and EAGLE are not compatible "
                             "when using V0.")
 
+                    from vllm.transformers_utils.configs import (
+                        SpeculatorsConfig)
                     from vllm.transformers_utils.configs.eagle import (
                         EAGLEConfig)
+
                     if isinstance(self.draft_model_config.hf_config,
-                                  EAGLEConfig):
+                                  (EAGLEConfig, SpeculatorsConfig)):
                         pass
                     else:
                         eagle_config = EAGLEConfig(

vllm/engine/arg_utils.py

@@ -978,8 +978,28 @@ class EngineArgs:
         provided as a JSON string input via CLI arguments or directly as a
         dictionary from the engine.
         """
+
+        from vllm.transformers_utils.config import get_config
+        from vllm.transformers_utils.configs.speculators.base import (
+            SpeculatorsConfig)
+
         if self.speculative_config is None:
-            return None
+            hf_config = get_config(self.hf_config_path or self.model,
+                                   self.trust_remote_code, self.revision,
+                                   self.code_revision, self.config_format)
+
+            # if loading a SpeculatorsConfig, load the specualtive_config
+            # details from the config directly
+            # no user input required / expected
+            if isinstance(hf_config, SpeculatorsConfig):
+                # We create one since we dont create one
+                self.speculative_config = {}
+                self.speculative_config[
+                    "num_speculative_tokens"] = hf_config.num_lookahead_tokens
+                self.speculative_config["model"] = self.model
+                self.speculative_config["method"] = hf_config.method
+            else:
+                return None
 
         # Note(Shangming): These parameters are not obtained from the cli arg
         # '--speculative-config' and must be passed in when creating the engine

vllm/model_executor/models/llama_eagle3.py

@@ -51,6 +51,25 @@ class LlamaDecoderLayer(LlamaDecoderLayer):
 
         self.hidden_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
+        if getattr(config, "norm_before_residual", False):
+            self._residual_norm = self._norm_before_residual
+        else:
+            self._residual_norm = self._norm_after_residual
+
+    def _norm_before_residual(
+            self,
+            hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        hidden_states = self.hidden_norm(hidden_states)
+        residual = hidden_states
+        return hidden_states, residual
+
+    def _norm_after_residual(
+            self,
+            hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        residual = hidden_states
+        hidden_states = self.hidden_norm(hidden_states)
+        return hidden_states, residual
+
     def forward(
         self,
         positions: torch.Tensor,
@@ -59,9 +78,10 @@ class LlamaDecoderLayer(LlamaDecoderLayer):
         residual: Optional[torch.Tensor],
     ) -> tuple[torch.Tensor, torch.Tensor]:
 
-        residual = hidden_states
         embeds = self.input_layernorm(embeds)
-        hidden_states = self.hidden_norm(hidden_states)
+
+        hidden_states, residual = self._residual_norm(
+            hidden_states=hidden_states)
 
         hidden_states = torch.cat([embeds, hidden_states], dim=-1)
         # Self Attention
@@ -102,7 +122,7 @@ class LlamaModel(nn.Module):
 
         self.layers = nn.ModuleList([
             LlamaDecoderLayer(
-                self.config,
+                config=self.config,
                 prefix=maybe_prefix(prefix, f"layers.{start_layer_id}"),
             )
         ])

vllm/transformers_utils/config.py

@@ -35,8 +35,9 @@ from vllm.transformers_utils.configs import (ChatGLMConfig, DeepseekVLV2Config,
                                              MllamaConfig, MLPSpeculatorConfig,
                                              Nemotron_Nano_VL_Config,
                                              NemotronConfig, NVLM_D_Config,
-                                             RWConfig, Step3TextConfig,
+                                             RWConfig, SpeculatorsConfig,
-                                             Step3VLConfig, UltravoxConfig)
+                                             Step3TextConfig, Step3VLConfig,
+                                             UltravoxConfig)
 # yapf: enable
 from vllm.transformers_utils.configs.mistral import adapt_config_dict
 from vllm.transformers_utils.utils import check_gguf_file
@@ -81,6 +82,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = {
     "mlp_speculator": MLPSpeculatorConfig,
     "medusa": MedusaConfig,
     "eagle": EAGLEConfig,
+    "speculators": SpeculatorsConfig,
     "nemotron": NemotronConfig,
     "NVLM_D": NVLM_D_Config,
     "ultravox": UltravoxConfig,
@@ -287,6 +289,27 @@ def _maybe_remap_hf_config_attrs(config: PretrainedConfig) -> PretrainedConfig:
     return config
 
 
+def maybe_override_with_speculators_target_model(
+        model: str,
+        tokenizer: str,
+        trust_remote_code: bool,
+        revision: Optional[str] = None) -> tuple[str, str]:
+    """
+    If running a speculators config, override running model with target model
+    """
+    config_dict, _ = PretrainedConfig.get_config_dict(
+        model,
+        revision=revision,
+        trust_remote_code=trust_remote_code,
+        token=_get_hf_token(),
+    )
+    spec_config = config_dict.get("speculators_config")
+    # Return the target model
+    if spec_config is not None:
+        model = tokenizer = spec_config["verifier"]["name_or_path"]
+    return model, tokenizer
+
+
 def get_config(
     model: Union[str, Path],
     trust_remote_code: bool,
@@ -345,9 +368,12 @@ def get_config(
             token=_get_hf_token(),
             **kwargs,
         )
-
         # Use custom model class if it's in our registry
         model_type = config_dict.get("model_type")
+        if model_type is None:
+            model_type = "speculators" if config_dict.get(
+                "speculators_config") is not None else model_type
+
         if model_type in _CONFIG_REGISTRY:
             config_class = _CONFIG_REGISTRY[model_type]
             config = config_class.from_pretrained(

vllm/transformers_utils/configs/__init__.py

@@ -24,6 +24,7 @@ from vllm.transformers_utils.configs.nemotron import NemotronConfig
 from vllm.transformers_utils.configs.nemotron_h import NemotronHConfig
 from vllm.transformers_utils.configs.nemotron_vl import Nemotron_Nano_VL_Config
 from vllm.transformers_utils.configs.nvlm_d import NVLM_D_Config
+from vllm.transformers_utils.configs.speculators.base import SpeculatorsConfig
 from vllm.transformers_utils.configs.step3_vl import (Step3TextConfig,
                                                       Step3VisionEncoderConfig,
                                                       Step3VLConfig)
@@ -44,6 +45,7 @@ __all__ = [
     "NemotronHConfig",
     "Nemotron_Nano_VL_Config",
     "NVLM_D_Config",
+    "SpeculatorsConfig",
     "UltravoxConfig",
     "Step3VLConfig",
     "Step3VisionEncoderConfig",

vllm/transformers_utils/configs/speculators/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

vllm/transformers_utils/configs/speculators/algos.py

@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+SUPPORTED_SPECULATORS_TYPES = {}
+
+
+def register_speculator(name):
+
+    def decorator(fn):
+        SUPPORTED_SPECULATORS_TYPES[name] = fn
+        return fn
+
+    return decorator
+
+
+@register_speculator("eagle3")
+def update_eagle3(config_dict: dict, vllm_config: dict) -> None:
+    """
+    Apply Eagle-3 specific configuration transformations.
+    
+    Eagle-3 specific fields:
+    - draft_vocab_size: Size of the draft model's vocabulary
+    - target_hidden_size: Hidden size of the target model
+    - norm_before_residual: Whether to apply norm before residual connection
+    """
+
+    vllm_config["draft_vocab_size"] = config_dict.get("draft_vocab_size")
+    if config_dict.get("target_hidden_size") is not None:
+        vllm_config["target_hidden_size"] = config_dict["target_hidden_size"]
+    vllm_config["norm_before_residual"] = config_dict.get(
+        "norm_before_residual", True)
+    vllm_config["architectures"] = ["Eagle3LlamaForCausalLM"]

vllm/transformers_utils/configs/speculators/base.py

@@ -0,0 +1,91 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+from typing import Any, Union
+
+from transformers import PretrainedConfig
+
+from vllm.transformers_utils.configs.speculators.algos import (
+    SUPPORTED_SPECULATORS_TYPES)
+
+__all__ = ["SpeculatorsConfig"]
+
+
+class SpeculatorsConfig(PretrainedConfig):
+    model_type = "speculators"
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Union[str, os.PathLike],
+        **kwargs,
+    ) -> "SpeculatorsConfig":
+        """Load speculators Eagle config and convert to vLLM format."""
+        config_dict, _ = cls.get_config_dict(pretrained_model_name_or_path,
+                                             **kwargs)
+
+        speculators_model_type = config_dict.get("speculators_model_type")
+        if speculators_model_type not in SUPPORTED_SPECULATORS_TYPES:
+            raise ValueError(
+                f"Expected one of: {SUPPORTED_SPECULATORS_TYPES}. "
+                "Please ensure you're loading a speculators-format model.")
+
+        # validate fields
+        # TODO: @dsikka - use speculators pydantic model to validate
+        cls.validate_speculators_config(config_dict=config_dict)
+        # Convert from speculators config -> format that can be ingested by vLLM
+        vllm_config = cls.convert_speculators_to_vllm(config_dict=config_dict)
+        # Apply anything specific to the supported algorithm
+        algo_updater = SUPPORTED_SPECULATORS_TYPES[speculators_model_type]
+        algo_updater(config_dict=config_dict, vllm_config=vllm_config)
+        return cls(**vllm_config)
+
+    @classmethod
+    def validate_speculators_config(cls, config_dict: dict[str, Any]) -> None:
+        try:
+            spec_config = config_dict["speculators_config"]
+            methods = spec_config["proposal_methods"]
+            first_method = methods[0]
+            _ = first_method["speculative_tokens"]
+            _ = spec_config["verifier"]["name_or_path"]
+            _ = config_dict["speculators_model_type"]
+        except (KeyError, IndexError, TypeError) as e:
+            raise ValueError("Invalid speculators config structure") from e
+
+        if "transformer_layer_config" not in config_dict:
+            raise ValueError("Must provide transformer_layer_config")
+
+        if not isinstance(config_dict["transformer_layer_config"], dict):
+            raise TypeError(
+                "'transformer_layer_config' must be a dictionary if provided")
+
+    @classmethod
+    def convert_speculators_to_vllm(
+            cls, config_dict: dict[str, Any]) -> dict[str, Any]:
+        """
+        Convert speculators config format to vLLM format.
+        
+        This method handles the translation of field names and structure
+        between speculators and vLLM formats.
+        
+        Returns:
+            Dictionary with vLLM-compatible configuration
+        """
+        # Currently we only support one proposal method
+        spec_config = config_dict["speculators_config"]
+        first_method = spec_config.get("proposal_methods")[0]
+        num_lookahead_tokens = first_method.get("speculative_tokens")
+
+        if num_lookahead_tokens is None:
+            raise ValueError(
+                "Missing 'speculative_tokens' in proposal method. "
+                f"Got: {first_method}")
+
+        # Build base vLLM config
+        vllm_config = {
+            "method": config_dict.get("speculators_model_type"),
+            "num_lookahead_tokens": num_lookahead_tokens,
+            "target_model": spec_config.get("verifier")["name_or_path"]
+        }
+        vllm_config.update(config_dict["transformer_layer_config"])
+        return vllm_config