[WWB] Add eagle3 pipeline (#2812)

## Description
Add eagle3 pipeline

Ticket:  CVS-170888


## Checklist:
- [ ] Tests have been updated or added to cover the new code <!--- If
the change isn't maintenance related, update the tests at
https://github.com/openvinotoolkit/openvino.genai/tree/master/tests or
explain in the description why the tests don't need an update. -->
- [ ] This patch fully addresses the ticket. <!--- If follow-up pull
requests are needed, specify in description. -->
- [ ] I have made corresponding changes to the documentation

---------

Co-authored-by: Chen Peter <peter.chen@intel.com>

Author: sunxiaoxia2022

tools/who_what_benchmark/whowhatbench/model_loaders.py

@@ -1,3 +1,4 @@
+from pathlib import Path
 import logging
 import json
 import torch
@@ -99,6 +100,17 @@ def load_text_genai_pipeline(model_dir, device="CPU", ov_config=None, **kwargs):
             ov_adapter = openvino_genai.Adapter(adapter)
             adapter_config.add(ov_adapter, alpha)
 
+    draft_model_path = kwargs.get("draft_model", '')
+    if draft_model_path:
+        if not Path(draft_model_path).exists():
+            raise RuntimeError(f"Error: Draft model path does not exist: {draft_model_path}")
+        draft_device = kwargs.get("draft_device", None) or device
+        draft_model_load_kwargs = (
+            {"scheduler_config": get_scheduler_config_genai(kwargs["draft_cb_config"])}
+            if kwargs["draft_cb_config"] is not None else {}
+        )
+        ov_config["draft_model"] = openvino_genai.draft_model(draft_model_path, draft_device.upper(), **draft_model_load_kwargs)
+
     is_continuous_batching = kwargs.get("cb_config", None) is not None
 
     if is_continuous_batching:

tools/who_what_benchmark/whowhatbench/text_evaluator.py

@@ -36,7 +36,9 @@ def __init__(
         seqs_per_request=None,
         use_chat_template=None,
         long_prompt=False,
-        empty_adapters=False
+        empty_adapters=False,
+        num_assistant_tokens=0,
+        assistant_confidence_threshold=0.0
     ) -> None:
         assert (
             base_model is not None or gt_data is not None
@@ -53,6 +55,8 @@ def __init__(
         self.seqs_per_request = seqs_per_request
         self.generation_fn = gen_answer_fn
         self.use_chat_template = use_chat_template
+        self.num_assistant_tokens = num_assistant_tokens
+        self.assistant_confidence_threshold = assistant_confidence_threshold
         if self.generation_config is not None:
             assert self.seqs_per_request is not None
         self.empty_adapters = empty_adapters
@@ -135,7 +139,8 @@ def worst_examples(self, top_k: int = 5, metric="similarity"):
         return res
 
     def _generate_data(self, model, gen_answer_fn=None, generation_config=None):
-        def default_gen_answer(model, tokenizer, prompt, max_new_tokens, crop_question, use_chat_template=False, empty_adapters=False):
+        def default_gen_answer(model, tokenizer, prompt, max_new_tokens, crop_question, use_chat_template=False, empty_adapters=False,
+                               num_assistant_tokens=0, assistant_confidence_threshold=0.0):
             is_awq = getattr(model, "is_awq", None) is not None
             device = "cpu"
             if hasattr(model, "device"):
@@ -196,7 +201,9 @@ def default_gen_answer(model, tokenizer, prompt, max_new_tokens, crop_question,
                         self.max_new_tokens,
                         self._crop_question,
                         self.use_chat_template,
-                        empty_adapters=self.empty_adapters
+                        self.empty_adapters,
+                        self.num_assistant_tokens,
+                        self.assistant_confidence_threshold
                     )
                 )
         else:

tools/who_what_benchmark/whowhatbench/wwb.py

@@ -232,6 +232,35 @@ def parse_args():
         "If the base/target model is a local path, gguf-file should be just the filename (e.g., 'model.gguf'). "
         "If the base/target model is a HuggingFace model ID, gguf-file should be a relative path.",
     )
+    parser.add_argument(
+        "--draft-model",
+        default=None,
+        help="Path to draft model folder including IR files for Speculative decoding generation.",
+    )
+    parser.add_argument(
+        "--draft-device",
+        type=str,
+        default=None,
+        help="Inference device for Speculative decoding of draft model, e.g. 'CPU', 'GPU'.",
+    )
+    parser.add_argument(
+        "--draft-cb-config",
+        type=str,
+        default=None,
+        help="Path to file with Continuous Batching Scheduler settings or dict for Speculative decoding of draft model",
+    )
+    parser.add_argument(
+        "--num-assistant-tokens",
+        type=int,
+        default=None,
+        help="Config option num_assistant_tokens for Speculative decoding and Prompt Lookup decoding.",
+    )
+    parser.add_argument(
+        "--assistant-confidence-threshold",
+        type=float,
+        default=None,
+        help="Config option assistant_confidence_threshold for Speculative decoding.",
+    )
 
     return parser.parse_args()
 
@@ -387,16 +416,26 @@ def diff_strings(a: str, b: str, *, use_loguru_colors: bool = False) -> str:
     return "".join(output)
 
 
-def genai_gen_text(model, tokenizer, question, max_new_tokens, skip_question, use_chat_template=False, empty_adapters=False):
+def genai_gen_text(model, tokenizer, question, max_new_tokens, skip_question, use_chat_template=False, empty_adapters=False,
+                   num_assistant_tokens=0, assistant_confidence_threshold=0.0):
     kwargs = {}
     if empty_adapters:
         import openvino_genai
         kwargs["adapters"] = openvino_genai.AdapterConfig()
 
-    return model.generate(question, do_sample=False, max_new_tokens=max_new_tokens, apply_chat_template=use_chat_template, **kwargs)
+    return model.generate(
+        question,
+        do_sample=False,
+        max_new_tokens=max_new_tokens,
+        apply_chat_template=use_chat_template,
+        num_assistant_tokens=num_assistant_tokens,
+        assistant_confidence_threshold=assistant_confidence_threshold,
+        **kwargs,
+    )
 
 
-def llamacpp_gen_text(model, tokenizer, question, max_new_tokens, skip_question, use_chat_template=False):
+def llamacpp_gen_text(model, tokenizer, question, max_new_tokens, skip_question, use_chat_template=False, num_assistant_tokens=0,
+                      assistant_confidence_threshold=0.0):
     if use_chat_template:
         output = model.create_chat_completion(messages=[{"role": "user", "content": question}], max_tokens=max_new_tokens, temperature=0.0)
         text = output["choices"][0]["message"]["content"]
@@ -523,6 +562,14 @@ def create_evaluator(base_model, args):
                 gen_answer_fn=gen_answer_fn,
                 use_chat_template=use_chat_template,
                 long_prompt=args.long_prompt,
+                num_assistant_tokens=(
+                    int(args.num_assistant_tokens)
+                    if args.num_assistant_tokens is not None else 0
+                ),
+                assistant_confidence_threshold=(
+                    float(args.assistant_confidence_threshold)
+                    if args.assistant_confidence_threshold is not None else 0.0
+                ),
             )
         elif task == "text-to-image":
             return EvaluatorCLS(
@@ -725,6 +772,14 @@ def main():
     kwargs["embeds_normalize"] = args.embeds_normalize
     kwargs["embeds_padding_side"] = args.embeds_padding_side
 
+    if args.draft_model is not None:
+        kwargs["draft_model"] = args.draft_model
+        kwargs["draft_device"] = args.draft_device
+        draft_cb_config = None
+        if args.draft_cb_config is not None:
+            draft_cb_config = read_cb_config(args.draft_cb_config)
+        kwargs["draft_cb_config"] = draft_cb_config
+
     if args.gt_data and os.path.exists(args.gt_data):
         evaluator = create_evaluator(None, args)
     else: