feat(model): Add more support model list and modify LLM benchmarks (#823)

README.md

@@ -115,16 +115,26 @@ At present, we have introduced several key features to showcase our current capa
 - **SMMF(Service-oriented Multi-model Management Framework)**
 
   We offer extensive model support, including dozens of large language models (LLMs) from both open-source and API agents, such as LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, and many more.
+  
   - [Vicuna](https://huggingface.co/Tribbiani/vicuna-13b)
   - [vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5)
   - [LLama2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
-  - [baichuan2-13b](https://huggingface.co/baichuan-inc)
+  - [baichuan2-13b](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat)
-  - [baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
+  - [baichuan2-7b](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)
   - [chatglm-6b](https://huggingface.co/THUDM/chatglm-6b)
   - [chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
+  - [chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)
   - [falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
   - [internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
-  - [Qwen-7B-Chat/Qwen-14B-Chat](https://huggingface.co/Qwen/)
+  - [internlm-chat-20b](https://huggingface.co/internlm/internlm-chat-20b)
+  - [qwen-7b-chat](https://huggingface.co/Qwen/Qwen-7B-Chat)
+  - [qwen-14b-chat](https://huggingface.co/Qwen/Qwen-14B-Chat)
+  - [wizardlm-13b](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)
+  - [orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b)
+  - [orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
+  - [openchat_3.5](https://huggingface.co/openchat/openchat_3.5)
+  - [zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)
+  - [mistral-7b-instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
 
   - Support API Proxy LLMs
     - [x] [ChatGPT](https://api.openai.com/)

README.zh.md

@@ -112,15 +112,27 @@ DB-GPT是一个开源的数据库领域大模型框架。目的是构建大模
 
 - **多模型支持与管理**
 
-  海量模型支持，包括开源、API代理等几十种大语言模型。如LLaMA/LLaMA2、Baichuan、ChatGLM、文心、通义、智谱等。
+  海量模型支持，包括开源、API代理等几十种大语言模型。如LLaMA/LLaMA2、Baichuan、ChatGLM、文心、通义、智谱等。当前已支持如下模型: 
-  - 支持多种大语言模型, 当前已支持如下模型: 
+
   - [Vicuna](https://huggingface.co/Tribbiani/vicuna-13b)
   - [vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5)
   - [LLama2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
-  - [baichuan2-13b](https://huggingface.co/baichuan-inc)
+  - [baichuan2-13b](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat)
-  - [baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
+  - [baichuan2-7b](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)
   - [chatglm-6b](https://huggingface.co/THUDM/chatglm-6b)
   - [chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
+  - [chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)
+  - [falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
+  - [internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
+  - [internlm-chat-20b](https://huggingface.co/internlm/internlm-chat-20b)
+  - [qwen-7b-chat](https://huggingface.co/Qwen/Qwen-7B-Chat)
+  - [qwen-14b-chat](https://huggingface.co/Qwen/Qwen-14B-Chat)
+  - [wizardlm-13b](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)
+  - [orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b)
+  - [orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
+  - [openchat_3.5](https://huggingface.co/openchat/openchat_3.5)
+  - [zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)
+  - [mistral-7b-instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
 
   - 支持在线代理模型 
     - [x] [ChatGPT](https://api.openai.com/)

pilot/configs/model_config.py

@@ -53,6 +53,8 @@ LLM_MODEL_CONFIG = {
     "chatglm-6b": os.path.join(MODEL_PATH, "chatglm-6b"),
     "chatglm2-6b": os.path.join(MODEL_PATH, "chatglm2-6b"),
     "chatglm2-6b-int4": os.path.join(MODEL_PATH, "chatglm2-6b-int4"),
+    # https://huggingface.co/THUDM/chatglm3-6b
+    "chatglm3-6b": os.path.join(MODEL_PATH, "chatglm3-6b"),
     "guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"),
     "falcon-40b": os.path.join(MODEL_PATH, "falcon-40b"),
     "gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"),
@@ -74,6 +76,18 @@ LLM_MODEL_CONFIG = {
     "baichuan-7b": os.path.join(MODEL_PATH, "baichuan-7b"),
     "baichuan2-7b": os.path.join(MODEL_PATH, "Baichuan2-7B-Chat"),
     "baichuan2-13b": os.path.join(MODEL_PATH, "Baichuan2-13B-Chat"),
+    # https://huggingface.co/Qwen/Qwen-7B-Chat
+    "qwen-7b-chat": os.path.join(MODEL_PATH, "Qwen-7B-Chat"),
+    # https://huggingface.co/Qwen/Qwen-7B-Chat-Int8
+    "qwen-7b-chat-int8": os.path.join(MODEL_PATH, "Qwen-7B-Chat-Int8"),
+    # https://huggingface.co/Qwen/Qwen-7B-Chat-Int4
+    "qwen-7b-chat-int4": os.path.join(MODEL_PATH, "Qwen-7B-Chat-Int4"),
+    # https://huggingface.co/Qwen/Qwen-14B-Chat
+    "qwen-14b-chat": os.path.join(MODEL_PATH, "Qwen-14B-Chat"),
+    # https://huggingface.co/Qwen/Qwen-14B-Chat-Int8
+    "qwen-14b-chat-int8": os.path.join(MODEL_PATH, "Qwen-14B-Chat-Int8"),
+    # https://huggingface.co/Qwen/Qwen-14B-Chat-Int4
+    "qwen-14b-chat-int4": os.path.join(MODEL_PATH, "Qwen-14B-Chat-Int4"),
     # (Llama2 based) We only support WizardLM-13B-V1.2 for now, which is trained from Llama-2 13b, see https://huggingface.co/WizardLM/WizardLM-13B-V1.2
     "wizardlm-13b": os.path.join(MODEL_PATH, "WizardLM-13B-V1.2"),
     # wget https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/resolve/main/vicuna-13b-v1.5.Q4_K_M.gguf -O models/ggml-model-q4_0.gguf
@@ -88,6 +102,30 @@ LLM_MODEL_CONFIG = {
     "codellama-13b-sql-sft": os.path.join(MODEL_PATH, "codellama-13b-sql-sft"),
     # For test now
     "opt-125m": os.path.join(MODEL_PATH, "opt-125m"),
+    # https://huggingface.co/microsoft/Orca-2-7b
+    "orca-2-7b": os.path.join(MODEL_PATH, "Orca-2-7b"),
+    # https://huggingface.co/microsoft/Orca-2-13b
+    "orca-2-13b": os.path.join(MODEL_PATH, "Orca-2-13b"),
+    # https://huggingface.co/openchat/openchat_3.5
+    "openchat_3.5": os.path.join(MODEL_PATH, "openchat_3.5"),
+    # https://huggingface.co/hfl/chinese-alpaca-2-7b
+    "chinese-alpaca-2-7b": os.path.join(MODEL_PATH, "chinese-alpaca-2-7b"),
+    # https://huggingface.co/hfl/chinese-alpaca-2-13b
+    "chinese-alpaca-2-13b": os.path.join(MODEL_PATH, "chinese-alpaca-2-13b"),
+    # https://huggingface.co/THUDM/codegeex2-6b
+    "codegeex2-6b": os.path.join(MODEL_PATH, "codegeex2-6b"),
+    # https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
+    "zephyr-7b-alpha": os.path.join(MODEL_PATH, "zephyr-7b-alpha"),
+    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
+    "mistral-7b-instruct-v0.1": os.path.join(MODEL_PATH, "Mistral-7B-Instruct-v0.1"),
+    # https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
+    "mistral-7b-openorca": os.path.join(MODEL_PATH, "Mistral-7B-OpenOrca"),
+    # https://huggingface.co/Xwin-LM/Xwin-LM-7B-V0.1
+    "xwin-lm-7b-v0.1": os.path.join(MODEL_PATH, "Xwin-LM-7B-V0.1"),
+    # https://huggingface.co/Xwin-LM/Xwin-LM-13B-V0.1
+    "xwin-lm-13b-v0.1": os.path.join(MODEL_PATH, "Xwin-LM-13B-V0.1"),
+    # https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1
+    "xwin-lm-70b-v0.1": os.path.join(MODEL_PATH, "Xwin-LM-70B-V0.1"),
 }
 
 EMBEDDING_MODEL_CONFIG = {

pilot/model/base.py

@@ -8,6 +8,7 @@ from dataclasses import dataclass, asdict
 import time
 from datetime import datetime
 from pilot.utils.parameter_utils import ParameterDescription
+from pilot.utils.model_utils import GPUInfo
 
 
 class Message(TypedDict):
@@ -53,6 +54,8 @@ class WorkerApplyType(str, Enum):
 class ModelInferenceMetrics:
     """A class to represent metrics for assessing the inference performance of a LLM."""
 
+    collect_index: Optional[int] = 0
+
     start_time_ms: Optional[int] = None
     """The timestamp (in milliseconds) when the model inference starts."""
 
@@ -83,6 +86,12 @@ class ModelInferenceMetrics:
     speed_per_second: Optional[float] = None
     """The average number of tokens generated per second."""
 
+    current_gpu_infos: Optional[List[GPUInfo]] = None
+    """Current gpu information, all devices"""
+
+    avg_gpu_infos: Optional[List[GPUInfo]] = None
+    """Average memory usage across all collection points"""
+
     @staticmethod
     def create_metrics(
         last_metrics: Optional["ModelInferenceMetrics"] = None,
@@ -99,6 +108,8 @@ class ModelInferenceMetrics:
         completion_tokens = last_metrics.completion_tokens if last_metrics else None
         total_tokens = last_metrics.total_tokens if last_metrics else None
         speed_per_second = last_metrics.speed_per_second if last_metrics else None
+        current_gpu_infos = last_metrics.current_gpu_infos if last_metrics else None
+        avg_gpu_infos = last_metrics.avg_gpu_infos if last_metrics else None
 
         if not start_time_ms:
             start_time_ms = time.time_ns() // 1_000_000
@@ -116,6 +127,8 @@ class ModelInferenceMetrics:
             completion_tokens=completion_tokens,
             total_tokens=total_tokens,
             speed_per_second=speed_per_second,
+            current_gpu_infos=current_gpu_infos,
+            avg_gpu_infos=avg_gpu_infos,
         )
 
     def to_dict(self) -> Dict:

pilot/model/cluster/worker/default_worker.py

@@ -11,7 +11,7 @@ from pilot.model.base import ModelOutput, ModelInferenceMetrics
 from pilot.model.loader import ModelLoader, _get_model_real_path
 from pilot.model.parameter import ModelParameters
 from pilot.model.cluster.worker_base import ModelWorker
-from pilot.utils.model_utils import _clear_model_cache
+from pilot.utils.model_utils import _clear_model_cache, _get_current_cuda_memory
 from pilot.utils.parameter_utils import EnvArgumentParser, _get_dict_from_obj
 from pilot.utils.tracer import root_tracer, SpanType, SpanTypeRunName
 from pilot.utils.system_utils import get_system_info
@@ -363,6 +363,7 @@ def _new_metrics_from_model_output(
     usage: Optional[Dict] = None,
 ) -> ModelInferenceMetrics:
     metrics = ModelInferenceMetrics.create_metrics(last_metric)
+    metrics.collect_index = last_metric.collect_index + 1
     if is_first_generate:
         logger.info(f"is_first_generate, usage: {usage}")
         metrics.first_completion_time_ms = time.time_ns() // 1_000_000
@@ -385,6 +386,13 @@ def _new_metrics_from_model_output(
         metrics.first_completion_tokens = completion_tokens
         if completion_tokens == 1:
             metrics.first_token_time_ms = metrics.first_completion_time_ms
+    if (
+        not is_first_generate
+        and metrics.first_token_time_ms is None
+        and completion_tokens == 1
+    ):
+        # Case: first generate has 0 token, and second generate has 1 token
+        metrics.first_token_time_ms = time.time_ns() // 1_000_000
 
     if prompt_tokens:
         metrics.prompt_tokens = prompt_tokens
@@ -400,4 +408,28 @@ def _new_metrics_from_model_output(
         # time cost(seconds)
         duration = (metrics.current_time_ms - metrics.start_time_ms) / 1000.0
         metrics.speed_per_second = total_tokens / duration
+
+    current_gpu_infos = _get_current_cuda_memory()
+    metrics.current_gpu_infos = current_gpu_infos
+    if not metrics.avg_gpu_infos:
+        metrics.avg_gpu_infos = current_gpu_infos
+    elif current_gpu_infos:
+        for i, last_avg in enumerate(metrics.avg_gpu_infos):
+            allocated_memory_gb = (
+                last_avg.allocated_memory_gb * (metrics.collect_index - 1)
+                + current_gpu_infos[i].allocated_memory_gb
+            )
+            metrics.avg_gpu_infos[i].allocated_memory_gb = (
+                allocated_memory_gb / metrics.collect_index
+            )
+            metrics.avg_gpu_infos[i].total_memory_gb = current_gpu_infos[
+                i
+            ].total_memory_gb
+            metrics.avg_gpu_infos[i].cached_memory_gb = current_gpu_infos[
+                i
+            ].cached_memory_gb
+            metrics.avg_gpu_infos[i].available_memory_gb = current_gpu_infos[
+                i
+            ].available_memory_gb
+
     return metrics

pilot/utils/benchmarks/llm/fastchat_benchmarks_inference.py

@@ -42,7 +42,7 @@ def generate_stream(
     params: Dict,
     device: str,
     context_len: int,
-    stream_interval: int = 2,
+    stream_interval: int = 1,
     judge_sent_end: bool = False,
 ):
     if hasattr(model, "device"):

pilot/utils/benchmarks/llm/llm_benchmarks.py

@@ -8,6 +8,7 @@ import argparse
 import logging
 import traceback
 from pilot.configs.model_config import ROOT_PATH, LLM_MODEL_CONFIG
+from datetime import timedelta, datetime
 
 from pilot.model.cluster.worker.manager import (
     run_worker_manager,
@@ -53,13 +54,20 @@ prompt_file_map = {
 METRICS_HEADERS = [
     # Params
     "model_name",
+    "gpu_nums",
     "parallel_nums",
     "input_length",
     "output_length",
     # Merge parallel result
     "test_time_cost_ms",
     "test_total_tokens",
-    "test_speed_per_second",  # (tokens / s)
+    # avg_test_speed_per_second: (tokens / s), test_total_tokens / (test_time_cost_ms / 1000.0)
+    "avg_test_speed_per_second(tokens/s)",
+    # avg_first_token_latency_ms: sum(first_token_time_ms) / parallel_nums
+    "avg_first_token_latency_ms",
+    # avg_latency_ms: sum(end_time_ms - start_time_ms) / parallel_nums
+    "avg_latency_ms",
+    "gpu_mem(GiB)",
     # Detail for each task
     "start_time_ms",
     "end_time_ms",
@@ -106,7 +114,11 @@ def build_param(
 
 
 async def run_batch(
-    wh, input_len: int, output_len: int, parallel_num: int, output_file: str
+    wh: WorkerManager,
+    input_len: int,
+    output_len: int,
+    parallel_num: int,
+    output_file: str,
 ):
     tasks = []
     prompt = read_prompt_from_file("11k")
@@ -117,6 +129,10 @@ async def run_batch(
             max_input_str_len *= 2
         prompt = prompt[-max_input_str_len:]
 
+    # Warmup first
+    params = build_param(input_len, output_len, prompt, system_prompt="")
+    await wh.generate(params)
+
     for _ in range(parallel_num):
         params = build_param(input_len, output_len, prompt, system_prompt="")
         tasks.append(wh.generate(params))
@@ -129,6 +145,10 @@ async def run_batch(
 
     test_time_cost_ms = end_time_ms - start_time_ms
     test_total_tokens = 0
+    first_token_latency_ms = 0
+    latency_ms = 0
+    gpu_nums = 0
+    avg_gpu_mem = 0
     rows = []
     for r in results:
         metrics = r.metrics
@@ -136,9 +156,22 @@ async def run_batch(
             metrics = ModelInferenceMetrics(**metrics)
         print(r)
         test_total_tokens += metrics.total_tokens
+        first_token_latency_ms += metrics.first_token_time_ms - metrics.start_time_ms
+        latency_ms += metrics.end_time_ms - metrics.start_time_ms
         row_data = metrics.to_dict()
+        del row_data["collect_index"]
+        if "avg_gpu_infos" in row_data:
+            avg_gpu_infos = row_data["avg_gpu_infos"]
+            gpu_nums = len(avg_gpu_infos)
+            avg_gpu_mem = (
+                sum(i["allocated_memory_gb"] for i in avg_gpu_infos) / gpu_nums
+            )
+            del row_data["avg_gpu_infos"]
+            del row_data["current_gpu_infos"]
         rows.append(row_data)
-    test_speed_per_second = test_total_tokens / (test_time_cost_ms / 1000.0)
+    avg_test_speed_per_second = test_total_tokens / (test_time_cost_ms / 1000.0)
+    avg_first_token_latency_ms = first_token_latency_ms / len(results)
+    avg_latency_ms = latency_ms / len(results)
 
     with open(output_file, "a", newline="", encoding="utf-8") as f:
         writer = csv.DictWriter(f, fieldnames=METRICS_HEADERS)
@@ -152,7 +185,11 @@ async def run_batch(
             row["output_length"] = output_len
             row["test_time_cost_ms"] = test_time_cost_ms
             row["test_total_tokens"] = test_total_tokens
-            row["test_speed_per_second"] = test_speed_per_second
+            row["avg_test_speed_per_second(tokens/s)"] = avg_test_speed_per_second
+            row["avg_first_token_latency_ms"] = avg_first_token_latency_ms
+            row["avg_latency_ms"] = avg_latency_ms
+            row["gpu_nums"] = gpu_nums
+            row["gpu_mem(GiB)"] = avg_gpu_mem
             writer.writerow(row)
     print(
         f"input_len: {input_len}, output_len: {output_len}, parallel_num: {parallel_num}, save result to {output_file}"
@@ -164,7 +201,9 @@ async def run_model(wh: WorkerManager) -> None:
     if not result_csv_file:
         result_csv_file = get_result_csv_file()
     if os.path.exists(result_csv_file):
-        os.rename(result_csv_file, f"{result_csv_file}.bak.csv")
+        now = datetime.now()
+        now_str = now.strftime("%Y-%m-%d")
+        os.rename(result_csv_file, f"{result_csv_file}.bak_{now_str}.csv")
     for parallel_num in parallel_nums:
         for input_len, output_len in zip(input_lens, output_lens):
             try:
@@ -176,6 +215,8 @@ async def run_model(wh: WorkerManager) -> None:
                 logging.error(
                     f"Run benchmarks error, input_len: {input_len}, output_len: {output_len}, parallel_num: {parallel_num}, error message: {msg}"
                 )
+                if "torch.cuda.OutOfMemoryError" in msg:
+                    return
 
     sys.exit(0)
 

pilot/utils/model_utils.py

@@ -1,3 +1,5 @@
+from typing import List, Tuple
+from dataclasses import dataclass
 import logging
 
 logger = logging.getLogger(__name__)
@@ -37,3 +39,46 @@ def _clear_torch_cache(device="cuda"):
                     torch.cuda.ipc_collect()
         else:
             logger.info("No cuda or mps, not support clear torch cache yet")
+
+
+@dataclass
+class GPUInfo:
+    total_memory_gb: float
+    allocated_memory_gb: float
+    cached_memory_gb: float
+    available_memory_gb: float
+
+
+def _get_current_cuda_memory() -> List[GPUInfo]:
+    try:
+        import torch
+    except ImportError:
+        logger.warn("Torch not installed")
+        return []
+    if torch.cuda.is_available():
+        num_gpus = torch.cuda.device_count()
+        gpu_infos = []
+        for gpu_id in range(num_gpus):
+            with torch.cuda.device(gpu_id):
+                device = torch.cuda.current_device()
+                gpu_properties = torch.cuda.get_device_properties(device)
+                total_memory = round(gpu_properties.total_memory / (1.0 * 1024**3), 2)
+                allocated_memory = round(
+                    torch.cuda.memory_allocated() / (1.0 * 1024**3), 2
+                )
+                cached_memory = round(
+                    torch.cuda.memory_reserved() / (1.0 * 1024**3), 2
+                )
+                available_memory = total_memory - allocated_memory
+                gpu_infos.append(
+                    GPUInfo(
+                        total_memory_gb=total_memory,
+                        allocated_memory_gb=allocated_memory,
+                        cached_memory_gb=cached_memory,
+                        available_memory_gb=available_memory,
+                    )
+                )
+        return gpu_infos
+    else:
+        logger.warn("CUDA is not available.")
+        return []

scripts/run_llm_benchmarks.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
-default_input_lens="64,64,64,512,1024,1024,2048"
+default_input_lens="8,8,256,1024"
-default_output_lens="256,512,1024,1024,1024,2048,2048"
+default_output_lens="256,512,1024,1024"
 default_parallel_nums="1,2,4,16,32"
 
 input_lens=${1:-$default_input_lens}