Support input for llama3.2 multi-modal model (#69)

Co-authored-by: jibxie <[email protected]>

Author: xiejibing

src/model.py

@@ -31,7 +31,9 @@
 import queue
 import threading
 from typing import Dict, List
-
+import base64
+from PIL import Image
+from io import BytesIO
 import numpy as np
 import torch
 import triton_python_backend_utils as pb_utils
@@ -40,6 +42,7 @@
 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
 
@@ -71,6 +74,14 @@ def auto_complete_config(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 = [{"name": "text_output", "data_type": "TYPE_STRING", "dims": [-1]}]
 
         # Store the model configuration as a dictionary.
@@ -385,6 +396,25 @@ async def generate(self, request):
             ).as_numpy()[0]
             if isinstance(prompt, bytes):
                 prompt = prompt.decode("utf-8")
+
+            if _VLLM_VERSION >= "0.6.3.post1":
+                image_input_tensor = pb_utils.get_input_tensor_by_name(
+                    request, "image"
+                )
+                if image_input_tensor:
+                    image_list = []
+                    for image_raw in image_input_tensor.as_numpy():
+                        image_data = base64.b64decode(image_raw.decode("utf-8"))
+                        image = Image.open(BytesIO(image_data)).convert("RGB")
+                        image_list.append(image)
+                    if len(image_list) > 0:
+                        prompt = {
+                            "prompt": prompt,
+                            "multi_modal_data": {
+                                "image": image_list
+                            }
+                        }
+
             stream = pb_utils.get_input_tensor_by_name(request, "stream")
             if stream:
                 stream = stream.as_numpy()[0]

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: