Merge tag 'v0.5.2' into v0.5.2-dtk24.04.1

examples/fuyu_example.py

+import requests
+from PIL import Image
+
+from vllm import LLM, SamplingParams
+
+
+def run_fuyu():
+    llm = LLM(model="adept/fuyu-8b", max_model_len=4096)
+
+    # single-image prompt
+    prompt = "What is the highest life expectancy at of male?\n"
+    url = "https://huggingface.co/adept/fuyu-8b/resolve/main/chart.png"
+    image = Image.open(requests.get(url, stream=True).raw)
+    sampling_params = SamplingParams(temperature=0, max_tokens=64)
+
+    outputs = llm.generate(
+        {
+            "prompt": prompt,
+            "multi_modal_data": {
+                "image": image
+            },
+        },
+        sampling_params=sampling_params)
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
+
+if __name__ == "__main__":
+    run_fuyu()

examples/llava_example.py

-import argparse
 import os
 import subprocess
 
-import torch
 from PIL import Image
 
 from vllm import LLM
-from vllm.multimodal.image import ImageFeatureData, ImagePixelData
 
 # The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
 # You can use `.buildkite/download-images.sh` to download them
 
 
-def run_llava_pixel_values(*, disable_image_processor: bool = False):
-    llm = LLM(
-        model="llava-hf/llava-1.5-7b-hf",
-        image_input_type="pixel_values",
-        image_token_id=32000,
-        image_input_shape="1,3,336,336",
-        image_feature_size=576,
-        disable_image_processor=disable_image_processor,
-    )
+def run_llava():
+    llm = LLM(model="llava-hf/llava-1.5-7b-hf")
 
-    prompt = "<image>" * 576 + (
-        "\nUSER: What is the content of this image?\nASSISTANT:")
+    prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"
 
-    if disable_image_processor:
-        image = torch.load("images/stop_sign_pixel_values.pt")
-    else:
-        image = Image.open("images/stop_sign.jpg")
+    image = Image.open("images/stop_sign.jpg")
 
     outputs = llm.generate({
         "prompt": prompt,
-        "multi_modal_data": ImagePixelData(image),
+        "multi_modal_data": {
+            "image": image
+        },
     })
 
     for o in outputs:
@@ -40,45 +28,11 @@ def run_llava_pixel_values(*, disable_image_processor: bool = False):
         print(generated_text)
 
 
-def run_llava_image_features():
-    llm = LLM(
-        model="llava-hf/llava-1.5-7b-hf",
-        image_input_type="image_features",
-        image_token_id=32000,
-        image_input_shape="1,576,1024",
-        image_feature_size=576,
-    )
-
-    prompt = "<image>" * 576 + (
-        "\nUSER: What is the content of this image?\nASSISTANT:")
-
-    image: torch.Tensor = torch.load("images/stop_sign_image_features.pt")
-
-    outputs = llm.generate({
-        "prompt": prompt,
-        "multi_modal_data": ImageFeatureData(image),
-    })
-
-    for o in outputs:
-        generated_text = o.outputs[0].text
-        print(generated_text)
-
-
-def main(args):
-    if args.type == "pixel_values":
-        run_llava_pixel_values()
-    else:
-        run_llava_image_features()
+def main():
+    run_llava()
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Demo on Llava")
-    parser.add_argument("--type",
-                        type=str,
-                        choices=["pixel_values", "image_features"],
-                        default="pixel_values",
-                        help="image input type")
-    args = parser.parse_args()
     # Download from s3
     s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
     local_directory = "images"
@@ -95,4 +49,4 @@ if __name__ == "__main__":
         local_directory,
         "--no-sign-request",
     ])
-    main(args)
+    main()

examples/llava_next_example.py

+from io import BytesIO
+
+import requests
+from PIL import Image
+
+from vllm import LLM, SamplingParams
+
+
+def run_llava_next():
+    llm = LLM(model="llava-hf/llava-v1.6-mistral-7b-hf", max_model_len=4096)
+
+    prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
+    url = "https://h2o-release.s3.amazonaws.com/h2ogpt/bigben.jpg"
+    image = Image.open(BytesIO(requests.get(url).content))
+    sampling_params = SamplingParams(temperature=0.8,
+                                     top_p=0.95,
+                                     max_tokens=100)
+
+    outputs = llm.generate(
+        {
+            "prompt": prompt,
+            "multi_modal_data": {
+                "image": image
+            }
+        },
+        sampling_params=sampling_params)
+
+    generated_text = ""
+    for o in outputs:
+        generated_text += o.outputs[0].text
+
+    print(f"LLM output:{generated_text}")
+
+
+if __name__ == "__main__":
+    run_llava_next()

examples/llm_engine_example.py

@@ -2,6 +2,7 @@ import argparse
 from typing import List, Tuple
 
 from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
+from vllm.utils import FlexibleArgumentParser
 
 
 def create_test_prompts() -> List[Tuple[str, SamplingParams]]:
@@ -55,7 +56,7 @@ def main(args: argparse.Namespace):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
+    parser = FlexibleArgumentParser(
         description='Demo on using the LLMEngine class directly')
     parser = EngineArgs.add_cli_args(parser)
     args = parser.parse_args()

examples/offline_inference_distributed.py

@@ -5,7 +5,7 @@ distributively on a multi-nodes cluster.
 Learn more about Ray Data in https://docs.ray.io/en/latest/data/data.html
 """
 
-from typing import Dict
+from typing import Any, Dict, List
 
 import numpy as np
 import ray
@@ -40,8 +40,8 @@ class LLMPredictor:
         # The output is a list of RequestOutput objects that contain the prompt,
         # generated text, and other information.
         outputs = self.llm.generate(batch["text"], sampling_params)
-        prompt = []
-        generated_text = []
+        prompt: List[str] = []
+        generated_text: List[str] = []
         for output in outputs:
             prompt.append(output.prompt)
             generated_text.append(' '.join([o.text for o in output.outputs]))
@@ -71,7 +71,7 @@ def scheduling_strategy_fn():
         pg, placement_group_capture_child_tasks=True))
 
 
-resources_kwarg = {}
+resources_kwarg: Dict[str, Any] = {}
 if tensor_parallel_size == 1:
     # For tensor_parallel_size == 1, we simply set num_gpus=1.
     resources_kwarg["num_gpus"] = 1

examples/offline_inference_mlpspeculator.py

+import gc
+import time
+from typing import List
+
+from vllm import LLM, SamplingParams
+
+
+def time_generation(llm: LLM, prompts: List[str],
+                    sampling_params: SamplingParams):
+    # Generate texts from the prompts. The output is a list of RequestOutput
+    # objects that contain the prompt, generated text, and other information.
+    # Warmup first
+    llm.generate(prompts, sampling_params)
+    llm.generate(prompts, sampling_params)
+    start = time.time()
+    outputs = llm.generate(prompts, sampling_params)
+    end = time.time()
+    print((end - start) / sum([len(o.outputs[0].token_ids) for o in outputs]))
+    # Print the outputs.
+    for output in outputs:
+        generated_text = output.outputs[0].text
+        print(f"text: {generated_text!r}")
+
+
+if __name__ == "__main__":
+
+    template = (
+        "Below is an instruction that describes a task. Write a response "
+        "that appropriately completes the request.\n\n### Instruction:\n{}"
+        "\n\n### Response:\n")
+
+    # Sample prompts.
+    prompts = [
+        "Write about the president of the United States.",
+    ]
+    prompts = [template.format(prompt) for prompt in prompts]
+    # Create a sampling params object.
+    sampling_params = SamplingParams(temperature=0.0, max_tokens=200)
+
+    # Create an LLM without spec decoding
+    llm = LLM(model="meta-llama/Llama-2-13b-chat-hf")
+
+    print("Without speculation")
+    time_generation(llm, prompts, sampling_params)
+
+    del llm
+    gc.collect()
+
+    # Create an LLM with spec decoding
+    llm = LLM(
+        model="meta-llama/Llama-2-13b-chat-hf",
+        speculative_model="ibm-fms/llama-13b-accelerator",
+        # These are currently required for MLPSpeculator decoding
+        use_v2_block_manager=True,
+    )
+
+    print("With speculation")
+    time_generation(llm, prompts, sampling_params)

examples/openai_vision_api_client.py

+"""An example showing how to use vLLM to serve VLMs.
+
+Launch the vLLM server with the following command:
+python -m vllm.entrypoints.openai.api_server \
+    --model llava-hf/llava-1.5-7b-hf \
+    --chat-template template_llava.jinja
+"""
+import base64
+
+import requests
+from openai import OpenAI
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    # defaults to os.environ.get("OPENAI_API_KEY")
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+models = client.models.list()
+model = models.data[0].id
+
+image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
+
+# Use image url in the payload
+chat_completion_from_url = client.chat.completions.create(
+    messages=[{
+        "role":
+        "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "What’s in this image?"
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url
+                },
+            },
+        ],
+    }],
+    model=model,
+)
+
+result = chat_completion_from_url.choices[0].message.content
+print(f"Chat completion output:{result}")
+
+
+# Use base64 encoded image in the payload
+def encode_image_base64_from_url(image_url: str) -> str:
+    """Encode an image retrieved from a remote url to base64 format."""
+
+    with requests.get(image_url) as response:
+        response.raise_for_status()
+        result = base64.b64encode(response.content).decode('utf-8')
+
+    return result
+
+
+image_base64 = encode_image_base64_from_url(image_url=image_url)
+chat_completion_from_base64 = client.chat.completions.create(
+    messages=[{
+        "role":
+        "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "What’s in this image?"
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/jpeg;base64,{image_base64}"
+                },
+            },
+        ],
+    }],
+    model=model,
+)
+
+result = chat_completion_from_base64.choices[0].message.content
+print(f"Chat completion output:{result}")

examples/paligemma_example.py

+import os
+import subprocess
+
+from PIL import Image
+
+from vllm import LLM
+
+# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
+# You can use `.buildkite/download-images.sh` to download them
+
+
+def run_paligemma():
+    llm = LLM(model="google/paligemma-3b-mix-224")
+
+    prompt = "caption es"
+
+    image = Image.open("images/stop_sign.jpg")
+
+    outputs = llm.generate({
+        "prompt": prompt,
+        "multi_modal_data": {
+            "image": image
+        },
+    })
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
+
+def main():
+    run_paligemma()
+
+
+if __name__ == "__main__":
+    # Download from s3
+    s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
+    local_directory = "images"
+
+    # Make sure the local directory exists or create it
+    os.makedirs(local_directory, exist_ok=True)
+
+    # Use AWS CLI to sync the directory, assume anonymous access
+    subprocess.check_call([
+        "aws",
+        "s3",
+        "sync",
+        s3_bucket_path,
+        local_directory,
+        "--no-sign-request",
+    ])
+    main()

examples/phi3v_example.py

+import os
+import subprocess
+
+from PIL import Image
+
+from vllm import LLM, SamplingParams
+
+# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
+# You can use `.buildkite/download-images.sh` to download them
+
+
+def run_phi3v():
+    model_path = "microsoft/Phi-3-vision-128k-instruct"
+
+    # Note: The default setting of max_num_seqs (256) and
+    # max_model_len (128k) for this model may cause OOM.
+    # You may lower either to run this example on lower-end GPUs.
+
+    # In this example, we override max_num_seqs to 5 while
+    # keeping the original context length of 128k.
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        max_num_seqs=5,
+    )
+
+    image = Image.open("images/cherry_blossom.jpg")
+
+    # single-image prompt
+    prompt = "<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n"  # noqa: E501
+    sampling_params = SamplingParams(temperature=0, max_tokens=64)
+
+    outputs = llm.generate(
+        {
+            "prompt": prompt,
+            "multi_modal_data": {
+                "image": image
+            },
+        },
+        sampling_params=sampling_params)
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
+
+if __name__ == "__main__":
+    s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
+    local_directory = "images"
+
+    # Make sure the local directory exists or create it
+    os.makedirs(local_directory, exist_ok=True)
+
+    # Use AWS CLI to sync the directory, assume anonymous access
+    subprocess.check_call([
+        "aws",
+        "s3",
+        "sync",
+        s3_bucket_path,
+        local_directory,
+        "--no-sign-request",
+    ])
+    run_phi3v()

examples/production_monitoring/Otel.md

+# Setup OpenTelemetry POC
+
+1. Install OpenTelemetry packages:
+    ```
+    pip install \
+        opentelemetry-sdk \
+        opentelemetry-api \
+        opentelemetry-exporter-otlp \
+        opentelemetry-semantic-conventions-ai
+    ```
+
+1. Start Jaeger in a docker container:
+    ```
+    # From: https://www.jaegertracing.io/docs/1.57/getting-started/
+    docker run --rm --name jaeger \
+        -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \
+        -p 6831:6831/udp \
+        -p 6832:6832/udp \
+        -p 5778:5778 \
+        -p 16686:16686 \
+        -p 4317:4317 \
+        -p 4318:4318 \
+        -p 14250:14250 \
+        -p 14268:14268 \
+        -p 14269:14269 \
+        -p 9411:9411 \
+        jaegertracing/all-in-one:1.57
+    ```
+
+1. In a new shell, export Jaeger IP:
+    ```
+    export JAEGER_IP=$(docker inspect   --format '{{ .NetworkSettings.IPAddress }}' jaeger)
+    export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
+    ```
+    Then set vLLM's service name for OpenTelemetry, enable insecure connections to Jaeger and run vLLM:
+    ```
+    export OTEL_SERVICE_NAME="vllm-server"
+    export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
+    python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
+    ```
+
+1. In a new shell, send requests with trace context from a dummy client
+    ```
+    export JAEGER_IP=$(docker inspect --format '{{ .NetworkSettings.IPAddress }}' jaeger)
+    export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
+    export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
+    export OTEL_SERVICE_NAME="client-service"
+    python dummy_client.py
+    ```
+
+1. Open Jaeger webui: http://localhost:16686/
+
+    In the search pane, select `vllm-server` service and hit `Find Traces`. You should get a list of traces, one for each request.
+    ![Traces](https://i.imgur.com/GYHhFjo.png)
+
+1. Clicking on a trace will show its spans and their tags. In this demo, each trace has 2 spans. One from the dummy client containing the prompt text and one from vLLM containing metadata about the request.
+![Spans details](https://i.imgur.com/OPf6CBL.png)
+
+## Exporter Protocol
+OpenTelemetry supports either `grpc` or `http/protobuf` as the transport protocol for trace data in the exporter.
+By default, `grpc` is used. To set `http/protobuf` as the protocol, configure the `OTEL_EXPORTER_OTLP_TRACES_PROTOCOL` environment variable as follows:
+```
+export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
+export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://$JAEGER_IP:4318/v1/traces
+python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
+```
+
+## Instrumentation of FastAPI
+OpenTelemetry allows automatic instrumentation of FastAPI.
+1. Install the instrumentation library
+    ```
+    pip install opentelemetry-instrumentation-fastapi
+    ```
+
+1. Run vLLM with `opentelemetry-instrument`
+    ```
+    opentelemetry-instrument python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" 
+    ```
+
+1. Send a request to vLLM and find its trace in Jaeger. It should contain spans from FastAPI.
+
+![FastAPI Spans](https://i.imgur.com/hywvoOJ.png)
\ No newline at end of file

examples/production_monitoring/dummy_client.py

+import requests
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+    OTLPSpanExporter)
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import (BatchSpanProcessor,
+                                            ConsoleSpanExporter)
+from opentelemetry.trace import SpanKind, set_tracer_provider
+from opentelemetry.trace.propagation.tracecontext import (
+    TraceContextTextMapPropagator)
+
+trace_provider = TracerProvider()
+set_tracer_provider(trace_provider)
+
+trace_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
+trace_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))
+
+tracer = trace_provider.get_tracer("dummy-client")
+
+url = "http://localhost:8000/v1/completions"
+with tracer.start_as_current_span("client-span", kind=SpanKind.CLIENT) as span:
+    prompt = "San Francisco is a"
+    span.set_attribute("prompt", prompt)
+    headers = {}
+    TraceContextTextMapPropagator().inject(headers)
+    payload = {
+        "model": "facebook/opt-125m",
+        "prompt": prompt,
+        "max_tokens": 10,
+        "best_of": 20,
+        "n": 3,
+        "use_beam_search": "true",
+        "temperature": 0.0,
+        # "stream": True,
+    }
+    response = requests.post(url, headers=headers, json=payload)

examples/production_monitoring/grafana.json

 {
   "__inputs": [
-    {
-      "name": "DS_PROMETHEUS",
-      "label": "prometheus",
-      "description": "",
-      "type": "datasource",
-      "pluginId": "prometheus",
-      "pluginName": "Prometheus"
-    }
   ],
   "__elements": {},
   "__requires": [
@@ -1215,11 +1207,21 @@
   "templating": {
     "list": [
       {
+        "type": "datasource",
+        "name": "DS_PROMETHEUS",
+        "label": "datasource",
         "current": {},
-        "datasource": {
-          "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
-        },
+        "hide": 0,
+        "includeAll": false,
+        "multi": false,
+        "options": [],
+        "query": "prometheus",
+        "queryValue": "",
+        "refresh": 1,
+        "regex": "",
+        "skipUrlSync": false
+      },
+      {
         "definition": "label_values(model_name)",
         "hide": 0,
         "includeAll": false,
@@ -1250,3 +1252,4 @@
   "version": 1,
   "weekStart": ""
 }
+

examples/save_sharded_state.py

@@ -20,15 +20,15 @@ llm = LLM(
     tensor_parallel_size=8,
 )
 """
-import argparse
 import dataclasses
 import os
 import shutil
 from pathlib import Path
 
 from vllm import LLM, EngineArgs
+from vllm.utils import FlexibleArgumentParser
 
-parser = argparse.ArgumentParser()
+parser = FlexibleArgumentParser()
 EngineArgs.add_cli_args(parser)
 parser.add_argument("--output",
                     "-o",

examples/tensorize_vllm_model.py

@@ -3,18 +3,13 @@ import dataclasses
 import json
 import os
 import uuid
-from functools import partial
-
-from tensorizer import stream_io
 
 from vllm import LLM
-from vllm.distributed import (init_distributed_environment,
-                              initialize_model_parallel)
 from vllm.engine.arg_utils import EngineArgs
-from vllm.engine.llm_engine import LLMEngine
 from vllm.model_executor.model_loader.tensorizer import (TensorizerArgs,
                                                          TensorizerConfig,
-                                                         serialize_vllm_model)
+                                                         tensorize_vllm_model)
+from vllm.utils import FlexibleArgumentParser
 
 # yapf conflicts with isort for this docstring
 # yapf: disable
@@ -61,6 +56,12 @@ Which downloads the model tensors from your S3 bucket and deserializes them.
 You can also provide a `--keyfile` argument to decrypt the model weights if 
 they were serialized with encryption.
 
+To support distributed tensor-parallel models, each model shard will be
+serialized to a separate file. The tensorizer_uri is then specified as a string
+template with a format specifier such as '%03d' that will be rendered with the
+shard's rank. Sharded models serialized with this script will be named as
+model-rank-%03d.tensors
+
 For more information on the available arguments for serializing, run 
 `python -m examples.tensorize_vllm_model serialize --help`.
 
@@ -96,7 +97,7 @@ deserialization in this example script, although `--tensorizer-uri` and
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(
+    parser = FlexibleArgumentParser(
         description="An example script that can be used to serialize and "
         "deserialize vLLM models. These models "
         "can be loaded using tensorizer directly to the GPU "
@@ -168,77 +169,72 @@ def parse_args():
 def deserialize():
     llm = LLM(model=args.model,
               load_format="tensorizer",
+              tensor_parallel_size=args.tensor_parallel_size,
               model_loader_extra_config=tensorizer_config
     )
     return llm
 
 
+if __name__ == '__main__':
+    args = parse_args()
 
-args = parse_args()
-
-s3_access_key_id = (getattr(args, 's3_access_key_id', None)
-                    or os.environ.get("S3_ACCESS_KEY_ID", None))
-s3_secret_access_key = (getattr(args, 's3_secret_access_key', None)
-                        or os.environ.get("S3_SECRET_ACCESS_KEY", None))
-s3_endpoint = (getattr(args, 's3_endpoint', None)
-               or os.environ.get("S3_ENDPOINT_URL", None))
-
-credentials = {
-    "s3_access_key_id": s3_access_key_id,
-    "s3_secret_access_key": s3_secret_access_key,
-    "s3_endpoint": s3_endpoint
-}
+    s3_access_key_id = (getattr(args, 's3_access_key_id', None)
+                        or os.environ.get("S3_ACCESS_KEY_ID", None))
+    s3_secret_access_key = (getattr(args, 's3_secret_access_key', None)
+                            or os.environ.get("S3_SECRET_ACCESS_KEY", None))
+    s3_endpoint = (getattr(args, 's3_endpoint', None)
+                or os.environ.get("S3_ENDPOINT_URL", None))
 
-_read_stream, _write_stream = (partial(
-    stream_io.open_stream,
-    mode=mode,
-    s3_access_key_id=s3_access_key_id,
-    s3_secret_access_key=s3_secret_access_key,
-    s3_endpoint=s3_endpoint,
-) for mode in ("rb", "wb+"))
+    credentials = {
+        "s3_access_key_id": s3_access_key_id,
+        "s3_secret_access_key": s3_secret_access_key,
+        "s3_endpoint": s3_endpoint
+    }
 
-model_ref = args.model
+    model_ref = args.model
 
-model_name = model_ref.split("/")[1]
+    model_name = model_ref.split("/")[1]
 
-os.environ["MASTER_ADDR"] = "127.0.0.1"
-os.environ["MASTER_PORT"] = "8080"
+    keyfile = args.keyfile if args.keyfile else None
 
-init_distributed_environment(world_size=1, rank=0, local_rank=0)
-initialize_model_parallel()
+    if args.model_loader_extra_config:
+        config = json.loads(args.model_loader_extra_config)
+        tensorizer_args = \
+            TensorizerConfig(**config)._construct_tensorizer_args()
+        tensorizer_args.tensorizer_uri = args.path_to_tensors
+    else:
+        tensorizer_args = None
 
-keyfile = args.keyfile if args.keyfile else None
+    if args.command == "serialize":
+        eng_args_dict = {f.name: getattr(args, f.name) for f in
+                        dataclasses.fields(EngineArgs)}
 
+        engine_args = EngineArgs.from_cli_args(
+            argparse.Namespace(**eng_args_dict)
+        )
 
-if args.model_loader_extra_config:
-    config = json.loads(args.model_loader_extra_config)
-    tensorizer_args = TensorizerConfig(**config)._construct_tensorizer_args()
-    tensorizer_args.tensorizer_uri = args.path_to_tensors
-else:
-    tensorizer_args = None
-
-if args.command == "serialize":
-    eng_args_dict = {f.name: getattr(args, f.name) for f in
-                     dataclasses.fields(EngineArgs)}
-
-    engine_args = EngineArgs.from_cli_args(argparse.Namespace(**eng_args_dict))
-    engine = LLMEngine.from_engine_args(engine_args)
+        input_dir = args.serialized_directory.rstrip('/')
+        suffix = args.suffix if args.suffix else uuid.uuid4().hex
+        base_path = f"{input_dir}/vllm/{model_ref}/{suffix}"
+        if engine_args.tensor_parallel_size > 1:
+            model_path = f"{base_path}/model-rank-%03d.tensors"
+        else:
+            model_path = f"{base_path}/model.tensors"
 
-    input_dir = args.serialized_directory.rstrip('/')
-    suffix = args.suffix if args.suffix else uuid.uuid4().hex
-    base_path = f"{input_dir}/vllm/{model_ref}/{suffix}"
-    model_path = f"{base_path}/model.tensors"
-    tensorizer_config = TensorizerConfig(
-        tensorizer_uri=model_path,
-        **credentials)
-    serialize_vllm_model(engine, tensorizer_config, keyfile)
-elif args.command == "deserialize":
-    if not tensorizer_args:
         tensorizer_config = TensorizerConfig(
-            tensorizer_uri=args.path_to_tensors,
-            encryption_keyfile = keyfile,
-            **credentials
-        )
-    deserialize()
-else:
-    raise ValueError("Either serialize or deserialize must be specified.")
+            tensorizer_uri=model_path,
+            encryption_keyfile=keyfile,
+            **credentials)
+
+        tensorize_vllm_model(engine_args, tensorizer_config)
+
+    elif args.command == "deserialize":
+        if not tensorizer_args:
+            tensorizer_config = TensorizerConfig(
+                tensorizer_uri=args.path_to_tensors,
+                encryption_keyfile = keyfile,
+                **credentials
+            )
+        deserialize()
+    else:
+        raise ValueError("Either serialize or deserialize must be specified.")

format.sh

@@ -36,12 +36,12 @@ tool_version_check() {
     fi
 }
 
-tool_version_check "yapf" $YAPF_VERSION "$(grep yapf requirements-dev.txt | cut -d'=' -f3)"
-tool_version_check "ruff" $RUFF_VERSION "$(grep "ruff==" requirements-dev.txt | cut -d'=' -f3)"
-tool_version_check "mypy" "$MYPY_VERSION" "$(grep mypy requirements-dev.txt | cut -d'=' -f3)"
-tool_version_check "isort" "$ISORT_VERSION" "$(grep isort requirements-dev.txt | cut -d'=' -f3)"
-tool_version_check "codespell" "$CODESPELL_VERSION" "$(grep codespell requirements-dev.txt | cut -d'=' -f3)"
-tool_version_check "clang-format" "$CLANGFORMAT_VERSION" "$(grep clang-format requirements-dev.txt | cut -d'=' -f3)"
+tool_version_check "yapf" $YAPF_VERSION "$(grep yapf requirements-lint.txt | cut -d'=' -f3)"
+tool_version_check "ruff" $RUFF_VERSION "$(grep "ruff==" requirements-lint.txt | cut -d'=' -f3)"
+tool_version_check "mypy" "$MYPY_VERSION" "$(grep mypy requirements-lint.txt | cut -d'=' -f3)"
+tool_version_check "isort" "$ISORT_VERSION" "$(grep isort requirements-lint.txt | cut -d'=' -f3)"
+tool_version_check "codespell" "$CODESPELL_VERSION" "$(grep codespell requirements-lint.txt | cut -d'=' -f3)"
+tool_version_check "clang-format" "$CLANGFORMAT_VERSION" "$(grep clang-format requirements-lint.txt | cut -d'=' -f3)"
 
 YAPF_FLAGS=(
     '--recursive'
@@ -96,22 +96,23 @@ echo 'vLLM yapf: Done'
 
 # Run mypy
 echo 'vLLM mypy:'
+mypy tests --config-file pyproject.toml
+mypy vllm/*.py --config-file pyproject.toml
 mypy vllm/attention --config-file pyproject.toml
 mypy vllm/core --config-file pyproject.toml
 mypy vllm/distributed --config-file pyproject.toml
+mypy vllm/engine  --config-file pyproject.toml
 mypy vllm/entrypoints --config-file pyproject.toml
 mypy vllm/executor --config-file pyproject.toml
+mypy vllm/logging --config-file pyproject.toml
+mypy vllm/lora --config-file pyproject.toml
+mypy vllm/model_executor  --config-file pyproject.toml
 mypy vllm/multimodal --config-file pyproject.toml
-mypy vllm/usage --config-file pyproject.toml
-mypy vllm/*.py --config-file pyproject.toml
+mypy vllm/prompt_adapter --config-file pyproject.toml
+mypy vllm/spec_decode --config-file pyproject.toml
 mypy vllm/transformers_utils --config-file pyproject.toml
-mypy vllm/engine  --config-file pyproject.toml
+mypy vllm/usage --config-file pyproject.toml
 mypy vllm/worker --config-file pyproject.toml
-mypy vllm/spec_decode --config-file pyproject.toml
-mypy vllm/model_executor  --config-file pyproject.toml
-mypy vllm/lora --config-file pyproject.toml
-mypy vllm/logging --config-file pyproject.toml
-mypy vllm/model_executor --config-file pyproject.toml
 
 
 # If git diff returns a file that is in the skip list, the file may be checked anyway:

pyproject.toml

@@ -5,7 +5,7 @@ requires = [
     "ninja",
     "packaging",
     "setuptools >= 49.4.0",
-    "torch == 2.3.0",
+    "torch == 2.3.1",
     "wheel",
 ]
 build-backend = "setuptools.build_meta"
@@ -69,7 +69,5 @@ skip_gitignore = true
 [tool.pytest.ini_options]
 markers = [
     "skip_global_cleanup",
-    "llm: run tests for vLLM API only",
-    "openai: run tests for OpenAI API only",
-    "llava: run tests for LLaVA models only",
+    "vlm: run tests for vision language models only",
 ]

requirements-build.txt

@@ -3,5 +3,5 @@ cmake>=3.21
 ninja
 packaging
 setuptools>=49.4.0
-torch==2.3.0
+torch==2.3.1
 wheel

requirements-common.txt

@@ -2,10 +2,11 @@ cmake >= 3.21
 ninja  # For faster builds.
 psutil
 sentencepiece  # Required for LLaMA tokenizer.
-numpy
+numpy < 2.0.0
 requests
+tqdm
 py-cpuinfo
-transformers >= 4.40.0  # Required for StarCoder2 & Llava, Llama 3.
+transformers >= 4.42.4  # Required for Gemma 2 and for additional chat template parameters.
 tokenizers >= 0.19.1  # Required for Llama 3.
 fastapi
 aiohttp
@@ -16,7 +17,8 @@ pillow  # Required for image processing
 prometheus_client >= 0.18.0
 prometheus-fastapi-instrumentator >= 7.0.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
-lm-format-enforcer == 0.10.1
-outlines >= 0.0.43 # Requires torch >= 2.1.0
+lm-format-enforcer == 0.10.3
+outlines >= 0.0.43, < 0.1 # Requires torch >= 2.1.0
 typing_extensions
 filelock >= 3.10.4 # filelock starts to support `mode` argument from 3.10.4
+pyzmq

requirements-cpu.txt

@@ -2,5 +2,6 @@
 -r requirements-common.txt
 
 # Dependencies for x86_64 CPUs
-torch == 2.3.0+cpu
-triton >= 2.2.0  # FIXME(woosuk): This is a hack to avoid import error.
+torch == 2.3.1+cpu; platform_machine != "ppc64le"
+torchvision == 0.18.1+cpu; platform_machine != "ppc64le"   # required for the image processor of phi3v, this must be updated alongside torch
\ No newline at end of file
+triton >= 2.2.0  # FIXME(woosuk): This is a hack to avoid import error.