add qwen3

vllm-v0.6.2/examples/tensorize_vllm_model.py

@@ -0,0 +1,240 @@
+import argparse
+import dataclasses
+import json
+import os
+import uuid
+
+from vllm import LLM
+from vllm.engine.arg_utils import EngineArgs
+from vllm.model_executor.model_loader.tensorizer import (TensorizerArgs,
+                                                         TensorizerConfig,
+                                                         tensorize_vllm_model)
+from vllm.utils import FlexibleArgumentParser
+
+# yapf conflicts with isort for this docstring
+# yapf: disable
+"""
+tensorize_vllm_model.py is a script that can be used to serialize and 
+deserialize vLLM models. These models can be loaded using tensorizer 
+to the GPU extremely quickly over an HTTP/HTTPS endpoint, an S3 endpoint,
+or locally. Tensor encryption and decryption is also supported, although 
+libsodium must be installed to use it. Install vllm with tensorizer support 
+using `pip install vllm[tensorizer]`. To learn more about tensorizer, visit
+https://github.com/coreweave/tensorizer
+
+To serialize a model, install vLLM from source, then run something 
+like this from the root level of this repository:
+
+python -m examples.tensorize_vllm_model \
+   --model facebook/opt-125m \
+   serialize \
+   --serialized-directory s3://my-bucket \
+   --suffix v1
+   
+Which downloads the model from HuggingFace, loads it into vLLM, serializes it,
+and saves it to your S3 bucket. A local directory can also be used. This
+assumes your S3 credentials are specified as environment variables
+in the form of `S3_ACCESS_KEY_ID`, `S3_SECRET_ACCESS_KEY`, and 
+`S3_ENDPOINT_URL`. To provide S3 credentials directly, you can provide 
+`--s3-access-key-id` and `--s3-secret-access-key`, as well as `--s3-endpoint` 
+as CLI args to this script.
+
+You can also encrypt the model weights with a randomly-generated key by 
+providing a `--keyfile` argument.
+
+To deserialize a model, you can run something like this from the root 
+level of this repository:
+
+python -m examples.tensorize_vllm_model \
+   --model EleutherAI/gpt-j-6B \
+   --dtype float16 \
+   deserialize \
+   --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors
+
+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`.
+
+Or for deserializing:
+
+`python -m examples.tensorize_vllm_model deserialize --help`.
+
+Once a model is serialized, tensorizer can be invoked with the `LLM` class 
+directly to load models:
+
+    llm = LLM(model="facebook/opt-125m",
+              load_format="tensorizer",
+              model_loader_extra_config=TensorizerConfig(
+                    tensorizer_uri = path_to_tensors,
+                    num_readers=3,
+                    )
+              )
+            
+A serialized model can be used during model loading for the vLLM OpenAI
+inference server. `model_loader_extra_config` is exposed as the CLI arg
+`--model-loader-extra-config`, and accepts a JSON string literal of the
+TensorizerConfig arguments desired.
+
+In order to see all of the available arguments usable to configure 
+loading with tensorizer that are given to `TensorizerConfig`, run:
+
+`python -m examples.tensorize_vllm_model deserialize --help`
+
+under the `tensorizer options` section. These can also be used for
+deserialization in this example script, although `--tensorizer-uri` and
+`--path-to-tensors` are functionally the same in this case.
+"""
+
+
+def parse_args():
+    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 "
+        "extremely quickly. Tensor encryption and decryption is "
+        "also supported, although libsodium must be installed to "
+        "use it.")
+    parser = EngineArgs.add_cli_args(parser)
+    subparsers = parser.add_subparsers(dest='command')
+
+    serialize_parser = subparsers.add_parser(
+        'serialize', help="Serialize a model to `--serialized-directory`")
+
+    serialize_parser.add_argument(
+        "--suffix",
+        type=str,
+        required=False,
+        help=(
+            "The suffix to append to the serialized model directory, which is "
+            "used to construct the location of the serialized model tensors, "
+            "e.g. if `--serialized-directory` is `s3://my-bucket/` and "
+            "`--suffix` is `v1`, the serialized model tensors will be "
+            "saved to "
+            "`s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors`. "
+            "If none is provided, a random UUID will be used."))
+    serialize_parser.add_argument(
+        "--serialized-directory",
+        type=str,
+        required=True,
+        help="The directory to serialize the model to. "
+        "This can be a local directory or S3 URI. The path to where the "
+        "tensors are saved is a combination of the supplied `dir` and model "
+        "reference ID. For instance, if `dir` is the serialized directory, "
+        "and the model HuggingFace ID is `EleutherAI/gpt-j-6B`, tensors will "
+        "be saved to `dir/vllm/EleutherAI/gpt-j-6B/suffix/model.tensors`, "
+        "where `suffix` is given by `--suffix` or a random UUID if not "
+        "provided.")
+
+    serialize_parser.add_argument(
+        "--keyfile",
+        type=str,
+        required=False,
+        help=("Encrypt the model weights with a randomly-generated binary key,"
+              " and save the key at this path"))
+
+    deserialize_parser = subparsers.add_parser(
+        'deserialize',
+        help=("Deserialize a model from `--path-to-tensors`"
+              " to verify it can be loaded and used."))
+
+    deserialize_parser.add_argument(
+        "--path-to-tensors",
+        type=str,
+        required=True,
+        help="The local path or S3 URI to the model tensors to deserialize. ")
+
+    deserialize_parser.add_argument(
+        "--keyfile",
+        type=str,
+        required=False,
+        help=("Path to a binary key to use to decrypt the model weights,"
+              " if the model was serialized with encryption"))
+
+    TensorizerArgs.add_cli_args(deserialize_parser)
+
+    return parser.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()
+
+    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
+    }
+
+    model_ref = args.model
+
+    model_name = model_ref.split("/")[1]
+
+    keyfile = args.keyfile if args.keyfile else None
+
+    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)
+        )
+
+        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"
+
+        tensorizer_config = TensorizerConfig(
+            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.")