Merge branch 'main' of github.com:triton-inference-server/vllm_backen…

…d into jacky-vllm-additional-outputs

src/model.py

@@ -25,21 +25,25 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import asyncio
+import base64
 import gc
 import json
 import os
 import queue
 import threading
+from io import BytesIO
 from typing import Dict, List
 
 import numpy as np
 import torch
 import triton_python_backend_utils as pb_utils
+from PIL import Image
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.lora.request import LoRARequest
 from vllm.sampling_params import SamplingParams
 from vllm.utils import random_uuid
+from vllm.version import __version__ as _VLLM_VERSION
 
 from utils.metrics import VllmStatLogger
 
@@ -67,7 +71,7 @@ def auto_complete_config(cls, auto_complete_model_config):
 
     @staticmethod
     def _auto_complete_inputs_and_outputs(auto_complete_model_config):
-        # Inputs/Outputs expected by the backend.
+        # Inputs expected by the backend.
         inputs = [
             {"name": "text_input", "data_type": "TYPE_STRING", "dims": [1]},
             {
@@ -107,6 +111,16 @@ def _auto_complete_inputs_and_outputs(auto_complete_model_config):
                 "optional": True,
             },
         ]
+        if _VLLM_VERSION >= "0.6.3.post1":
+            inputs.append(
+                {
+                    "name": "image",
+                    "data_type": "TYPE_STRING",
+                    "dims": [-1],  # can be multiple images as separate elements
+                    "optional": True,
+                }
+            )
+        # Outputs expected by the backend.
         outputs = [
             {"name": "text_output", "data_type": "TYPE_STRING", "dims": [-1]},
             {"name": "finish_reason", "data_type": "TYPE_STRING", "dims": [-1]},
@@ -313,6 +327,21 @@ def _get_input_tensors(self, request):
         if isinstance(prompt, bytes):
             prompt = prompt.decode("utf-8")
 
+        # image
+        if _VLLM_VERSION >= "0.6.3.post1":
+            images = pb_utils.get_input_tensor_by_name(request, "image")
+            if images:
+                images_vllm = []
+                for image_np in images.as_numpy():
+                    image_b = base64.b64decode(image_np.decode("utf-8"))
+                    image_rgb = Image.open(BytesIO(image_b)).convert("RGB")
+                    images_vllm.append(image_rgb)
+                if len(images_vllm) > 0:
+                    prompt = {
+                        "prompt": prompt,
+                        "multi_modal_data": {"image": images_vllm},
+                    }
+
         # stream
         stream = pb_utils.get_input_tensor_by_name(request, "stream")
         if stream:

src/utils/metrics.py

@@ -32,7 +32,7 @@
 from vllm.engine.metrics import StatLoggerBase as VllmStatLoggerBase
 from vllm.engine.metrics import Stats as VllmStats
 from vllm.engine.metrics import SupportsMetricsInfo, build_1_2_5_buckets
-
+from vllm.version import __version__ as _VLLM_VERSION
 
 class TritonMetrics:
     def __init__(self, labels: List[str], max_model_len: int):
@@ -76,11 +76,14 @@ def __init__(self, labels: List[str], max_model_len: int):
             description="Number of generation tokens processed.",
             kind=pb_utils.MetricFamily.HISTOGRAM,
         )
-        self.histogram_best_of_request_family = pb_utils.MetricFamily(
-            name="vllm:request_params_best_of",
-            description="Histogram of the best_of request parameter.",
-            kind=pb_utils.MetricFamily.HISTOGRAM,
-        )
+        # 'best_of' metric has been hidden since vllm 0.6.3
+        # https://github.com/vllm-project/vllm/commit/cbc2ef55292b2af6ff742095c030e8425124c005
+        if _VLLM_VERSION < "0.6.3":
+            self.histogram_best_of_request_family = pb_utils.MetricFamily(
+                name="vllm:request_params_best_of",
+                description="Histogram of the best_of request parameter.",
+                kind=pb_utils.MetricFamily.HISTOGRAM,
+            )
         self.histogram_n_request_family = pb_utils.MetricFamily(
             name="vllm:request_params_n",
             description="Histogram of the n request parameter.",
@@ -159,10 +162,11 @@ def __init__(self, labels: List[str], max_model_len: int):
                 buckets=build_1_2_5_buckets(max_model_len),
             )
         )
-        self.histogram_best_of_request = self.histogram_best_of_request_family.Metric(
-            labels=labels,
-            buckets=[1, 2, 5, 10, 20],
-        )
+        if _VLLM_VERSION < "0.6.3":
+            self.histogram_best_of_request = self.histogram_best_of_request_family.Metric(
+                labels=labels,
+                buckets=[1, 2, 5, 10, 20],
+            )
         self.histogram_n_request = self.histogram_n_request_family.Metric(
             labels=labels,
             buckets=[1, 2, 5, 10, 20],
@@ -247,10 +251,10 @@ def log(self, stats: VllmStats) -> None:
                 self.metrics.histogram_num_generation_tokens_request,
                 stats.num_generation_tokens_requests,
             ),
-            (self.metrics.histogram_best_of_request, stats.best_of_requests),
             (self.metrics.histogram_n_request, stats.n_requests),
         ]
-
+        if _VLLM_VERSION < "0.6.3":
+            histogram_metrics.append((self.metrics.histogram_best_of_request, stats.best_of_requests))
         for metric, data in counter_metrics:
             self._log_counter(metric, data)
         for metric, data in histogram_metrics: