add mtbench dataste

benchmarks/benchmark_dataset.py

@@ -405,6 +405,13 @@ class ShareGPTDataset(BenchmarkDataset):
                 entry["conversations"][1]["value"],
             )
 
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
             lora_request, tokenizer = self.get_random_lora_request(
                 tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
             prompt_ids = tokenizer(prompt).input_ids
@@ -763,6 +770,14 @@ class InstructCoderDataset(HuggingFaceDataset):
             if len(sampled_requests) >= num_requests:
                 break
             prompt = f"{item['instruction']}:\n{item['input']}"
+
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
             prompt_len = len(tokenizer(prompt).input_ids)
             sampled_requests.append(
                 SampleRequest(
@@ -801,6 +816,13 @@ class AIMODataset(HuggingFaceDataset):
                 break
             prompt, completion = item['problem'], item["solution"]
 
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
             prompt_ids = tokenizer(prompt).input_ids
             completion_ids = tokenizer(completion).input_ids
             prompt_len = len(prompt_ids)
@@ -898,3 +920,103 @@ class ASRDataset(HuggingFaceDataset):
                            " what Whisper supports.", skipped)
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
+
+
+class MTBenchDataset(HuggingFaceDataset):
+    """
+    MT-Bench Dataset.
+    https://huggingface.co/datasets/philschmid/mt-bench
+
+    We create a single turn dataset for MT-Bench. 
+    This is similar to Spec decoding benchmark setup in vLLM
+    https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
+    """ # noqa: E501
+
+    DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
+    SUPPORTED_DATASET_PATHS = {
+        "philschmid/mt-bench",
+    }
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        sampled_requests = []
+
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt = item['turns'][0]
+
+            # apply template
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+class CNNDailyMailDataset(HuggingFaceDataset):
+    """
+    MT-Bench Dataset.
+    https://huggingface.co/datasets/philschmid/mt-bench
+
+    We create a single turn dataset for MT-Bench. 
+    This is similar to Spec decoding benchmark setup in vLLM
+    https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
+    """ # noqa: E501
+
+    DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
+    SUPPORTED_DATASET_PATHS = {
+        "abisee/cnn_dailymail",
+    }
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        sampled_requests = []
+
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            instruction = "Could you summarize the following article, " \
+            "please reuse text from the article if possible: "
+            prompt = instruction + item['article']
+
+            # apply template
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests

benchmarks/benchmark_throughput.py

@@ -12,7 +12,8 @@ from typing import Any, Optional, Union
 import torch
 import uvloop
 from benchmark_dataset import (AIMODataset, BurstGPTDataset,
-                               ConversationDataset, InstructCoderDataset,
+                               CNNDailyMailDataset, ConversationDataset,
+                               InstructCoderDataset, MTBenchDataset,
                                RandomDataset, SampleRequest, ShareGPTDataset,
                                SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
@@ -339,6 +340,14 @@ def get_requests(args, tokenizer):
             dataset_cls = AIMODataset
             common_kwargs['dataset_subset'] = None
             common_kwargs['dataset_split'] = "train"
+        elif args.dataset_path in MTBenchDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = MTBenchDataset
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
+        elif args.dataset_path in CNNDailyMailDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = CNNDailyMailDataset
+            common_kwargs['dataset_subset'] = '3.0.0'
+            common_kwargs['dataset_split'] = "train"
     else:
         raise ValueError(f"Unknown dataset name: {args.dataset_name}")
     # Remove None values
@@ -477,8 +486,11 @@ def validate_args(args):
                 VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
                 | ConversationDataset.SUPPORTED_DATASET_PATHS):
             assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend."  #noqa: E501
-        elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
-                                   | AIMODataset.SUPPORTED_DATASET_PATHS):
+        elif args.dataset_path in (
+                InstructCoderDataset.SUPPORTED_DATASET_PATHS
+                | AIMODataset.SUPPORTED_DATASET_PATHS
+                | MTBenchDataset.SUPPORTED_DATASET_PATHS
+                | CNNDailyMailDataset.SUPPORTED_DATASET_PATHS):
             assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend."  #noqa: E501
         else:
             raise ValueError(