Save request outputs and add eval accuracy support (#8)

* Save request outputs and add eval accuracy support

* add readme and requirements

* add space in readme

* add username

* replace Benchmarks with Benchmark

* fix the path

Author: FanhaiLu1

benchmarks/README.md

@@ -0,0 +1,50 @@
+# JetStream Benchmark And Eval
+
+## Install Dependencies 
+
+```
+cd ~/JetStream/benchmarks
+pip install -r requirements.in
+```
+
+## Benchmark 
+
+### Prepare DataSet
+
+```
+cd ~/data
+wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+
+``` 
+
+### Run Benchmark with maxtext tokenizer
+
+```
+python benchmark_serving.py \
+--tokenizer /home/{username}/maxtext/assets/tokenizer \
+--num-prompts 10  \
+--dataset ~/data/ShareGPT_V3_unfiltered_cleaned_split.json
+
+``` 
+
+### Save request outputs in Benchmark
+
+Please use --save-request-outputs flag to enable this feature.
+
+```
+python benchmark_serving.py \
+--tokenizer /home/{username}/maxtext/assets/tokenizer \
+--num-prompts 10  \
+--dataset ~/data/ShareGPT_V3_unfiltered_cleaned_split.json \
+--save-request-outputs
+
+```
+
+## Eval Accuracy
+
+Evaluate inference genereted output accuracy using saved request outputs.
+
+```
+python eval_accuracy.py
+
+```

benchmarks/benchmark_serving.py

@@ -81,14 +81,33 @@ class BenchmarkMetrics:
   p99_tpot_ms: float
 
 
+@dataclass
+class InputRequest:
+  prompt: str = ""
+  prompt_len: int = 0
+  output: str = ""
+  output_len: int = 0
+
 @dataclass
 class RequestFuncOutput:
+  input_request: InputRequest = None
   generated_text: str = ""
   success: bool = False
   latency: float = 0
   ttft: float = 0
   prompt_len: int = 0
 
+  # Flatten the structure and return only the necessary results
+  def to_dict(self): 
+    return {
+      "prompt": self.input_request.prompt,
+      "original_output": self.input_request.output,
+      "generated_text": self.generated_text,
+      "success": self.success,
+      "latency": self.latency,
+      "prompt_len": self.prompt_len
+    }  
+
 
 def get_tokenizer(tokenizer_name: str) -> Any:
   """Return a tokenizer or a tokenizer placholder."""
@@ -105,7 +124,7 @@ def sample_requests(
     dataset_path: str,
     num_requests: int,
     tokenizer: Any,
-) -> List[Tuple[str, int, int]]:
+) -> List[InputRequest]:
   # Load the dataset.
   with open(dataset_path) as f:
     dataset = json.load(f)
@@ -133,11 +152,12 @@ def sample_requests(
   tokenized_dataset = []
   for i in range(len(dataset)):
     output_len = len(completion_token_ids[i])
-    tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
+    tokenized_dataset.append((prompts[i], prompt_token_ids[i], completions[i], output_len))
 
   # Filter out too long sequences.
-  filtered_dataset: List[Tuple[str, int, int]] = []
-  for prompt, prompt_token_ids, output_len in tokenized_dataset:
+  filtered_dataset: List[InputRequest] = []
+
+  for prompt, prompt_token_ids, output, output_len in tokenized_dataset:
     prompt_len = len(prompt_token_ids)
     if prompt_len < 4 or output_len < 4:
       # Prune too short sequences.
@@ -147,17 +167,18 @@ def sample_requests(
     if prompt_len > 1024 or prompt_len + output_len > 2048:
       # Prune too long sequences.
       continue
-    filtered_dataset.append((prompt, prompt_len, output_len))
+    reqeust = InputRequest(prompt, prompt_len, output, output_len)
+    filtered_dataset.append(reqeust)
 
   # Sample the requests.
   sampled_requests = random.sample(filtered_dataset, num_requests)
   return sampled_requests
 
 
 async def get_request(
-    input_requests: List[Tuple[str, int, int]],
+    input_requests: List[InputRequest],
     request_rate: float,
-) -> AsyncGenerator[Tuple[str, int, int], None]:
+) -> AsyncGenerator[InputRequest, None]:
   input_requests = iter(input_requests)
   for request in input_requests:
     yield request
@@ -172,7 +193,7 @@ async def get_request(
 
 
 def calculate_metrics(
-    input_requests: List[Tuple[str, int, int]],
+    input_requests: List[InputRequest],
     outputs: List[RequestFuncOutput],
     dur_s: float,
     tokenizer: Any,
@@ -190,7 +211,7 @@ def calculate_metrics(
           else "ĊŌƟ"
       )
       total_output += output_len
-      total_input += input_requests[i][1]
+      total_input += input_requests[i].prompt_len
       per_token_latencies.append(outputs[i].latency / output_len)
       ttfts.append(outputs[i].ttft)
       completed += 1
@@ -234,25 +255,24 @@ def grpc_sync_request(api_url: str, request: Any) -> tuple[str, float, float]:
 
 async def send_request(
     api_url: str,
-    prompt: str,
-    prompt_len: int,
+    input_request: InputRequest,
     pbar: tqdm,
     session_cache: str,
     priority: int,
-    max_tokens: int,
     threads: int,
 ) -> RequestFuncOutput:
   """Send the request to JetStream server."""
   loop = asyncio.get_running_loop()
   loop.set_default_executor(ThreadPoolExecutor(max_workers=threads))
   request = jetstream_pb2.DecodeRequest(
       session_cache=session_cache,
-      additional_text=prompt,
+      additional_text=input_request.prompt,
       priority=priority,
-      max_tokens=max_tokens,
+      max_tokens=input_request.output_len,
   )
   output = RequestFuncOutput()
-  output.prompt_len = prompt_len
+  output.input_request = input_request
+  output.prompt_len = input_request.prompt_len
   generated_text, ttft, latency = await loop.run_in_executor(
       None, grpc_sync_request, api_url, request
   )
@@ -268,7 +288,7 @@ async def send_request(
 async def benchmark(
     api_url: str,
     tokenizer: Any,
-    input_requests: List[Tuple[str, int, int]],
+    input_requests: List[InputRequest],
     request_rate: float,
     disable_tqdm: bool,
     session_cache: str,
@@ -283,17 +303,14 @@ async def benchmark(
   benchmark_start_time = time.perf_counter()
   tasks = []
   async for request in get_request(input_requests, request_rate):
-    prompt, prompt_len, output_len = request
     tasks.append(
         asyncio.create_task(
             send_request(
                 api_url=api_url,
-                prompt=prompt,
-                prompt_len=prompt_len,
+                input_request=request,
                 pbar=pbar,
                 session_cache=session_cache,
                 priority=priority,
-                max_tokens=output_len,
                 threads=threads,
             )
         )
@@ -341,17 +358,19 @@ async def benchmark(
       "median_tpot_ms": metrics.median_tpot_ms,
       "p99_tpot_ms": metrics.p99_tpot_ms,
   }
-  return result
+  return result, outputs
 
 
 def mock_requests(total_mock_requests: int):
   """Generates a list of mock requests containing mock data."""
   data = []
   for _ in range(total_mock_requests):
-    name = f"Item {random.randint(1, 1000)}"
-    price = random.randint(10, 100)
-    quantity = random.randint(1, 10)
-    data.append((name, price, quantity))
+    reqeust = InputRequest()
+    reqeust.prompt = f"Prompt {random.randint(1, 1000)}"
+    reqeust.prompt_len = random.randint(10, 100)
+    reqeust.out = f"Output {random.randint(1, 1000)}"
+    reqeust.output_len = random.randint(1, 10)
+    data.append(reqeust)
   return data
 
 
@@ -367,11 +386,11 @@ def main(args: argparse.Namespace):
 
   tokenizer = get_tokenizer(tokenizer_id)
   if tokenizer == "test" or args.dataset == "test":
-    input_requests = mock_requests(args.total_mock_requests) # e.g. [("AB", 2, 3)]
+    input_requests = mock_requests(args.total_mock_requests) # e.g. [("AB", 2, "AB", 3)]
   else:
     input_requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
 
-  benchmark_result = asyncio.run(
+  benchmark_result, request_outputs = asyncio.run(
       benchmark(
           api_url=api_url,
           tokenizer=tokenizer,
@@ -411,6 +430,11 @@ def main(args: argparse.Namespace):
     with open(file_name, "w") as outfile:
       json.dump(result_json, outfile)
 
+  if args.save_request_outputs:
+    file_path = args.request_outputs_file_path
+    with open(file_path, "w") as output_file:
+        json.dump([output.to_dict() for output in request_outputs], output_file, indent=4) 
+
 
 if __name__ == "__main__":
   parser = argparse.ArgumentParser(
@@ -506,6 +530,19 @@ def main(args: argparse.Namespace):
           " not implemented, use default empty str)"
       ),
   )
+  parser.add_argument(
+      "--save-request-outputs",
+      action="store_true",
+      help="Specify to store request outputs into a json file",
+  )
+  parser.add_argument(
+      "--request-outputs-file-path",
+      type=str,
+      default="/tmp/request-outputs.json",
+      help=(
+          "File path to store request outputs"
+      ),
+  )
 
   args = parser.parse_args()
   main(args)

benchmarks/eval_accuracy.py

@@ -0,0 +1,55 @@
+import argparse
+import nltk
+import evaluate
+import json
+
+import numpy as np
+
+def postprocess_text(preds, targets):
+    preds = [pred.strip() for pred in preds]
+    targets = [target.strip() for target in targets]
+
+    # rougeLSum expects newline after each sentence
+    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
+    targets = ["\n".join(nltk.sent_tokenize(target)) for target in targets]
+
+    return preds, targets
+
+
+def eval_accuracy(request_outputs_dict):
+    metric = evaluate.load("rouge")
+    nltk.download('punkt')
+    preds = []
+    targets = []
+    
+    for output in request_outputs_dict:
+        preds.append(output["generated_text"])
+        targets.append(output["original_output"])
+    preds, targets = postprocess_text(preds, targets)
+    result = metric.compute(
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
+    result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+    prediction_lens = [len(pred) for pred in preds]
+    result["gen_len"] = np.sum(prediction_lens)
+    result["gen_num"] = len(preds)
+    print("\nResults\n")
+    print(result)
+
+
+def main(args):
+    with open(args.output_path) as f:
+        request_outputs_dict = json.load(f)
+    
+    eval_accuracy(request_outputs_dict)        
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+      "--output_path", type=str,
+      default="/tmp/request-outputs.json",
+      help="File path which has original_output and inference generated_text.")
+    
+    args = parser.parse_args()
+    
+    main(args)

benchmarks/requirements.in

@@ -0,0 +1,3 @@
+nltk
+evaluate
+rouge-score