[Misc]add coding benchmark for speculative decoding (#15303)

Signed-off-by: CXIAAAAA <[email protected]>

Author: CXIAAAAA

benchmarks/benchmark_dataset.py

@@ -715,3 +715,66 @@ def sample(
                 ))
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# Instruct Coder Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class InstructCoderDataset(HuggingFaceDataset):
+    """
+    InstructCoder Dataset.
+    https://huggingface.co/datasets/likaixin/InstructCoder
+
+    InstructCoder is the dataset designed for general code editing.
+    It consists of 114,239 instruction-input-output triplets,
+    and covers multiple distinct code editing scenario.
+    """
+
+    DEFAULT_OUTPUT_LEN = 200  # this is the average default output length
+    DEFAULT_NUM_REQUESTS = 1000
+    INSTRUCT_CODER_DATASET_PATH = "likaixin/InstructCoder"
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        if self.dataset_path != self.INSTRUCT_CODER_DATASET_PATH:
+            raise ValueError(f"Only support likaixin/InstructCoder dataset.\
+                    This data path {self.dataset_path} is not valid.")
+        if self.dataset_subset is None and self.dataset_split != "train":
+            raise ValueError("Dataset split must be 'train'.")
+
+    def load_data(self) -> None:
+        dataset = load_dataset(
+            self.dataset_path,
+            name=self.dataset_subset,
+            split=self.dataset_split,
+            streaming=True,
+        )
+        self.data = dataset.shuffle(seed=self.random_seed)
+
+    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 = f"{item['instruction']}:\n{item['input']}"
+            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_serving.py

@@ -53,8 +53,9 @@
     from argparse import ArgumentParser as FlexibleArgumentParser
 
 from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
-                               RandomDataset, SampleRequest, ShareGPTDataset,
-                               SonnetDataset, VisionArenaDataset)
+                               InstructCoderDataset, RandomDataset,
+                               SampleRequest, ShareGPTDataset, SonnetDataset,
+                               VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -588,9 +589,14 @@ def main(args: argparse.Namespace):
     elif args.dataset_name == "hf":
         # Choose between VisionArenaDataset
         # and HuggingFaceDataset based on provided parameters.
-        dataset_class = (VisionArenaDataset if args.dataset_path
-                         == VisionArenaDataset.VISION_ARENA_DATASET_PATH
-                         and args.hf_subset is None else HuggingFaceDataset)
+        dataset_class = HuggingFaceDataset
+        if args.dataset_path == VisionArenaDataset.VISION_ARENA_DATASET_PATH:
+            assert args.hf_subset is None, "VisionArenaDataset needs hf_subset to be None."  #noqa: E501
+            dataset_class = VisionArenaDataset
+        elif args.dataset_path == "likaixin/InstructCoder":
+            dataset_class = InstructCoderDataset
+            args.hf_split = "train"
+
         input_requests = dataset_class(
             dataset_path=args.dataset_path,
             dataset_subset=args.hf_subset,

benchmarks/benchmark_throughput.py

@@ -12,8 +12,9 @@
 import torch
 import uvloop
 from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
-                               RandomDataset, SampleRequest, ShareGPTDataset,
-                               SonnetDataset, VisionArenaDataset)
+                               InstructCoderDataset, RandomDataset,
+                               SampleRequest, ShareGPTDataset, SonnetDataset,
+                               VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
@@ -300,6 +301,7 @@ def get_requests(args, tokenizer):
         "input_len": args.input_len,
         "output_len": args.output_len,
     }
+
     if args.dataset_path is None or args.dataset_name == "random":
         sample_kwargs["range_ratio"] = args.random_range_ratio
         sample_kwargs["prefix_len"] = args.prefix_len
@@ -317,17 +319,21 @@ def get_requests(args, tokenizer):
     elif args.dataset_name == "burstgpt":
         dataset_cls = BurstGPTDataset
     elif args.dataset_name == "hf":
-        if args.backend != "vllm-chat":
-            raise ValueError(
-                "hf datasets only are supported by vllm-chat backend")
-        # Choose between VisionArenaDataset and HuggingFaceDataset based on
-        # provided parameters.
-        dataset_cls = (VisionArenaDataset if args.dataset_path
-                       == VisionArenaDataset.VISION_ARENA_DATASET_PATH
-                       and args.hf_subset is None else HuggingFaceDataset)
-        common_kwargs['dataset_subset'] = args.hf_subset
-        common_kwargs['dataset_split'] = args.hf_split
-        sample_kwargs["enable_multimodal_chat"] = True
+        if args.dataset_path == VisionArenaDataset.VISION_ARENA_DATASET_PATH:
+            if args.args.backend == "vllm-chat":
+                raise ValueError(
+                    "hf datasets only are supported by vllm-chat backend")
+            # Choose between VisionArenaDataset and HuggingFaceDataset based on
+            # provided parameters.
+            dataset_cls = (VisionArenaDataset if args.dataset_path
+                           == VisionArenaDataset.VISION_ARENA_DATASET_PATH
+                           and args.hf_subset is None else HuggingFaceDataset)
+            common_kwargs['dataset_subset'] = args.hf_subset
+            common_kwargs['dataset_split'] = args.hf_split
+            sample_kwargs["enable_multimodal_chat"] = True
+        elif args.dataset_path == "likaixin/InstructCoder":
+            dataset_cls = InstructCoderDataset
+            common_kwargs['dataset_split'] = "train"
 
     else:
         raise ValueError(f"Unknown dataset name: {args.dataset_name}")
@@ -462,9 +468,14 @@ def validate_args(args):
         warnings.warn("--hf-subset and --hf-split will be ignored \
                 since --dataset-name is not 'hf'.",
                       stacklevel=2)
-    elif args.dataset_name == "hf" and args.backend != "vllm-chat":
-        raise ValueError(
-            "When --dataset-name is 'hf', backend must be 'vllm-chat'")
+    elif args.dataset_name == "hf":
+        if args.dataset_path == VisionArenaDataset.VISION_ARENA_DATASET_PATH:
+            assert args.backend == "vllm-chat", "VisionArenaDataset needs to use vllm-chat as the backend."  #noqa: E501
+        elif args.dataset_path == "likaixin/InstructCoder":
+            assert args.backend == "vllm", "InstructCoder dataset needs to use vllm as the backend."  #noqa: E501
+        else:
+            raise ValueError(
+                f"{args.dataset_path} is not supported by hf dataset.")
 
     # --random-range-ratio: only used when dataset_name is 'random'
     if args.dataset_name != 'random' and args.random_range_ratio is not None: